docs(skill): plain wording for the unclear-chain case

fix(skill): walk every introducing chain before not_reached
For a transitive package the caller of the affected API is the package one hop above it on each chain (body-parser for express > body-parser > qs), not the direct dependency. Inspect that package on every chain; any chain left unclear keeps the finding at imported.
2026-11-09 05:19:39 -10:00 · 2026-11-08 19:36:51 +00:00 · 2026-11-08 19:27:15 +00:00 · 2026-11-06 12:20:53 +00:00 · 2026-11-04 21:57:34 +02:00 · 2026-11-04 10:26:08 -05:00
59 changed files with 1112 additions and 215 deletions
@@ -28,7 +28,7 @@ Target-specific workflows built on the same engine:
 - **Open-source CLI (self-hosted):** free, fully local, BYO LLM key, needs Docker. Best for local dev loops, air-gapped/offline, and full control.
  ```bash
  curl -sSL https://strix.ai/install | bash        # install
-  export STRIX_LLM="openrouter/z-ai/glm-5.3"        # any LiteLLM model id
+  export STRIX_LLM="openrouter/z-ai/glm-5.3"        # any LiteLLM model id, or vercel_ai_gateway/zai/glm-5.3
  export LLM_API_KEY="<key>"
  strix -n -t ./ --scan-mode quick --max-budget 10  # headless scan; always use -n
  ```
@@ -31,7 +31,7 @@ Thank you for your interest in contributing to Strix! This guide will help you g

 3. **Configure your LLM provider**
   ```bash
-   export STRIX_LLM="openrouter/z-ai/glm-5.3"
+   export STRIX_LLM="openrouter/z-ai/glm-5.3"   # or vercel_ai_gateway/zai/glm-5.3
   export LLM_API_KEY="your-api-key"
   ```

@@ -84,7 +84,8 @@ Strix are autonomous AI penetration testing agents that act just like real hacke
 curl -sSL https://strix.ai/install | bash

 # Configure your AI provider
-export STRIX_LLM="openrouter/z-ai/glm-5.3"
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"   # Vercel AI Gateway
+# export STRIX_LLM="openrouter/z-ai/glm-5.3"       # OpenRouter
 export LLM_API_KEY="your-api-key"

 # Run your first security assessment
@@ -296,7 +297,8 @@ jobs:
 ### Configuration

 ```bash
-export STRIX_LLM="openrouter/z-ai/glm-5.3"
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"   # Vercel AI Gateway
+# export STRIX_LLM="openrouter/z-ai/glm-5.3"       # OpenRouter
 export LLM_API_KEY="your-api-key"

 # Optional
@@ -352,12 +354,14 @@ Each server's tools are namespaced by `name`, for example `github_list_issues`.

 **Recommended models for best results:**

- [Z.ai GLM-5.3 on OpenRouter](https://openrouter.ai/z-ai/glm-5.3) - `openrouter/z-ai/glm-5.3` (the default pick)
- [OpenAI GPT-5.4](https://openai.com/api/) - `openai/gpt-5.4`
- [Anthropic Claude Sonnet 4.6](https://claude.com/platform/api) - `anthropic/claude-sonnet-4-6`
- [Google Gemini 3 Pro Preview](https://cloud.google.com/vertex-ai) - `vertex_ai/gemini-3-pro-preview`
+- [Z.ai GLM-5.3 on Vercel AI Gateway](https://vercel.com/ai-gateway/models/glm-5.3) - `vercel_ai_gateway/zai/glm-5.3` (recommended)
+- [Z.ai GLM-5.3 on OpenRouter](https://openrouter.ai/z-ai/glm-5.3) - `openrouter/z-ai/glm-5.3` (recommended)
+- [DeepSeek V4.1 Flash on OpenRouter](https://openrouter.ai/deepseek/deepseek-v4.1-flash-1017) - `openrouter/deepseek/deepseek-v4.1-flash-1017`
+- [OpenAI GPT-6.1 Sol](https://openai.com/api/) - `openai/gpt-6.1-sol` (needs [Daybreak](https://openai.com/daybreak/) access)
+- [Anthropic Claude Opus 5.5](https://claude.com/platform/api) - `anthropic/claude-opus-5-5` (needs [CVP](https://support.claude.com/en/articles/14604842-cyber-verification-program) access)
+- [Google Gemini 4 Argon](https://cloud.google.com/vertex-ai) - `vertex_ai/gemini-4-argon` (needs [Fairwind](https://deepmind.google/fairwind-program/) access)
 - [DeepSeek V4 Pro](https://platform.deepseek.com) - `deepseek/deepseek-v4-pro`
- [Moonshot Kimi K3](https://platform.kimi.ai) - `moonshot/kimi-k3`
+- [Moonshot Kimi K3.1 Pro](https://platform.kimi.ai) - `moonshot/kimi-k3.1-pro`

 See the [LLM Providers documentation](https://docs.strix.ai/llm-providers/overview) for all supported providers including Vertex AI, Bedrock, Azure, and local models.

@@ -186,20 +186,84 @@ RUN set -eux; \
        aarch64|arm64) GITLEAKS_ARCH="arm64" ;; \
        *) echo "Unsupported architecture: $ARCH" >&2; exit 1 ;; \
    esac; \
-    TAG="$(curl -fsSL https://api.github.com/repos/gitleaks/gitleaks/releases/latest | jq -r .tag_name)"; \
+    TAG="$(curl -fsSLI -o /dev/null -w '%{url_effective}' https://github.com/gitleaks/gitleaks/releases/latest | sed 's#.*/##')"; \
    curl -fsSL "https://github.com/gitleaks/gitleaks/releases/download/${TAG}/gitleaks_${TAG#v}_linux_${GITLEAKS_ARCH}.tar.gz" -o /tmp/gitleaks.tgz; \
    tar -xzf /tmp/gitleaks.tgz -C /tmp; \
    install -m 0755 /tmp/gitleaks /usr/local/bin/gitleaks; \
    rm -f /tmp/gitleaks /tmp/gitleaks.tgz

-RUN curl -sfL https://raw.githubusercontent.com/aquasecurity/trivy/main/contrib/install.sh | sh -s -- -b /usr/local/bin
+ARG TRIVY_VERSION=0.76.1
+ARG TRIVY_SHA256_X86=c7cbe3137fb8a48a17574bede3dd6beb16742d1e9772929940fc90082c39d9c0
+ARG TRIVY_SHA256_AARCH64=e174dff2a6dba5e3ae41f8d0537a6324405bd08132497340304c6b20e0f16a1f
+RUN set -eux; \
+    case "$(uname -m)" in \
+        x86_64) TRIVY_ARCH="64bit"; TRIVY_SHA256="${TRIVY_SHA256_X86}" ;; \
+        aarch64|arm64) TRIVY_ARCH="ARM64"; TRIVY_SHA256="${TRIVY_SHA256_AARCH64}" ;; \
+        *) echo "Unsupported architecture: $(uname -m)" >&2; exit 1 ;; \
+    esac; \
+    curl -fsSL "https://github.com/aquasecurity/trivy/releases/download/v${TRIVY_VERSION}/trivy_${TRIVY_VERSION}_Linux-${TRIVY_ARCH}.tar.gz" -o /tmp/trivy.tgz; \
+    echo "${TRIVY_SHA256}  /tmp/trivy.tgz" | sha256sum -c -; \
+    tar -xzf /tmp/trivy.tgz -C /tmp trivy; \
+    install -m 0755 /tmp/trivy /usr/local/bin/trivy; \
+    rm -f /tmp/trivy /tmp/trivy.tgz; \
+    trivy --version

 RUN apt-get install -y wapiti

+# CVE-2026-111497: out-of-bounds write in the brotli decoder, fixed in brotli
+# 1.2.1 / Debian 1.2.0-4.1. kali-rolling still ships 1.2.0-4, and node, curl and
+# python3 in this image all load the shared libbrotlidec, so every HTTP client here
+# would decode a malicious `Content-Encoding: br` response with the vulnerable
+# code. Install the fixed Debian packages until Kali picks them up.
+ARG BROTLI_DEB_VERSION=1.2.0-4.1
+RUN set -eux; \
+    ARCH="$(dpkg --print-architecture)"; \
+    for pkg in libbrotli1 python3-brotli; do \
+        curl -fsSL "https://deb.debian.org/debian/pool/main/b/brotli/${pkg}_${BROTLI_DEB_VERSION}_${ARCH}.deb" -o "/tmp/${pkg}.deb"; \
+    done; \
+    dpkg -i /tmp/libbrotli1.deb /tmp/python3-brotli.deb; \
+    rm -f /tmp/libbrotli1.deb /tmp/python3-brotli.deb; \
+    test "$(dpkg-query -W -f='${Version}' libbrotli1)" = "${BROTLI_DEB_VERSION}"; \
+    node -e 'const z=require("zlib");z.brotliDecompressSync(z.brotliCompressSync(Buffer.from("ok")))'
+
+ARG OPENGREP_VERSION=1.31.0
+ARG OPENGREP_SHA256_X86=dbbe3c2058dbe600c449a8ba22ea79a1643a6660658400090d0323a8d0d31147
+ARG OPENGREP_SHA256_AARCH64=2b713a29a00237f85408b83002fc111db5b94c7fa8d263f0a93184a960655d9d
+RUN set -eux; \
+    case "$(uname -m)" in \
+        x86_64) OPENGREP_ARCH="x86"; OPENGREP_SHA256="${OPENGREP_SHA256_X86}" ;; \
+        aarch64|arm64) OPENGREP_ARCH="aarch64"; OPENGREP_SHA256="${OPENGREP_SHA256_AARCH64}" ;; \
+        *) echo "Unsupported architecture: $(uname -m)" >&2; exit 1 ;; \
+    esac; \
+    curl -fsSL "https://github.com/opengrep/opengrep/releases/download/v${OPENGREP_VERSION}/opengrep_manylinux_${OPENGREP_ARCH}" -o /tmp/opengrep; \
+    echo "${OPENGREP_SHA256}  /tmp/opengrep" | sha256sum -c -; \
+    install -m 0755 /tmp/opengrep /usr/local/bin/opengrep; \
+    rm -f /tmp/opengrep; \
+    opengrep --version
+
+# Opengrep ships no rule registry of its own; bake the community ruleset so
+# scans run offline. Keep only rule YAML and LICENSE; drop tests, fixtures,
+# tooling, and rules marked with a proprietary license.
+ARG OPENGREP_RULES_REF=f1d2b562b414783763fd02a6ed2736eaed622efa
+RUN set -eux; \
+    mkdir -p /opt/opengrep-rules; \
+    curl -fsSL "https://codeload.github.com/opengrep/opengrep-rules/tar.gz/${OPENGREP_RULES_REF}" \
+        | tar -xz --strip-components=1 -C /opt/opengrep-rules; \
+    cd /opt/opengrep-rules; \
+    rm -rf .github scripts stats; \
+    find . -maxdepth 1 -type f ! -name LICENSE -delete; \
+    find . -mindepth 2 -type f \( ! \( -name '*.yaml' -o -name '*.yml' \) -o -name '*.test.yaml' -o -name '*.test.yml' -o -name '.*' \) -delete; \
+    grep -rlZ 'license: proprietary' . | xargs -0 -r rm -f; \
+    find . -type d -empty -delete; \
+    chmod -R a+rX /opt/opengrep-rules
+
+# Strix-authored rules that close gaps in the community ruleset.
+COPY containers/opengrep-rules/ /opt/opengrep-rules/strix/
+RUN chmod -R a+rX /opt/opengrep-rules/strix
+
 USER pentester

-RUN pipx install semgrep && \
-    pipx install bandit
+RUN pipx install bandit

 RUN npm install -g jshint

@@ -0,0 +1,37 @@
+rules:
+  - id: github-owned-action-not-pinned-to-commit-sha
+    patterns:
+      - pattern-inside: "{steps: ...}"
+      - pattern: |
+          uses: "$USES"
+      - metavariable-regex:
+          metavariable: $USES
+          regex: '^(actions|github)/'
+      - metavariable-pattern:
+          metavariable: $USES
+          language: generic
+          patterns:
+            - pattern-not-regex: "@[0-9a-f]{40}$"
+    message: >-
+      GitHub-owned action `$USES` is referenced by a mutable tag or branch
+      instead of a full-length commit SHA. Tags can be moved, so a compromised
+      or force-pushed release changes what this workflow runs. Pin to the
+      commit SHA of the release you reviewed and let a bot keep it current.
+    languages:
+      - yaml
+    severity: WARNING
+    metadata:
+      cwe:
+        - "CWE-1357: Reliance on Insufficiently Trustworthy Component"
+        - "CWE-353: Missing Support for Integrity Check"
+      owasp: A06:2021 - Vulnerable and Outdated Components
+      references:
+        - https://docs.github.com/en/actions/security-guides/security-hardening-for-github-actions#using-third-party-actions
+      category: security
+      technology:
+        - github-actions
+      subcategory:
+        - vuln
+      likelihood: LOW
+      impact: LOW
+      confidence: HIGH
@@ -8,7 +8,7 @@ Configure Strix using environment variables or a config file.
 ## LLM Configuration

 <ParamField path="STRIX_LLM" type="string" required>
-  Model name in LiteLLM format (e.g., `openrouter/z-ai/glm-5.3`, `openai/gpt-5.4`).
+  Model name in LiteLLM format (e.g., `openrouter/z-ai/glm-5.3`, `openai/gpt-6.1-sol`).
 </ParamField>

 <ParamField path="LLM_API_KEY" type="string">
@@ -19,10 +19,29 @@ Configure Strix using environment variables or a config file.
  Custom API base URL. Also accepts `OPENAI_API_BASE`, `LITELLM_BASE_URL`, or `OLLAMA_API_BASE`.
 </ParamField>

+<ParamField path="STRIX_LLM_INPUT_COST_PER_MTOK" type="number">
+  Price per 1M input tokens in USD for `STRIX_LLM`. Use it for gateway or self-hosted model ids that LiteLLM can't price.
+</ParamField>
+
+<ParamField path="STRIX_LLM_OUTPUT_COST_PER_MTOK" type="number">
+  Price per 1M output tokens in USD for `STRIX_LLM`.
+</ParamField>
+
+<ParamField path="STRIX_LLM_CACHED_INPUT_COST_PER_MTOK" type="number">
+  Price per 1M cached input tokens in USD for `STRIX_LLM`. If unset, cached tokens use the input price.
+</ParamField>
+
+<Note>
+  Setting any of these prices replaces the provider-reported and LiteLLM cost for `STRIX_LLM` in reports and `--max-budget`. An unset input or output price counts as `0`, so set both. A separate `STRIX_DEDUPE_MODEL` keeps its LiteLLM price.
+</Note>
+
 <ParamField path="STRIX_API_TYPE" type="string">
  Select the OpenAI API path for the model: `responses` or `chat_completions`.
-  By default, a custom `LLM_API_BASE` uses chat completions. Set this variable
-  when your gateway requires the other API. Also accepts `STRIX_FORCE_API`.
+  By default the model decides: Responses when LiteLLM's model catalog lists
+  `/v1/responses` for it (e.g. `gpt-5`), chat completions otherwise. A custom
+  `LLM_API_BASE` does not change this, so set `STRIX_API_TYPE=chat_completions`
+  when your gateway only serves chat completions. Applies to bare and `openai/`
+  model names. Also accepts `STRIX_FORCE_API`.
 </ParamField>

 <ParamField path="LLM_EXTRA_HEADERS" type="string">
@@ -167,7 +186,7 @@ When remote vars are set, Strix dual-writes telemetry to both local JSONL and th

 ## Docker Configuration

-<ParamField path="STRIX_IMAGE" default="ghcr.io/usestrix/strix-sandbox:1.3.0" type="string">
+<ParamField path="STRIX_IMAGE" default="ghcr.io/usestrix/strix-sandbox:1.4.0" type="string">
  Docker image to use for the sandbox container.
 </ParamField>

@@ -35,8 +35,8 @@
              "llm-providers/overview",
              "llm-providers/openai",
              "llm-providers/anthropic",
-              "llm-providers/openrouter",
              "llm-providers/vercel-ai-gateway",
+              "llm-providers/openrouter",
              "llm-providers/vertex",
              "llm-providers/bedrock",
              "llm-providers/azure",
@@ -73,7 +73,20 @@ Strix uses a graph of specialized agents for comprehensive security testing:

 ## Quick Example

-```bash
+<CodeGroup>
+```bash Vercel AI Gateway
+# Install
+curl -sSL https://strix.ai/install | bash
+
+# Configure
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"
+export LLM_API_KEY="your-api-key"
+
+# Scan
+strix --target ./your-app
+```
+
+```bash OpenRouter
 # Install
 curl -sSL https://strix.ai/install | bash

@@ -84,6 +97,7 @@ export LLM_API_KEY="your-api-key"
 # Scan
 strix --target ./your-app
 ```
+</CodeGroup>

 ## Community

@@ -6,7 +6,7 @@ description: "Configure Strix with Claude models"
 ## Setup

 ```bash
-export STRIX_LLM="anthropic/claude-sonnet-4-6"
+export STRIX_LLM="anthropic/claude-sonnet-5-5"
 export LLM_API_KEY="sk-ant-..."
 ```

@@ -14,8 +14,12 @@ export LLM_API_KEY="sk-ant-..."

 | Model | Description |
 |-------|-------------|
-| `anthropic/claude-sonnet-4-6` | Best balance of intelligence and speed |
-| `anthropic/claude-opus-4-6` | Maximum capability for deep analysis |
+| `anthropic/claude-sonnet-5-5` | Best balance of intelligence and speed |
+| `anthropic/claude-opus-5-5` | Maximum capability for deep analysis |
+
+<Note>
+Security testing with Claude needs the [Cyber Verification Program](https://support.claude.com/en/articles/14604842-cyber-verification-program) (CVP). Without it the safety classifiers block most offensive tasks. Red Team Access covers authorized penetration testing.
+</Note>

 ## Get API Key

@@ -6,7 +6,7 @@ description: "Configure Strix with OpenAI models via Azure"
 ## Setup

 ```bash
-export STRIX_LLM="azure/your-gpt5-deployment"
+export STRIX_LLM="azure/your-gpt6-deployment"
 export AZURE_API_KEY="your-azure-api-key"
 export AZURE_API_BASE="https://your-resource.openai.azure.com"
 export AZURE_API_VERSION="2025-11-01-preview"
@@ -24,7 +24,7 @@ export AZURE_API_VERSION="2025-11-01-preview"
 ## Example

 ```bash
-export STRIX_LLM="azure/gpt-5.4-deployment"
+export STRIX_LLM="azure/gpt-6.1-sol-deployment"
 export AZURE_API_KEY="abc123..."
 export AZURE_API_BASE="https://mycompany.openai.azure.com"
 export AZURE_API_VERSION="2025-11-01-preview"
@@ -33,5 +33,9 @@ export AZURE_API_VERSION="2025-11-01-preview"
 ## Prerequisites

 1. Create an Azure OpenAI resource
-2. Deploy a model (e.g., GPT-5.4)
+2. Deploy a model such as GPT-6.1 Sol
 3. Get the endpoint URL and API key from the Azure portal
+
+<Note>
+Security testing with OpenAI models needs [Daybreak](https://openai.com/daybreak/) access, also on Azure.
+</Note>
@@ -6,7 +6,7 @@ description: "Configure Strix with models via AWS Bedrock"
 ## Setup

 ```bash
-export STRIX_LLM="bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0"
+export STRIX_LLM="bedrock/us.anthropic.claude-sonnet-5-5"
 ```

 No API key required—uses AWS credentials from environment.
@@ -36,10 +36,13 @@ Automatically uses instance role credentials.

 | Model | Description |
 |-------|-------------|
-| `bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0` | Claude 4.5 Sonnet |
-| `bedrock/anthropic.claude-4-5-opus-20251022-v1:0` | Claude 4.5 Opus |
-| `bedrock/anthropic.claude-4-5-haiku-20251022-v1:0` | Claude 4.5 Haiku |
-| `bedrock/amazon.titan-text-premier-v2:0` | Amazon Titan Premier v2 |
+| `bedrock/us.anthropic.claude-sonnet-5-5` | Claude Sonnet 5.5 |
+| `bedrock/us.anthropic.claude-opus-5-5` | Claude Opus 5.5 |
+| `bedrock/us.anthropic.claude-haiku-5-5` | Claude Haiku 5.5 |
+
+<Note>
+Security testing with Claude needs the [Cyber Verification Program](https://support.claude.com/en/articles/14604842-cyber-verification-program) (CVP), also on Bedrock.
+</Note>

 ## Prerequisites

@@ -8,7 +8,7 @@ description: "Configure Strix with Novita AI models"
 ## Setup

 ```bash
-export STRIX_LLM="openai/moonshotai/kimi-k2.5"
+export STRIX_LLM="openai/zai-org/glm-5.3"
 export LLM_API_KEY="your-novita-api-key"
 export LLM_API_BASE="https://api.novita.ai/openai"
 ```
@@ -18,11 +18,12 @@ export LLM_API_BASE="https://api.novita.ai/openai"
 | Model | Configuration |
 |-------|---------------|
 | GLM-5.3 | `openai/zai-org/glm-5.3` |
+| Kimi K3.1 Pro | `openai/moonshotai/kimi-k3.1-pro` |
 | Kimi K3 | `openai/moonshotai/kimi-k3` |
+| DeepSeek V4.1 Flash | `openai/deepseek/deepseek-v4.1-flash-1017` |
 | DeepSeek V4 Pro | `openai/deepseek/deepseek-v4-pro` |
-| Kimi K2.5 | `openai/moonshotai/kimi-k2.5` |
-| GLM-5 | `openai/zai-org/glm-5` |
-| MiniMax M2.5 | `openai/minimax/minimax-m2.5` |
+| MiniMax M3.1 Flash | `openai/minimax/minimax-m3.1-flash` |
+| GLM-5.3 Flash | `openai/zai-org/glm-5.3-flash` |

 ## Get API Key

@@ -6,13 +6,23 @@ description: "Configure Strix with OpenAI models"
 ## Setup

 ```bash
-export STRIX_LLM="openai/gpt-5.4"
+export STRIX_LLM="openai/gpt-6.1-sol"
 export LLM_API_KEY="sk-..."
 ```

 ## Available Models

-See [OpenAI Models Documentation](https://platform.openai.com/docs/models) for the full list of available models.
+| Model | Description |
+|-------|-------------|
+| `openai/gpt-6.1-sol` | Best balance of capability and cost for agentic security work |
+| `openai/gpt-6-astra` | Flagship, maximum capability |
+| `openai/gpt-6-luna` | Fast and cheap |
+
+See [OpenAI Models Documentation](https://platform.openai.com/docs/models) for the full list.
+
+<Note>
+Security testing with OpenAI models needs [Daybreak](https://openai.com/daybreak/) (Trusted Access for Cyber). Without it the models refuse most offensive tasks. [Request access](https://openai.com/form/enterprise-trusted-access-for-cyber/).
+</Note>

 ## Get API Key

@@ -25,7 +35,7 @@ See [OpenAI Models Documentation](https://platform.openai.com/docs/models) for t
 For OpenAI-compatible APIs:

 ```bash
-export STRIX_LLM="openai/gpt-5.4"
+export STRIX_LLM="openai/gpt-6.1-sol"
 export LLM_API_KEY="your-key"
 export LLM_API_BASE="https://your-proxy.com/v1"
 ```
@@ -18,13 +18,19 @@ Access any model on OpenRouter using the format `openrouter/<provider>/<model>`:

 | Model | Configuration |
 |-------|---------------|
-| GLM-5.3 (default) | `openrouter/z-ai/glm-5.3` |
-| GPT-5.4 | `openrouter/openai/gpt-5.4` |
-| Claude Sonnet 4.6 | `openrouter/anthropic/claude-sonnet-4.6` |
-| Gemini 3 Pro | `openrouter/google/gemini-3-pro-preview` |
+| GLM-5.3 (recommended) | `openrouter/z-ai/glm-5.3` |
+| GPT-6.1 Sol | `openrouter/openai/gpt-6.1-sol` |
+| GPT-6 Astra | `openrouter/openai/gpt-6-astra` |
+| Claude Opus 5.5 | `openrouter/anthropic/claude-opus-5.5` |
+| Claude Sonnet 5.5 | `openrouter/anthropic/claude-sonnet-5.5` |
+| Gemini 4 Argon | `openrouter/google/gemini-4-argon` |
+| Gemini 3.8 Flash | `openrouter/google/gemini-3.8-flash` |
+| DeepSeek V4.1 Flash | `openrouter/deepseek/deepseek-v4.1-flash-1017` |
 | DeepSeek V4 Pro | `openrouter/deepseek/deepseek-v4-pro` |
-| Kimi K3 | `openrouter/moonshotai/kimi-k3` |
-| GLM-4.7 | `openrouter/z-ai/glm-4.7` |
+| Kimi K3.1 Pro | `openrouter/moonshotai/kimi-k3.1-pro` |
+| GLM-5.3 Flash | `openrouter/z-ai/glm-5.3-flash` |
+
+OpenAI, Anthropic and Gemini models keep their provider safeguards on OpenRouter. Security testing with them needs [Daybreak](https://openai.com/daybreak/), [CVP](https://support.claude.com/en/articles/14604842-cyber-verification-program) or [Fairwind](https://deepmind.google/fairwind-program/) access.

 ## Get API Key

@@ -9,19 +9,33 @@ Strix uses [LiteLLM](https://docs.litellm.ai/docs/providers) for model compatibi

 Set your model and API key:

-| Model                | Provider          | Configuration                    |
-| -------------------- | ----------------- | -------------------------------- |
-| GLM-5.3 (default)    | Z.ai (OpenRouter) | `openrouter/z-ai/glm-5.3`        |
-| GPT-5.4              | OpenAI            | `openai/gpt-5.4`                 |
-| Claude Sonnet 4.6    | Anthropic         | `anthropic/claude-sonnet-4-6`    |
-| Gemini 3 Pro         | Google Vertex     | `vertex_ai/gemini-3-pro-preview` |
-| DeepSeek V4 Pro      | DeepSeek          | `deepseek/deepseek-v4-pro`       |
-| Kimi K3              | Moonshot          | `moonshot/kimi-k3`               |
+| Model                 | Provider                  | Configuration                    |
+| --------------------- | ------------------------- | -------------------------------- |
+| GLM-5.3 (recommended) | Vercel AI Gateway         | `vercel_ai_gateway/zai/glm-5.3`  |
+| GLM-5.3 (recommended) | OpenRouter                | `openrouter/z-ai/glm-5.3`        |
+| GPT-6.1 Sol           | OpenAI                    | `openai/gpt-6.1-sol`             |
+| Claude Opus 5.5       | Anthropic                 | `anthropic/claude-opus-5-5`      |
+| Claude Sonnet 5.5     | Anthropic                 | `anthropic/claude-sonnet-5-5`    |
+| Gemini 4 Argon        | Google Vertex             | `vertex_ai/gemini-4-argon`       |
+| DeepSeek V4.1 Flash   | DeepSeek                  | `deepseek/deepseek-flash`        |
+| DeepSeek V4 Pro       | DeepSeek                  | `deepseek/deepseek-v4-pro`       |
+| Kimi K3.1 Pro         | Moonshot                  | `moonshot/kimi-k3.1-pro`         |

-```bash
+<Note>
+Frontier OpenAI, Anthropic and Gemini models need a cyber access program for security testing: [Daybreak](https://openai.com/daybreak/) for OpenAI, the [Cyber Verification Program](https://support.claude.com/en/articles/14604842-cyber-verification-program) for Anthropic, and the [Fairwind Program](https://deepmind.google/fairwind-program/) for Gemini.
+</Note>
+
+<CodeGroup>
+```bash Vercel AI Gateway
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"
+export LLM_API_KEY="your-api-key"
+```
+
+```bash OpenRouter
 export STRIX_LLM="openrouter/z-ai/glm-5.3"
 export LLM_API_KEY="your-api-key"
 ```
+</CodeGroup>

 ## Local Models

@@ -47,7 +61,7 @@ See the [Local Models guide](/llm-providers/local) for setup instructions and re
    Access 100+ models through a single API.
  </Card>
  <Card title="Vercel AI Gateway" href="/llm-providers/vercel-ai-gateway">
-    Access models from multiple providers through one endpoint.
+    Access 100+ models through one OpenAI-compatible endpoint.
  </Card>
  <Card title="Google Vertex AI" href="/llm-providers/vertex">
    Gemini 3 models via Google Cloud.
@@ -69,9 +83,9 @@ Use LiteLLM's `provider/model-name` format:

 ```
 openrouter/z-ai/glm-5.3
-openai/gpt-5.4
-anthropic/claude-sonnet-4-6
-vertex_ai/gemini-3-pro-preview
-bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0
+openai/gpt-6.1-sol
+anthropic/claude-sonnet-5-5
+vertex_ai/gemini-4-argon
+bedrock/us.anthropic.claude-sonnet-5-5
 ollama/llama4
 ```
@@ -1,32 +1,47 @@
 ---
 title: "Vercel AI Gateway"
-description: "Configure Strix with models via Vercel AI Gateway"
+description: "Configure Strix with models through Vercel AI Gateway"
 ---

-[Vercel AI Gateway](https://vercel.com/docs/ai-gateway) provides an OpenAI-compatible API for models from multiple providers.
+[Vercel AI Gateway](https://vercel.com/docs/ai-gateway) provides access to 100+ models from multiple providers through one OpenAI-compatible API.

 ## Setup

 Create an [AI Gateway API key](https://vercel.com/docs/ai-gateway/authentication-and-byok), then configure Strix:

 ```bash
-export STRIX_LLM="openai/anthropic/claude-opus-5"
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"
 export LLM_API_KEY="your-ai-gateway-api-key"
-export LLM_API_BASE="https://ai-gateway.vercel.sh/v1"
 ```

-The first `openai/` segment tells Strix to use its OpenAI-compatible client. The remaining value is the [AI Gateway model ID](https://vercel.com/docs/ai-gateway/models-and-providers).
+The `vercel_ai_gateway/` prefix selects the Gateway. The remaining value is the [AI Gateway model ID](https://vercel.com/docs/ai-gateway/models-and-providers).

 ## Available Models

-Use any language model returned by the AI Gateway models endpoint:
+Access any model on the Gateway using the format `vercel_ai_gateway/<provider>/<model>`:
+
+| Model | Configuration |
+|-------|---------------|
+| GLM-5.3 (recommended) | `vercel_ai_gateway/zai/glm-5.3` |
+| GPT-6.1 Sol | `vercel_ai_gateway/openai/gpt-6.1-sol` |
+| GPT-6 Astra | `vercel_ai_gateway/openai/gpt-6-astra` |
+| Claude Opus 5.5 | `vercel_ai_gateway/anthropic/claude-opus-5.5` |
+| Claude Sonnet 5.5 | `vercel_ai_gateway/anthropic/claude-sonnet-5.5` |
+| Gemini 4 Argon | `vercel_ai_gateway/google/gemini-4-argon` |
+| Gemini 3.8 Flash | `vercel_ai_gateway/google/gemini-3.8-flash` |
+| DeepSeek V4.1 Flash | `vercel_ai_gateway/deepseek/deepseek-v4.1-flash-1017` |
+| DeepSeek V4 Pro | `vercel_ai_gateway/deepseek/deepseek-v4-pro` |
+| Kimi K3.1 Pro | `vercel_ai_gateway/moonshotai/kimi-k3.1-pro` |
+| GLM-5.3 Flash | `vercel_ai_gateway/zai/glm-5.3-flash` |
+
+OpenAI, Anthropic and Gemini models keep their provider safeguards on the Gateway. Security testing with them needs [Daybreak](https://openai.com/daybreak/), [CVP](https://support.claude.com/en/articles/14604842-cyber-verification-program) or [Fairwind](https://deepmind.google/fairwind-program/) access.
+
+The full list is at the [AI Gateway models page](https://vercel.com/ai-gateway/models) or the models endpoint:

 ```text
 https://ai-gateway.vercel.sh/v1/models
 ```

-Prefix its model ID with `openai/` when setting `STRIX_LLM`. For example, the Gateway model ID `anthropic/claude-opus-5` becomes `openai/anthropic/claude-opus-5` in Strix.
-
 ## Get API Key

 1. Open the [AI Gateway API key settings](https://vercel.com/docs/ai-gateway/authentication-and-byok)
@@ -35,5 +50,6 @@ Prefix its model ID with `openai/` when setting `STRIX_LLM`. For example, the Ga

 ## Benefits

- Access models from multiple providers through one endpoint
- Track Gateway usage and cost in Vercel
+- **Single API** — Access models from OpenAI, Anthropic, Google, Z.ai, and more
+- **Bring your own keys** — Route through your own provider accounts when you want
+- **Cost tracking** — Monitor Gateway usage and cost in Vercel
@@ -6,7 +6,7 @@ description: "Configure Strix with Gemini models via Google Cloud"
 ## Setup

 ```bash
-export STRIX_LLM="vertex_ai/gemini-3-pro-preview"
+export STRIX_LLM="vertex_ai/gemini-4-argon"
 ```

 No API key required—uses Google Cloud Application Default Credentials.
@@ -29,8 +29,12 @@ export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"

 | Model | Description |
 |-------|-------------|
-| `vertex_ai/gemini-3-pro-preview` | Best overall performance for security testing |
-| `vertex_ai/gemini-3-flash-preview` | Faster and cheaper |
+| `vertex_ai/gemini-4-argon` | Best overall performance for security testing |
+| `vertex_ai/gemini-3.8-flash` | Faster and cheaper |
+
+<Note>
+Security testing with Gemini needs the [Fairwind Program](https://deepmind.google/fairwind-program/). Without it the cyber guardrails block most offensive tasks.
+</Note>

 ## Project Configuration

@@ -25,15 +25,22 @@ description: "Install Strix and run your first security scan"

 ## Configuration

-Set your LLM provider:
+Set your LLM provider. We recommend GLM-5.3 through [Vercel AI Gateway](/llm-providers/vercel-ai-gateway) or [OpenRouter](/llm-providers/openrouter):

-```bash
-export STRIX_LLM="openrouter/z-ai/glm-5.3"
+<CodeGroup>
+```bash Vercel AI Gateway
+export STRIX_LLM="vercel_ai_gateway/zai/glm-5.3"
 export LLM_API_KEY="your-api-key"
 ```

+```bash OpenRouter
+export STRIX_LLM="openrouter/z-ai/glm-5.3"
+export LLM_API_KEY="your-api-key"
+```
+</CodeGroup>
+
 <Tip>
-For best results, use `openrouter/z-ai/glm-5.3` (the default pick), `openai/gpt-5.4`, `anthropic/claude-opus-4-6`, or `openai/gpt-5.2`.
+For best results, use `zai/glm-5.3`, `deepseek/deepseek-v4.1-flash-1017`, `anthropic/claude-opus-5-5` (needs [CVP](https://support.claude.com/en/articles/14604842-cyber-verification-program) access), or `openai/gpt-6.1-sol` (needs [Daybreak](https://openai.com/daybreak/) access).
 </Tip>

 ## Run Your First Scan
@@ -49,7 +49,7 @@ Strix runs inside a Kali Linux-based Docker container with a comprehensive set o

 | Tool                                                    | Description                                   |
 | ------------------------------------------------------- | --------------------------------------------- |
-| [Semgrep](https://github.com/semgrep/semgrep)          | Fast SAST and custom rule matching            |
+| [Opengrep](https://github.com/opengrep/opengrep)        | Fast SAST and custom rule matching            |
 | [ast-grep](https://ast-grep.github.io)                 | Structural AST/CST-aware code search (`sg`)   |
 | [Tree-sitter](https://tree-sitter.github.io/tree-sitter/) | Syntax tree parsing and symbol extraction (Java/JS/TS/Python/Go/Bash/JSON/YAML grammars pre-configured) |
 | [Bandit](https://bandit.readthedocs.io)                | Python security linter                        |
@@ -33,7 +33,7 @@ sqlmap -u "https://example.com/page?id=1"

 ```bash
 # Fast SAST triage
-semgrep --config auto ./src
+opengrep scan --config /opt/opengrep-rules ./src

 # Structural AST search
 sg scan ./src
@@ -46,7 +46,7 @@ Thorough penetration testing. Best for:

 **Duration**: 1-4 hours depending on target complexity

-**White-box behavior**: Runs broad source-aware triage (`semgrep`, AST structural search, secrets, supply-chain checks) and then systematically validates top candidates dynamically.
+**White-box behavior**: Runs broad source-aware triage (`opengrep`, AST structural search, secrets, supply-chain checks) and then systematically validates top candidates dynamically.

 <Note>
 Deep mode is the default. It explores edge cases, chained vulnerabilities, and complex attack paths.
@@ -4,7 +4,7 @@ set -euo pipefail

 APP=strix
 REPO="usestrix/strix"
-STRIX_IMAGE="ghcr.io/usestrix/strix-sandbox:1.3.0"
+STRIX_IMAGE="ghcr.io/usestrix/strix-sandbox:1.4.0"

 MUTED='\033[0;2m'
 RED='\033[0;31m'
@@ -63,7 +63,7 @@ jobs:
          fi
 ```

-Then tell the user to add two repository secrets: `STRIX_LLM` (model id, for example `openai/gpt-5.4`) and `LLM_API_KEY` (the provider key). Do not create these values yourself.
+Then tell the user to add two repository secrets: `STRIX_LLM` (model id, for example `openrouter/z-ai/glm-5.3`) and `LLM_API_KEY` (the provider key). Do not create these values yourself.

 Notes:
 - In CI/headless runs Strix automatically scopes to the PR's changed files (`--scope-mode auto`). If diff resolution fails, keep `fetch-depth: 0` or set `--diff-base` to the PR's actual base branch — use `origin/${{ github.base_ref }}` in GitHub Actions rather than a hard-coded `origin/main`, since repos use different default branches.
@@ -47,7 +47,7 @@ If unsure and the user has (or will create) an app.strix.ai account, prefer **Cl
   ```
 3. **LLM configured** — two environment variables:
   ```bash
-   export STRIX_LLM="openai/gpt-5.4"      # any LiteLLM model id (openai/..., anthropic/..., openrouter/...)
+   export STRIX_LLM="openrouter/z-ai/glm-5.3"      # any LiteLLM model id (openai/..., anthropic/..., openrouter/...)
   export LLM_API_KEY="<provider api key>"
   ```
   Ask the user for these if unset. Never hardcode or commit keys.
@@ -620,6 +620,15 @@ def _finish_tool_use_behavior(


 _COVERAGE_TOOLS: tuple[Tool, ...] = (record_coverage, update_coverage, list_coverage)
+_FINISH_SCAN_COVERAGE_STEP = re.compile(
+    r"\n5\. \*\*Coverage reconciliation\.\*\*.*?(?=\n\n)", re.DOTALL
+)
+
+
+def _without_coverage_step(tool: FunctionTool) -> FunctionTool:
+    description = _FINISH_SCAN_COVERAGE_STEP.sub("", tool.description)
+    return dataclasses.replace(tool, description=description)
+

 _BASE_TOOLS: tuple[Tool, ...] = (
    think,
@@ -719,6 +728,7 @@ def build_strix_agent(
    is_whitebox: bool = False,
    is_diff_scoped: bool = False,
    interactive: bool = False,
+    parallel_tool_calls: bool = False,
    chat_completions_tools: bool = False,
    strict_tool_schemas: bool = True,
    system_prompt_context: dict[str, Any] | None = None,
@@ -756,6 +766,7 @@ def build_strix_agent(
            is_root=is_root,
            is_diff_scoped=is_diff_scoped,
            interactive=interactive,
+            parallel_tool_calls=parallel_tool_calls,
            system_prompt_context=system_prompt_context,
            supports_images=supports_images,
            report_tool=report_tool.name,
@@ -769,6 +780,8 @@ def build_strix_agent(
    base_tools = [report_tool if t is create_vulnerability_report else t for t in _BASE_TOOLS]
    if not coverage_tools:
        base_tools = [t for t in base_tools if t not in _COVERAGE_TOOLS]
+        if finish_tool is finish_scan:
+            finish_tool = _without_coverage_step(finish_scan)
    if is_root:
        tools: list[Tool] = [*base_tools, *agent_tools, finish_tool]
    else:
@@ -821,6 +834,7 @@ def make_child_factory(
    is_whitebox: bool = False,
    is_diff_scoped: bool = False,
    interactive: bool = False,
+    parallel_tool_calls: bool = False,
    chat_completions_tools: bool = False,
    strict_tool_schemas: bool = True,
    system_prompt_context: dict[str, Any] | None = None,
@@ -844,6 +858,7 @@ def make_child_factory(
            is_whitebox=is_whitebox,
            is_diff_scoped=is_diff_scoped,
            interactive=interactive,
+            parallel_tool_calls=parallel_tool_calls,
            chat_completions_tools=chat_completions_tools,
            strict_tool_schemas=strict_tool_schemas,
            system_prompt_context=system_prompt_context,
@@ -83,6 +83,7 @@ def render_system_prompt(
    is_root: bool = False,
    is_diff_scoped: bool = False,
    interactive: bool = False,
+    parallel_tool_calls: bool = False,
    system_prompt_context: dict[str, Any] | None = None,
    include_scope: bool = True,
    supports_images: bool = True,
@@ -134,6 +135,7 @@ def render_system_prompt(
            requested_skill_names=[name for name in skill_content if name not in shared],
            available_skills=get_available_skills(),
            interactive=interactive,
+            parallel_tool_calls=parallel_tool_calls,
            is_root=is_root,
            system_prompt_context=system_prompt_context or {},
            include_scope=include_scope,
@@ -44,7 +44,11 @@ INTERACTIVE BEHAVIOR:
 - If you end a turn on plain text and the nudge arrives, your words already reached the user. Do not repeat them: call wait_for_user.
 - You can narrate while you work — plain text is shown to the user as you go. Narrating is free; wait_for_user is specifically the act of WAITING for the user, so do not call it just to give a status update.
 - Respond naturally when the user asks questions or gives instructions.
+{%- if parallel_tool_calls %}
+- While actively working on a task, every turn should carry a tool call (independent calls can go in the same turn) — use think to plan, the appropriate tool to act, and wait_for_user only when you genuinely need the user.
+{%- else %}
 - While actively working on a task, every turn should carry exactly one tool call — use think to plan, the appropriate tool to act, and wait_for_user only when you genuinely need the user.
+{%- endif %}
 - Never loop through think or other tools just to prepare, polish, confirm, or announce an answer. Once you know the answer, write it and call wait_for_user.
 {% else %}
 AUTONOMOUS BEHAVIOR:
@@ -55,6 +59,9 @@ AUTONOMOUS BEHAVIOR:
 - There is no user attached to this run, so there is nobody to ask and nothing to yield to. If there is nothing left to execute: do NOT send filler/repetitive text — either call wait_for_agents (only if you are genuinely expecting another agent to message you) or finish your work (subagents: agent_finish; root: finish_scan)
 - While the agent loop is running, almost every output MUST be a tool call. Do NOT send plain text messages; act via tools. If waiting on another agent, use wait_for_agents; when done, use agent_finish (subagents) or finish_scan (root)
 - A text-only turn does nothing: it neither ends the run nor yields — it just wastes a turn and forces a retry. The lifecycle tools (``finish_scan`` for root, ``agent_finish`` for subagents) are the ONLY way to terminate, and the report flows through them. If you find yourself wanting to say "Done!" or "Scan complete" without a tool call, call the lifecycle tool instead.
+{%- if parallel_tool_calls %}
+- Read-only calls can go together in one turn: when several file reads or searches don't depend on each other's output, issue them as separate tool calls in the same turn rather than one per turn. Anything else — writing files, running commands that change state, sending requests, creating or messaging agents, reporting, or finishing — goes alone in its own turn.
+{%- endif %}
 {% endif %}
 </communication_rules>

@@ -108,11 +115,11 @@ BLACK-BOX TESTING (domain/subdomain only):

 WHITE-BOX TESTING (code provided):
 - MUST perform BOTH static AND dynamic analysis
- Static: Use source-aware triage first to map risk quickly (`semgrep`, `ast-grep`, Tree-sitter tooling, `gitleaks`, `trufflehog`, `trivy fs`). Then review code for vulnerabilities
+- Static: Use source-aware triage first to map risk quickly (`opengrep`, `ast-grep`, Tree-sitter tooling, `gitleaks`, `trufflehog`, `trivy fs`). Then review code for vulnerabilities
 - Static coverage floor: execute at least one structural AST mapping pass (`sg` and/or Tree-sitter) per repository and keep artifact output
- Static coverage target per repository: run one `semgrep` pass, one secrets pass (`gitleaks` and/or `trufflehog`), one `trivy fs` pass, and one AST-structural pass (`sg` and/or Tree-sitter)
+- Static coverage target per repository: run one `opengrep` pass, one secrets pass (`gitleaks` and/or `trufflehog`), one `trivy fs` pass, and one AST-structural pass (`sg` and/or Tree-sitter)
 - Keep AST artifacts bounded and high-signal: scope to relevant paths/hypotheses, avoid whole-repo generic function dumps
- AST target selection rule: build `sg-targets.txt` from `semgrep.json` scope first (`paths.scanned`, fallback to unique `results[].path`), then run `xargs ... sg run` against that file list. Only use path-heuristic fallback if semgrep scope is unavailable.
+- AST target selection rule: build `sg-targets.txt` from `opengrep.json` scope first (`paths.scanned`, fallback to unique `results[].path`), then run `xargs ... sg run` against that file list. Only use path-heuristic fallback if opengrep scope is unavailable.
 - Dynamic: Run the application and test live to validate exploitability
 - NEVER rely solely on static code analysis when dynamic validation is possible
 - Begin with fast source triage and dynamic run preparation in parallel; use static findings to prioritize live testing.
@@ -181,7 +188,7 @@ EFFICIENCY TACTICS:
 - For Caido proxy automation inside Python, explicitly import from
  `caido_api`:
  `from caido_api import list_requests, view_request, repeat_request, list_sitemap, view_sitemap_entry, scope_rules`
- Prefer established fuzzers/scanners where applicable: ffuf, sqlmap, nuclei, wapiti, arjun, httpx, katana, semgrep, bandit, trufflehog, nmap. Use scripts mainly to coordinate or validate around them, not to replace them without reason
+- Prefer established fuzzers/scanners where applicable: ffuf, sqlmap, nuclei, wapiti, arjun, httpx, katana, opengrep, bandit, trufflehog, nmap. Use scripts mainly to coordinate or validate around them, not to replace them without reason
 - For trial-heavy vectors (SQLi, XSS, XXE, SSRF, RCE, auth/JWT, deserialization), DO NOT iterate payloads manually in the browser. Always spray payloads via Python scripts through `exec_command` or terminal tools.
 - When using established fuzzers/scanners, use the proxy for inspection where helpful
 - Generate/adapt large payload corpora: combine encodings (URL, unicode, base64), comment styles, wrappers, time-based/differential probes. Expand with wordlists/templates
@@ -451,7 +458,7 @@ JAVASCRIPT ANALYSIS:
 - js-beautify - JS beautifier/deobfuscator

 CODE ANALYSIS:
- semgrep - Static analysis/SAST
+- opengrep - Static analysis/SAST (bundled rules in /opt/opengrep-rules)
 - ast-grep (sg) - Structural AST/CST-aware code search
 - tree-sitter - Syntax-aware parsing and symbol extraction support
 - bandit - Python security linter
@@ -713,7 +713,7 @@ def configure_sdk_model_defaults(settings: Settings) -> None:
    if codex.subscription_model(llm.model):
        return
    _configure_litellm_compatibility()
-    _configure_openrouter_attribution(llm.model)
+    _configure_attribution(llm.model)
    if llm.api_key:
        set_default_openai_key(llm.api_key, use_for_tracing=False)
        _configure_litellm_default("api_key", llm.api_key)
@@ -884,9 +884,13 @@ def _openrouter_error_type(error: object) -> object:
    return None


-OPENROUTER_ATTRIBUTION_HEADERS = {
+APP_ATTRIBUTION_HEADERS = {
    "HTTP-Referer": "https://strix.ai",
    "X-Title": "Strix",
+}
+
+OPENROUTER_ATTRIBUTION_HEADERS = {
+    **APP_ATTRIBUTION_HEADERS,
    "X-OpenRouter-Categories": "cli-agent",
 }

@@ -895,20 +899,25 @@ def is_openrouter_model(model_name: str | None) -> bool:
    return bool(model_name) and "openrouter/" in (model_name or "").strip().lower()


-def _configure_openrouter_attribution(model_name: str | None) -> None:
+def is_vercel_ai_gateway_model(model_name: str | None) -> bool:
+    return (model_name or "").strip().lower().startswith("vercel_ai_gateway/")
+
+
+def attribution_headers(model_name: str | None) -> dict[str, str]:
+    if is_openrouter_model(model_name):
+        return OPENROUTER_ATTRIBUTION_HEADERS
+    if is_vercel_ai_gateway_model(model_name):
+        return APP_ATTRIBUTION_HEADERS
+    return {}
+
+
+def _configure_attribution(model_name: str | None) -> None:
    import litellm

    current: object = litellm.headers
    existing: dict[str, str] = current if isinstance(current, dict) else {}
-    if not is_openrouter_model(model_name):
-        if any(key in existing for key in OPENROUTER_ATTRIBUTION_HEADERS):
-            remaining = {
-                k: v for k, v in existing.items() if k not in OPENROUTER_ATTRIBUTION_HEADERS
-            }
-            litellm.headers = remaining or None  # type: ignore[assignment]
-        return
-
-    litellm.headers = {**existing, **OPENROUTER_ATTRIBUTION_HEADERS}  # type: ignore[assignment]
+    remaining = {k: v for k, v in existing.items() if k not in OPENROUTER_ATTRIBUTION_HEADERS}
+    litellm.headers = {**remaining, **attribution_headers(model_name)} or None  # type: ignore[assignment]


 def _configure_extra_headers(llm: LlmSettings) -> None:
@@ -61,10 +61,23 @@ class LlmSettings(BaseSettings):
        repr=False,
    )
    reasoning_effort: ReasoningEffort = Field(default="high", alias="STRIX_REASONING_EFFORT")
+    input_cost_per_mtok: float | None = Field(
+        default=None, ge=0, allow_inf_nan=False, alias="STRIX_LLM_INPUT_COST_PER_MTOK"
+    )
+    output_cost_per_mtok: float | None = Field(
+        default=None, ge=0, allow_inf_nan=False, alias="STRIX_LLM_OUTPUT_COST_PER_MTOK"
+    )
+    cached_input_cost_per_mtok: float | None = Field(
+        default=None, ge=0, allow_inf_nan=False, alias="STRIX_LLM_CACHED_INPUT_COST_PER_MTOK"
+    )
    force_required_tool_choice: bool = Field(
        default=False,
        alias="STRIX_FORCE_REQUIRED_TOOL_CHOICE",
    )
+    parallel_tool_calls: bool = Field(
+        default=False,
+        alias="STRIX_PARALLEL_TOOL_CALLS",
+    )
    prompt_cache: bool = Field(
        default=True,
        alias="STRIX_PROMPT_CACHE",
@@ -151,7 +164,7 @@ class RuntimeSettings(BaseSettings):
    model_config = _BASE_CONFIG

    image: str = Field(
-        default="ghcr.io/usestrix/strix-sandbox:1.3.0",
+        default="ghcr.io/usestrix/strix-sandbox:1.4.0",
        alias="STRIX_IMAGE",
    )
    backend: str = Field(default="docker", alias="STRIX_RUNTIME_BACKEND")
@@ -1009,7 +1009,7 @@ async def _append_tool_required_message(
        message = (
            "Your previous message ended a turn without a tool call. Plain text never ends "
            "execution and never hands control to the user: it is shown to the user, and the "
-            "run continues. Continue immediately and call exactly one tool. "
+            "run continues. Continue immediately and call a tool. "
            "If you have nothing to do until the user replies, call wait_for_user; your "
            "text already reached them, so do not repeat it. "
            "If you are blocked waiting for another agent, call wait_for_agents. "
@@ -1021,7 +1021,7 @@ async def _append_tool_required_message(
        message = (
            "Your previous response ended the autonomous run without a lifecycle tool "
            "call. That is invalid in non-interactive mode; plain text final answers are "
-            "ignored. Continue immediately and call exactly one tool. "
+            "ignored. Continue immediately and call a tool. "
            f"If your work is complete, call {finish_tool}. "
            "If you are blocked waiting for another agent, call wait_for_agents. "
            "Otherwise use the appropriate execution or planning tool. "
@@ -10,12 +10,11 @@ from openai.types.shared import Reasoning

 from strix.config.models import (
    DEFAULT_MODEL_RETRY,
-    OPENROUTER_ATTRIBUTION_HEADERS,
+    attribution_headers,
    bedrock_route_supports_prompt_caching,
    is_bedrock_route,
    is_claude_model,
    is_known_openai_bare_model,
-    is_openrouter_model,
    model_supports_reasoning,
    request_timeout_extra_args,
    routes_through_litellm,
@@ -247,6 +246,7 @@ def make_model_settings(
    *,
    model_name: str,
    force_required_tool_choice: bool = False,
+    parallel_tool_calls: bool = False,
    request_timeout: float | None = None,
    prompt_cache: bool = True,
    extra_headers: dict[str, str] | None = None,
@@ -255,7 +255,7 @@ def make_model_settings(
 ) -> ModelSettings:
    headers = _request_headers(model_name, extra_headers)
    model_settings = ModelSettings(
-        parallel_tool_calls=False if has_tools else None,
+        parallel_tool_calls=parallel_tool_calls if has_tools else None,
        retry=DEFAULT_MODEL_RETRY,
        include_usage=True,
        extra_args=request_timeout_extra_args(request_timeout),
@@ -286,9 +286,7 @@ def make_model_settings(
 def _request_headers(
    model_name: str, extra_headers: dict[str, str] | None
 ) -> dict[str, str] | None:
-    headers: dict[str, str] = {}
-    if is_openrouter_model(model_name):
-        headers.update(OPENROUTER_ATTRIBUTION_HEADERS)
+    headers: dict[str, str] = dict(attribution_headers(model_name))
    if extra_headers:
        headers.update(extra_headers)
    return headers or None
@@ -176,13 +176,17 @@ def _compose_root_instructions_override(
    is_whitebox: bool,
    is_diff_scoped: bool,
    interactive: bool,
+    parallel_tool_calls: bool,
    system_prompt_context: dict[str, Any],
    supports_images: bool,
    report_tool: str,
    coverage_tools: bool,
+    replace_scan_prompt: bool = False,
 ) -> str | None:
    if root_instructions_override is None:
        return None
+    if replace_scan_prompt:
+        return f"{root_instructions_override}\n\n{render_scope_prompt(system_prompt_context)}"

    base_instructions = render_system_prompt(
        skills=skills,
@@ -191,6 +195,7 @@ def _compose_root_instructions_override(
        is_root=True,
        is_diff_scoped=is_diff_scoped,
        interactive=interactive,
+        parallel_tool_calls=parallel_tool_calls,
        system_prompt_context=system_prompt_context,
        include_scope=False,
        supports_images=supports_images,
@@ -223,6 +228,7 @@ async def run_strix_scan(
    cleanup_on_exit: bool = True,
    event_sink: StreamEventSink | None = None,
    root_instructions_override: str | None = None,
+    replace_root_scan_prompt: bool = False,
    extra_system_prompt_context: dict[str, Any] | None = None,
    status_sink: StatusSink | None = None,
    mcp_connection_requests: list[McpConnectionRequest] | None = None,
@@ -236,6 +242,8 @@ async def run_strix_scan(

    ``root_instructions_override`` adds root scan instructions to the rendered
    root prompt without replacing the system-verified scope block.
+    ``replace_root_scan_prompt=True`` makes it the root's whole prompt, followed
+    by the scope, instead of adding it to the rendered scan prompt.
    ``extra_files`` entries (``{"workspace_path", "content"}``) are placed into
    the sandbox workspace at session bring-up; see
    :func:`strix.runtime.session_manager.create_or_reuse`.
@@ -418,6 +426,7 @@ async def run_strix_scan(
            settings.llm.reasoning_effort,
            model_name=resolved_model,
            force_required_tool_choice=settings.llm.force_required_tool_choice,
+            parallel_tool_calls=settings.llm.parallel_tool_calls,
            request_timeout=settings.llm.timeout,
            prompt_cache=settings.llm.prompt_cache,
            extra_headers=settings.llm.extra_headers,
@@ -513,10 +522,12 @@ async def run_strix_scan(
            is_whitebox=is_whitebox,
            is_diff_scoped=is_diff_scoped,
            interactive=interactive,
+            parallel_tool_calls=settings.llm.parallel_tool_calls,
            system_prompt_context=root_context,
            supports_images=supports_images,
            report_tool=report_tool.name,
            coverage_tools=coverage_tools,
+            replace_scan_prompt=replace_root_scan_prompt,
        )

        root_agent = build_strix_agent(
@@ -527,6 +538,7 @@ async def run_strix_scan(
            is_whitebox=is_whitebox,
            is_diff_scoped=is_diff_scoped,
            interactive=interactive,
+            parallel_tool_calls=settings.llm.parallel_tool_calls,
            chat_completions_tools=chat_completions_tools,
            strict_tool_schemas=strict_tool_schemas,
            system_prompt_context=root_context,
@@ -551,6 +563,7 @@ async def run_strix_scan(
            is_whitebox=is_whitebox,
            is_diff_scoped=is_diff_scoped,
            interactive=interactive,
+            parallel_tool_calls=settings.llm.parallel_tool_calls,
            chat_completions_tools=chat_completions_tools,
            strict_tool_schemas=strict_tool_schemas,
            system_prompt_context=scope_context,
@@ -300,7 +300,15 @@ def _parse_dedupe_response(content: str) -> dict[str, Any]:
        raise ValueError(f"No JSON object found in dedupe response: {content[:500]}")
    parsed = json.loads(text[start : end + 1])

-    duplicate_id = str(parsed.get("duplicate_id") or "")[:64]
+    is_duplicate = parsed.get("is_duplicate")
+    if not isinstance(is_duplicate, bool):
+        raise TypeError("is_duplicate must be a boolean")
+    duplicate_id = parsed.get("duplicate_id") or ""
+    if is_duplicate and (not isinstance(duplicate_id, str) or not duplicate_id):
+        raise ValueError("A duplicate decision must name an existing report ID")
+    # Keep the ID verbatim: truncating or stripping it could turn an unknown ID
+    # into a different, existing one before the membership check below.
+    duplicate_id = str(duplicate_id)
    reason = str(parsed.get("reason") or "")[:500]
    try:
        confidence = float(parsed.get("confidence", 0.0))
@@ -308,13 +316,22 @@ def _parse_dedupe_response(content: str) -> dict[str, Any]:
        confidence = 0.0

    return {
-        "is_duplicate": bool(parsed.get("is_duplicate", False)),
+        "is_duplicate": is_duplicate,
        "duplicate_id": duplicate_id,
        "confidence": confidence,
        "reason": reason,
    }


+def _validate_duplicate_reference(
+    result: dict[str, Any], existing_reports: list[dict[str, Any]]
+) -> None:
+    if result["is_duplicate"] and not any(
+        report.get("id") == result["duplicate_id"] for report in existing_reports
+    ):
+        raise ValueError("Duplicate decision refers to an unknown report ID")
+
+
 def _extract_text(response: ModelResponse) -> str:
    parts: list[str] = []
    for item in response.output:
@@ -397,6 +414,7 @@ async def check_duplicate(
            }

        result = _parse_dedupe_response(content)
+        _validate_duplicate_reference(result, existing_reports)

        logger.info(
            "Deduplication check: is_duplicate=%s, confidence=%.2f, reason=%s",
@@ -8,11 +8,38 @@ from typing import Any
 from agents.usage import Usage, deserialize_usage, serialize_usage
 from pydantic import BaseModel, TypeAdapter, ValidationError

+from strix.config.loader import load_settings
 from strix.report.pricing import resolve_litellm_model


 logger = logging.getLogger(__name__)

+Prices = tuple[float | None, float | None, float | None]
+
+
+def _configured_prices() -> tuple[str | None, Prices | None]:
+    llm = load_settings().llm
+    prices = (llm.input_cost_per_mtok, llm.output_cost_per_mtok, llm.cached_input_cost_per_mtok)
+    if all(price is None for price in prices):
+        return None, None
+    return (llm.model or "").strip() or None, prices
+
+
+def _configured_usage_cost(usage: Usage, prices: Prices) -> float:
+    input_rate, output_rate, cached_rate = prices
+    input_tokens = max(0, _int_or_zero(getattr(usage, "input_tokens", 0)))
+    cached_details = getattr(usage, "input_tokens_details", None)
+    cached_tokens = max(0, _int_or_zero(getattr(cached_details, "cached_tokens", 0)))
+    cached_tokens = min(cached_tokens, input_tokens)
+    output_tokens = max(0, _int_or_zero(getattr(usage, "output_tokens", 0)))
+    billable_input = input_tokens - cached_tokens
+    cost = (
+        billable_input * (input_rate or 0)
+        + cached_tokens * (cached_rate if cached_rate is not None else input_rate or 0)
+        + output_tokens * (output_rate or 0)
+    ) / 1_000_000
+    return _round_cost(cost)
+

 class ProviderUsage(BaseModel):
    """Running totals for one upstream provider OpenRouter routed calls to."""
@@ -37,6 +64,7 @@ class LLMUsageLedger:
        self._total_usage = Usage()
        self._agent_usage: dict[str, Usage] = {}
        self._agent_metadata: dict[str, dict[str, str]] = {}
+        self._agent_estimated_cost: dict[str, float] = {}
        self._observed_cost = 0.0
        self._estimated_cost = 0.0
        self._has_observed_cost = False
@@ -73,6 +101,9 @@ class LLMUsageLedger:
            estimated = _estimate_litellm_cost(usage, model)
            if estimated:
                self._estimated_cost += estimated
+                self._agent_estimated_cost[normalized_agent_id] = (
+                    self._agent_estimated_cost.get(normalized_agent_id, 0.0) + estimated
+                )

        return True

@@ -114,11 +145,23 @@ class LLMUsageLedger:
    def total_cost(self) -> float:
        if self.zero_cost:
            return 0.0
+        model, prices = _configured_prices()
+        if prices is not None:
+            return _round_cost(
+                sum(self._configured_agent_cost(aid, model, prices) for aid in self._agent_usage)
+            )
        return _round_cost(self._observed_cost if self._has_observed_cost else self._estimated_cost)

+    def _configured_agent_cost(self, agent_id: str, model: str | None, prices: Prices) -> float:
+        if self._agent_metadata.get(agent_id, {}).get("model") == model:
+            return _configured_usage_cost(self._agent_usage[agent_id], prices)
+        return self._agent_estimated_cost.get(agent_id, 0.0)
+
    def to_record(self) -> dict[str, Any]:
        record = serialize_usage(self._total_usage)
-        record["cost"] = self.total_cost
+        model, prices = _configured_prices()
+        total_cost = self.total_cost
+        record["cost"] = total_cost
        record["providers"] = {name: tally.model_dump() for name, tally in self._providers.items()}
        record["agents"] = []

@@ -128,7 +171,11 @@ class LLMUsageLedger:
            usage = self._agent_usage[agent_id]
            metadata = self._agent_metadata.get(agent_id, {})
            agent_cost = (
-                self.total_cost * (agent_tokens[agent_id] / total_tokens) if total_tokens else 0.0
+                self._configured_agent_cost(agent_id, model, prices)
+                if prices is not None and not self.zero_cost
+                else total_cost * (agent_tokens[agent_id] / total_tokens)
+                if total_tokens
+                else 0.0
            )

            agent_record = serialize_usage(usage)
@@ -148,6 +195,7 @@ class LLMUsageLedger:
        self._total_usage = Usage()
        self._agent_usage.clear()
        self._agent_metadata.clear()
+        self._agent_estimated_cost.clear()
        self._observed_cost = 0.0
        self._estimated_cost = 0.0
        self._has_observed_cost = False
@@ -183,6 +231,7 @@ class LLMUsageLedger:
                logger.exception("Failed to hydrate llm_usage for agent %s", agent_id)
                self._agent_usage[agent_id] = Usage()

+            self._agent_estimated_cost[agent_id] = _float_or_zero(raw_agent.get("cost"))
            metadata: dict[str, str] = {}
            agent_name = raw_agent.get("agent_name")
            model = raw_agent.get("model")
@@ -66,6 +66,33 @@ def _apply_sandbox_network(create_kwargs: dict[str, Any]) -> None:
        create_kwargs.pop("ports", None)


+_SANDBOX_DEVICES_ENV = "STRIX_DOCKER_SANDBOX_DEVICES"
+
+
+def _sandbox_devices() -> list[str]:
+    raw = os.environ.get(_SANDBOX_DEVICES_ENV, "")
+    return [item.strip() for item in raw.split(",") if item.strip()]
+
+
+def _apply_sandbox_devices(create_kwargs: dict[str, Any]) -> None:
+    """Pass the host devices listed in ``STRIX_DOCKER_SANDBOX_DEVICES`` through.
+
+    Comma-separated device paths, each mapped to the same path in the container
+    with ``rwm`` access. A tunnel brought up inside the sandbox (OpenVPN,
+    Tailscale) needs ``/dev/net/tun``: Docker's default device cgroup denies it
+    even with ``NET_ADMIN``. Unset means no extra devices.
+    """
+    devices = _sandbox_devices()
+    if not devices:
+        return
+    existing = [str(item) for item in (create_kwargs.get("devices") or [])]
+    for path in devices:
+        spec = f"{path}:{path}:rwm"
+        if spec not in existing and path not in existing:
+            existing.append(spec)
+    create_kwargs["devices"] = existing
+
+
 def _apply_resource_limits(create_kwargs: dict[str, Any]) -> None:
    """Apply optional cgroup resource caps from the environment. Unset/blank
    values leave docker's default (unbounded), so this is opt-in per host."""
@@ -233,6 +260,7 @@ class StrixDockerSandboxClient(DockerSandboxClient):
        extra_hosts["host.docker.internal"] = "host-gateway"

        _apply_sandbox_network(create_kwargs)
+        _apply_sandbox_devices(create_kwargs)
        _apply_resource_limits(create_kwargs)
        _apply_log_limits(create_kwargs)
        _apply_run_labels(create_kwargs)
@@ -40,7 +40,7 @@ The skills are dynamically injected into the agent's system prompt, allowing it

 Notable source-aware skills:
 - `source_aware_whitebox` (coordination): white-box orchestration playbook
- `source_aware_sast` (custom): semgrep/AST/secrets/supply-chain static triage workflow
+- `source_aware_sast` (custom): opengrep/AST/secrets/supply-chain static triage workflow
 - `dependency_cve_scanning` (custom): trivy-based SCA workflow for reporting known dependency CVEs via `create_dependency_report`
 - `npx_confusion` (custom): npx/npm exec/bunx fallback and adjacent package-runner identity confusion, with runner-specific registry and reporting gates
 - `semantic_confusion` (vulnerabilities): cross-boundary parser, normalization, and representation mismatch analysis
@@ -14,22 +14,22 @@ Increase white-box coverage by combining source-aware triage with dynamic valida
 ## Recommended Workflow

 1. Build a quick source map before deep exploitation, including at least one AST-structural pass (`sg` or `tree-sitter`) scoped to relevant paths.
-   - For `sg` baseline, derive `sg-targets.txt` from `semgrep.json` scope first (`paths.scanned`, fallback to unique `results[].path`) and run `xargs ... sg run` on that list.
-   - Only fall back to path heuristics when semgrep scope is unavailable.
+   - For `sg` baseline, derive `sg-targets.txt` from `opengrep.json` scope first (`paths.scanned`, fallback to unique `results[].path`) and run `xargs ... sg run` on that list.
+   - Only fall back to path heuristics when opengrep scope is unavailable.
 2. Run first-pass static triage to rank high-risk paths.
 3. Use triage outputs to prioritize dynamic PoC validation.
 4. Keep findings evidence-driven: no report without validation.

 ## Source-Aware Triage Stack

- `semgrep`: fast security-first triage and custom pattern scans
+- `opengrep`: fast security-first triage and custom pattern scans
 - `ast-grep` (`sg`): structural pattern hunting and targeted repo mapping
 - `tree-sitter`: syntax-aware parsing support for symbol and route extraction
 - `gitleaks` + `trufflehog`: complementary secret detection (working tree and history coverage)
 - `trivy fs`: dependency, misconfiguration, license, and secret checks

 Coverage target per repository:
- one `semgrep` pass
+- one `opengrep` pass
 - one AST structural pass (`sg` and/or `tree-sitter`)
 - one secrets pass (`gitleaks` and/or `trufflehog`)
 - one `trivy fs` pass
@@ -136,8 +136,12 @@ fi
 - A finding with a call stack ⇒ `reachability=reachable_call_path`, put the
  call-path excerpt (entrypoint → vulnerable function) in
  `reachability_evidence`.
- Listed as affecting a required module but with no reachable symbol ⇒ fall
-  back to the import/symbol checks below (`imported` / `not_imported`).
+- Listed as affecting a required module but with no reachable symbol ⇒ the
+  call graph found no path to the vulnerable function: `not_reached` when
+  application code imports the module (cite the govulncheck result and the
+  import), `not_imported` when nothing imports it. When govulncheck could not
+  analyze the module (build errors, missing deps), fall back to the
+  import/symbol checks below instead of claiming a level.

 **All other ecosystems — import check, then symbol match:**

@@ -148,15 +152,25 @@ fi
   `PyYAML` → `yaml`). No hits ⇒ `not_imported`, with the search scope stated
   in `reachability_evidence`. For a **transitive** dependency, the check is
   whether application code imports it directly; if not, it is reachable only
-   through the direct dependency — check whether the direct dep's usage can
-   hit it (if unclear, use `imported` when the direct dep is used at all).
+   through the packages that depend on it — walk every introducing chain
+   from the lockfile (e.g. `express > body-parser > qs`) and inspect the code
+   of the package one hop above the vulnerable one on each chain
+   (`body-parser`, not just `express`; its installed sources, or the class
+   references of its jar) for the affected API. No chain references it ⇒
+   `not_reached` (below), citing every artifact you inspected; referenced ⇒
+   that package's call is the usage site, continue the trace upward from
+   there. If you could not inspect every chain, or one of them is unclear,
+   the package is still loaded, so the level is `imported`, never
+   `not_reached`.
 2. **Symbol match — per CVE, not per package.** Read each CVE's own advisory
   (GHSA/NVD/OSV `affected[].ecosystem_specific.imports` or the advisory
   text) for the affected functions/classes/APIs. Search application code for
   those symbols (`ast-grep` pattern or `rg -n`). Hits ⇒
   `vulnerable_symbol_used`, with repo-relative `file:line` of each hit (up
-   to a handful) in `reachability_evidence`. Imported but no affected-symbol
-   usage found (or the advisory names no symbols) ⇒ `imported`.
+   to a handful) in `reachability_evidence`. Imported, search completed, and
+   you can say why the vulnerable code does not run here ⇒ `not_reached`
+   (below). Imported but the search was partial, the result is unclear, or
+   the advisory names no symbols ⇒ `imported`.
   Different CVEs on the same package usually affect **different** symbols
   (one hits a parser, another a header check) — never copy one CVE's
   verdict/evidence onto its siblings; run the symbol search against each
@@ -182,6 +196,23 @@ fi
   dynamic loading, unparsable sources) ⇒ `unknown` and say why in
   `assumptions`.

+**`not_reached` — loaded, but this CVE's vulnerable code does not run here.**
+Use it when the package is imported (so `not_imported` is wrong) and you
+showed that the code path the advisory describes cannot execute in this
+codebase, and `reachability_evidence` says what you checked. The judgement is
+per CVE: it rests on what triggers *this* flaw (an affected function, a mode,
+an option, a kind of input) against what this code does. Situations that
+commonly support it, as inspiration rather than a checklist: the affected API
+has no caller in application code and the search was complete; the only call
+site is behind a condition that is false here (a polyfill for an older
+runtime, a flag that is off, a path only a dev tool runs); a transitive
+package's parent never references the affected API; the package is build or
+test tooling that the shipped artifact never executes; a library compiled into
+the runtime whose flaw sits in a mode the application never uses (a TLS server
+path in a client-only service). An inconclusive or skipped check stays
+`imported`. `not_reached` is still an evidence level, not proof of safety:
+dynamic dispatch, reflection and framework wiring can evade static search.
+
 Cheap-first budgeting: the import check is one search per package — always do
 it. Do the per-CVE symbol match for every CVE whose advisory names affected
 symbols (they can be batched into one multi-pattern search per package);
@@ -189,13 +220,18 @@ prioritize `critical`/`high`/KEV when the budget is tight; a CVE whose symbol
 search was skipped may still be reported as `imported` (the import check is
 real evidence), but its `reachability_evidence` must state that the
 affected-symbol check was not performed, so a skipped search is never
-mistaken for a completed one with no hits. Never let this analysis stall
+mistaken for a completed one with no hits, and is never filed as
+`not_reached`. Never let this analysis stall
 reporting — `unknown` with a reason beats an unverified claim.

 Anti-overclaim rules:

 - `not_imported` still does NOT mean safe (dynamic `import()`/reflection/
  framework wiring evade static search) — never phrase it as "not exploitable".
+- `not_reached` needs the check that shows the vulnerable code does not run,
+  named in `reachability_evidence`; "no call found" after a partial search is
+  `imported`, and one CVE's `not_reached` never carries over to a sibling CVE
+  on the same package.
 - `reachable_call_path` is reserved for call-graph tools (govulncheck); a
  symbol grep hit is `vulnerable_symbol_used`, no matter how convinced you are.
 - The tool rejects any level other than `unknown` without
@@ -1,6 +1,6 @@
 ---
 name: source-aware-sast
-description: Practical source-aware SAST and AST playbook for semgrep, ast-grep, gitleaks, and trivy fs
+description: Practical source-aware SAST and AST playbook for opengrep, ast-grep, gitleaks, and trivy fs
 ---

 # Source-Aware SAST Playbook
@@ -23,19 +23,19 @@ Run this baseline once per repository before deep narrowing:
 ART=/workspace/.source-aware
 mkdir -p "$ART"

-semgrep scan --config p/default --config p/golang --config p/secrets \
-  --metrics=off --json --output "$ART/semgrep.json" .
-# Build deterministic AST targets from semgrep scope (no hardcoded path guessing)
+opengrep scan --config /opt/opengrep-rules --taint-intrafile \
+  --json --output "$ART/opengrep.json" .
+# Build deterministic AST targets from opengrep scope (no hardcoded path guessing)
 python3 - <<'PY'
 import json
 from pathlib import Path

 art = Path("/workspace/.source-aware")
-semgrep_json = art / "semgrep.json"
+opengrep_json = art / "opengrep.json"
 targets_file = art / "sg-targets.txt"

 try:
-    data = json.loads(semgrep_json.read_text(encoding="utf-8"))
+    data = json.loads(opengrep_json.read_text(encoding="utf-8"))
 except Exception:
    targets_file.write_text("", encoding="utf-8")
    raise
@@ -63,17 +63,18 @@ trivy fs --scanners vuln,misconfig --timeout 30m --offline-scan \
  --format json --output "$ART/trivy-fs.json" . || true
 ```

-## Semgrep First Pass
+## Opengrep First Pass

-Use Semgrep as the default static triage pass:
+Use Opengrep as the default static triage pass. Rules are bundled offline in `/opt/opengrep-rules` (one directory per language, plus `generic/secrets`):

 ```bash
-# Preferred deterministic profile set (works with --metrics=off)
-semgrep scan --config p/default --config p/golang --config p/secrets \
-  --metrics=off --json --output /workspace/.source-aware/semgrep.json .
+# Full bundled ruleset
+opengrep scan --config /opt/opengrep-rules --taint-intrafile \
+  --json --output /workspace/.source-aware/opengrep.json .

-# If you choose auto config, do not combine it with --metrics=off
-semgrep scan --config auto --json --output /workspace/.source-aware/semgrep-auto.json .
+# Narrow to the target stack when the full set is slow or noisy
+opengrep scan --config /opt/opengrep-rules/python --config /opt/opengrep-rules/generic/secrets \
+  --taint-intrafile --json --output /workspace/.source-aware/opengrep-python.json .
 ```

 If diff scope is active, restrict to changed files first, then expand only when needed.
@@ -205,10 +205,10 @@ Jinja2 backend without autoescape: `{{7*7}}`, RCE gadgets if sandbox misconfigur

 ## Tooling

-Static analysis is the fastest way to reach the sinks above in white-box scope. The sandbox ships `python`/`pipx`, `semgrep`, `bandit`, `ast-grep`, and `ripgrep`.
+Static analysis is the fastest way to reach the sinks above in white-box scope. The sandbox ships `python`/`pipx`, `opengrep`, `bandit`, `ast-grep`, and `ripgrep`.

 - **bandit** (preinstalled) — Python security linter; flags `mark_safe`, `extra()`, `RawSQL`, `subprocess`, weak crypto, hardcoded secrets: `bandit -r . -ll`
- **semgrep** (preinstalled) with the Django ruleset — higher-signal than bandit for framework-specific bugs (`.extra()`, `RawSQL`, `|safe`, `csrf_exempt`, `ALLOWED_HOSTS=['*']`): `semgrep --config p/django .`
+- **opengrep** (preinstalled) with the bundled Django rules — higher-signal than bandit for framework-specific bugs (`.extra()`, `RawSQL`, `|safe`, `csrf_exempt`, `ALLOWED_HOSTS=['*']`): `opengrep scan --config /opt/opengrep-rules/python/django .`
 - **pip-audit** (PyPA) — dependency CVE scanner for known-vuln Django/DRF/simplejwt versions: `pipx install pip-audit && pip-audit -r requirements.txt`
 - **ast-grep** (preinstalled) — quick structural grep for risky calls without a full SAST run: `ast-grep run -p 'mark_safe($X)' -l python`

@@ -15,7 +15,7 @@ Thorough understanding before exploitation. Test every parameter, every endpoint

 **Whitebox (source available)**
 - Map every file, module, and code path in the repository
- Start with broad source-aware triage (`semgrep`, `ast-grep`, `gitleaks`, `trufflehog`, `trivy fs`) and use outputs to drive deep review
+- Start with broad source-aware triage (`opengrep`, `ast-grep`, `gitleaks`, `trufflehog`, `trivy fs`) and use outputs to drive deep review
 - Execute at least one structural AST pass (`sg` and/or Tree-sitter) per repository and store artifacts for reuse
 - Keep AST artifacts bounded and query-driven (target relevant paths/sinks first; avoid whole-repo generic function dumps)
 - Use syntax-aware parsing (Tree-sitter tooling) to improve symbol, route, and sink extraction quality
@@ -1,6 +1,7 @@
 ---
 name: diff
 description: Methodology for diff-scoped review of a pull request, commit, or branch — what counts as in scope, how far to follow a change, and what not to report
+template: jinja
 ---

 # Diff-Scoped Review
@@ -69,9 +70,14 @@ When it is not, the closure rules apply unchanged: a complete
 source → control → sink → impact trace through the changed code is
 reportable at reduced confidence, with the missing runtime proof named in
 `confidence_rationale`. A candidate you can neither confirm nor rule out
+{% if coverage_tools %}
 with a named control is an `open_proof_gap` — record it as
 `needs_follow_up` coverage rather than dropping it because the
 environment was inconvenient.
+{% else %}
+with a named control is an `open_proof_gap` — carry it up as an open
+item rather than dropping it because the environment was inconvenient.
+{% endif %}

 ## Reporting

@@ -80,7 +86,9 @@ plainly which part of the diff introduced or exposed it. A reviewer
 reading your report next to the diff should be able to see the connection
 without re-deriving your analysis.

+{% if coverage_tools %}
 Record coverage per changed component, not per changed file — a
 formatting-only file and a rewritten auth module are not equal rows.
+{% endif %}
 State which changed areas you reviewed and cleared, so the author knows
 what a clean result actually covered.
@@ -15,7 +15,7 @@ Optimize for fast feedback on critical security issues. Skip exhaustive enumerat

 **Whitebox (source available)**
 - Focus on recent changes: git diffs, new commits, modified files—these are most likely to contain fresh bugs
- Run a fast static triage on changed files first (`semgrep`, then targeted `sg` queries)
+- Run a fast static triage on changed files first (`opengrep`, then targeted `sg` queries)
 - Run at least one lightweight AST pass (`sg` or Tree-sitter) so structural mapping is not skipped
 - Keep AST commands tightly scoped to changed or high-risk paths; avoid broad repository-wide pattern dumps
 - Run quick secret and dependency checks (`gitleaks`, `trufflehog`, `trivy fs`) scoped to changed areas when possible
@@ -15,7 +15,7 @@ Systematic testing across the full attack surface. Understand the application be

 **Whitebox (source available)**
 - Map codebase structure: modules, entry points, routing
- Run `semgrep` first-pass triage to prioritize risky flows before deep manual review
+- Run `opengrep` first-pass triage to prioritize risky flows before deep manual review
 - Run at least one AST-structural mapping pass (`sg` and/or Tree-sitter), then use outputs for route, sink, and trust-boundary mapping
 - Keep AST output bounded to relevant paths and hypotheses; avoid whole-repo generic function dumps
 - Identify architecture pattern (MVC, microservices, monolith)
@@ -0,0 +1,69 @@
+---
+name: opengrep
+description: Exact Opengrep CLI structure, bundled offline rules, scoped rule selection, taint mode, and automation-safe output patterns.
+---
+
+# Opengrep CLI Playbook
+
+Official docs:
+- https://github.com/opengrep/opengrep
+- https://github.com/opengrep/opengrep-rules
+
+Canonical syntax:
+`opengrep scan [flags] [targets]`
+
+Bundled rules:
+- `/opt/opengrep-rules` holds the full ruleset, one directory per language (`python`, `javascript`, `typescript`, `go`, `java`, `php`, `ruby`, …) plus `generic/secrets`, `dockerfile`, `terraform`, `yaml`, and `strix` (Strix-authored rules, e.g. GitHub-owned actions not pinned to a commit SHA)
+- Framework rules sit under the language (`python/django`, `python/flask`, `javascript/express`, …)
+
+High-signal flags:
+- `--config <path>` rule file or directory; repeat for several
+- `--taint-intrafile` intra-file inter-procedural taint (follows data across functions in one file)
+- `--json` / `--sarif` output format
+- `--output <file>` write findings to file
+- `--severity <level>` filter by severity (`INFO`, `WARNING`, `ERROR`)
+- `--error` return non-zero exit when findings exist
+- `--quiet` suppress progress noise
+- `--jobs <n>` parallel workers
+- `--timeout <seconds>` per-rule, per-file timeout
+- `--exclude <pattern>` / `--include <pattern>` path filters
+- `--exclude-rule <rule_id>` suppress specific rule
+- `--baseline-commit <sha>` only report findings introduced after baseline
+
+Agent-safe baseline for automation:
+`opengrep scan --config /opt/opengrep-rules --taint-intrafile --json --output opengrep.json --quiet --jobs 4 --timeout 20 /workspace`
+
+Common patterns:
+- Full bundled scan:
+  `opengrep scan --config /opt/opengrep-rules --json --output opengrep.json --quiet /workspace`
+- High-severity focused pass:
+  `opengrep scan --config /opt/opengrep-rules --severity ERROR --json --output opengrep_high.json --quiet /workspace`
+- SARIF output:
+  `opengrep scan --config /opt/opengrep-rules --sarif --output opengrep.sarif --quiet /workspace`
+- Language- or framework-specific rules:
+  `opengrep scan --config /opt/opengrep-rules/python --config /opt/opengrep-rules/generic/secrets --json --output opengrep_python.json --quiet /workspace`
+- Scoped directory scan:
+  `opengrep scan --config /opt/opengrep-rules --json --output opengrep_api.json --quiet /workspace/services/api`
+- Custom rule:
+  `opengrep scan --config ./rules/my-rule.yaml --json --output opengrep_custom.json --quiet /workspace`
+
+Critical correctness rules:
+- Always provide an explicit `--config` from `/opt/opengrep-rules` or a local rule file. Registry shorthands (`p/...`, `auto`) fetch rules from the network; do not use them.
+- Opengrep has no telemetry; there is no `--metrics` flag, and passing one fails the scan.
+- Prefer `--json --output <file>` or `--sarif --output <file>` for machine-readable downstream processing. JSON keeps `paths.scanned` and `results[].path`.
+- Keep the target path explicit; use an absolute or clearly scoped workspace path instead of `.` when possible.
+
+Usage rules:
+- Start with the full `/opt/opengrep-rules` set unless the task clearly calls for a narrower language or framework directory.
+- Add `--taint-intrafile` for injection-class hunting (SQLi, command injection, SSRF, path traversal) in supported languages.
+- Use `--quiet` in automation to reduce noisy logs.
+- Use `--jobs` and `--timeout` explicitly for reproducible runtime behavior.
+- `PartialParsing` errors on templates are normal; they do not invalidate the run.
+
+Failure recovery:
+- If scans are too slow, narrow the target path and the rule directories before changing engine settings.
+- If scans time out, increase `--timeout` modestly or lower `--jobs`.
+- If output is too broad, scope `--config`, add `--severity`, or exclude known irrelevant paths.
+
+If uncertain, query web_search with:
+`site:github.com/opengrep/opengrep <flag>`
@@ -1,72 +0,0 @@
---
-name: semgrep
-description: Exact Semgrep CLI structure, metrics-off scanning, scoped ruleset selection, and automation-safe output patterns.
---
-
-# Semgrep CLI Playbook
-
-Official docs:
- https://semgrep.dev/docs/cli-reference
- https://semgrep.dev/docs/getting-started/cli
- https://semgrep.dev/docs/semgrep-code/semgrep-pro-engine-intro
-
-Canonical syntax:
-`semgrep scan [flags]`
-
-High-signal flags:
- `--config <rule_or_ruleset>` ruleset, registry pack, local rule file, or directory
- `--metrics=off` disable telemetry and metrics reporting
- `--json` JSON output
- `--sarif` SARIF output
- `--output <file>` write findings to file
- `--severity <level>` filter by severity
- `--error` return non-zero exit when findings exist
- `--quiet` suppress progress noise
- `--jobs <n>` parallel workers
- `--timeout <seconds>` per-file timeout
- `--exclude <pattern>` exclude path pattern
- `--include <pattern>` include path pattern
- `--exclude-rule <rule_id>` suppress specific rule
- `--baseline-commit <sha>` only report findings introduced after baseline
- `--pro` enable Pro engine if available
- `--oss-only` force OSS engine only
-
-Agent-safe baseline for automation:
-`semgrep scan --config p/default --metrics=off --json --output semgrep.json --quiet --jobs 4 --timeout 20 /workspace`
-
-Common patterns:
- Default security scan:
-  `semgrep scan --config p/default --metrics=off --json --output semgrep.json --quiet /workspace`
- High-severity focused pass:
-  `semgrep scan --config p/default --severity ERROR --metrics=off --json --output semgrep_high.json --quiet /workspace`
- OWASP-oriented scan:
-  `semgrep scan --config p/owasp-top-ten --metrics=off --sarif --output semgrep.sarif --quiet /workspace`
- Language- or framework-specific rules:
-  `semgrep scan --config p/python --config p/secrets --metrics=off --json --output semgrep_python.json --quiet /workspace`
- Scoped directory scan:
-  `semgrep scan --config p/default --metrics=off --json --output semgrep_api.json --quiet /workspace/services/api`
- Pro engine check or run:
-  `semgrep scan --config p/default --pro --metrics=off --json --output semgrep_pro.json --quiet /workspace`
-
-Critical correctness rules:
- Always include `--metrics=off`; Semgrep sends telemetry by default.
- Always provide an explicit `--config`; do not rely on vague or implied defaults.
- Prefer `--json --output <file>` or `--sarif --output <file>` for machine-readable downstream processing.
- Keep the target path explicit; use an absolute or clearly scoped workspace path instead of `.` when possible.
- If Pro availability matters, check it explicitly with a bounded command before assuming cross-file analysis exists.
-
-Usage rules:
- Start with `p/default` unless the task clearly calls for a narrower pack.
- Add focused packs such as `p/secrets`, `p/python`, or `p/javascript` only when they match the target stack.
- Use `--quiet` in automation to reduce noisy logs.
- Use `--jobs` and `--timeout` explicitly for reproducible runtime behavior.
- Do not use `-h`/`--help` for routine operation unless absolutely necessary.
-
-Failure recovery:
- If scans are too slow, narrow the target path and reduce the active rulesets before changing engine settings.
- If scans time out, increase `--timeout` modestly or lower `--jobs`.
- If output is too broad, scope `--config`, add `--severity`, or exclude known irrelevant paths.
- If Pro mode fails, rerun with `--oss-only` or without `--pro` and note the loss of cross-file coverage.
-
-If uncertain, query web_search with:
-`site:semgrep.dev semgrep <flag> cli`
@@ -1693,6 +1693,7 @@ _VALID_REACHABILITY = frozenset(
    {
        "not_imported",
        "imported",
+        "not_reached",
        "vulnerable_symbol_used",
        "reachable_call_path",
        "unknown",
@@ -1840,6 +1841,9 @@ def _build_dependency_metadata(
 _REACHABILITY_EVIDENCE_LABELS = {
    "not_imported": "not imported by application code",
    "imported": "imported by application code; affected API usage unconfirmed",
+    "not_reached": (
+        "imported by application code; the vulnerable code of this advisory is not reached"
+    ),
    "vulnerable_symbol_used": "the advisory's affected API is used in application code",
    "reachable_call_path": (
        "a call path from application code to the vulnerable function was proven"
@@ -2157,6 +2161,11 @@ async def create_dependency_report(
      can evade static search).
    - ``imported`` — application code imports the package, but usage of
      the advisory's affected API was not confirmed.
+    - ``not_reached`` — application code imports the package, and the
+      analysis showed that the vulnerable code of this advisory does not
+      run here (the affected API has no caller, the only call site is
+      behind a condition that is false in this codebase, the parent
+      package never reaches it, ...). Per advisory, never per package.
    - ``vulnerable_symbol_used`` — the advisory's affected
      function/class/API appears in application code.
    - ``reachable_call_path`` — a call-graph tool (e.g. ``govulncheck``)
@@ -2210,8 +2219,9 @@ async def create_dependency_report(
            repository root. This binds the finding to its exact file so
            remediation can target the right repository.
        reachability: Usage-evidence level from static analysis — one of
-            ``not_imported`` / ``imported`` / ``vulnerable_symbol_used`` /
-            ``reachable_call_path`` / ``unknown``. Claim only what the
+            ``not_imported`` / ``imported`` / ``not_reached`` /
+            ``vulnerable_symbol_used`` / ``reachable_call_path`` /
+            ``unknown``. Claim only what the
            evidence proves; when in doubt use ``unknown``.
        reachability_evidence: **Required.** The concrete proof for the
            claimed level, or, for ``unknown``, what you searched and why
@@ -104,6 +104,10 @@ def test_report_tool_replaces_create_vulnerability_report() -> None:
        assert "create_vulnerability_report" not in agent.instructions


+def _descriptions(tools: list[Any]) -> list[str]:
+    return [t.description for t in tools if isinstance(t, FunctionTool)]
+
+
 def test_coverage_tools_can_be_turned_off() -> None:
    coverage = {"record_coverage", "update_coverage", "list_coverage"}
    for is_root in (True, False):
@@ -116,6 +120,22 @@ def test_coverage_tools_can_be_turned_off() -> None:
        assert "- COVERAGE" in on.instructions
        assert "## Recording Closure" in on.instructions
        assert not any(name in off.instructions for name in coverage)
+        on_descriptions = "\n".join(_descriptions(on.tools))
+        off_descriptions = "\n".join(_descriptions(off.tools))
+        if is_root:
+            assert "Coverage reconciliation" in on_descriptions
+            assert "Coverage reconciliation" not in off_descriptions
+        assert not any(name in off_descriptions for name in coverage)
+
+    diff_on = factory.build_strix_agent(is_root=True, is_diff_scoped=True)
+    diff_off = factory.build_strix_agent(is_root=True, is_diff_scoped=True, coverage_tools=False)
+    assert isinstance(diff_on.instructions, str)
+    assert isinstance(diff_off.instructions, str)
+    assert "Record coverage per changed component" in diff_on.instructions
+    assert "`needs_follow_up` coverage" in diff_on.instructions
+    assert "Record coverage per changed component" not in diff_off.instructions
+    assert "`needs_follow_up` coverage" not in diff_off.instructions
+    assert not any(name in diff_off.instructions for name in coverage)


 def test_instructions_override_is_used_verbatim() -> None:
@@ -0,0 +1,159 @@
+"""Only validated model decisions may suppress a finding."""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING, Any
+from unittest.mock import AsyncMock
+
+import pytest
+from agents.items import ModelResponse
+from agents.usage import Usage
+from openai.types.responses import ResponseOutputMessage, ResponseOutputText
+
+from strix.config import loader
+from strix.report import dedupe
+from strix.report.state import ReportState, get_global_report_state, set_global_report_state
+from strix.tools.reporting.tool import _do_create
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+    from pathlib import Path
+
+
+@pytest.fixture
+def report_state(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> Iterator[ReportState]:
+    monkeypatch.chdir(tmp_path)
+    previous = get_global_report_state()
+    state = ReportState(run_name="dedupe-decisions")
+    set_global_report_state(state)
+    state.add_vulnerability_report(title="Existing finding", severity="low")
+    try:
+        yield state
+    finally:
+        set_global_report_state(previous)
+
+
+def _mock_judge(monkeypatch: pytest.MonkeyPatch, decision: dict[str, Any]) -> AsyncMock:
+    settings = loader.load_settings().model_copy(deep=True)
+    settings.llm.model = "openai/synthetic-model"
+    model = AsyncMock()
+    model.get_response.return_value = ModelResponse(
+        output=[
+            ResponseOutputMessage(
+                id="msg_synthetic",
+                type="message",
+                role="assistant",
+                status="completed",
+                content=[
+                    ResponseOutputText(
+                        type="output_text",
+                        annotations=[],
+                        text=json.dumps({"confidence": 0.95, "reason": "Synthetic", **decision}),
+                    )
+                ],
+            )
+        ],
+        usage=Usage(),
+        response_id="synthetic",
+    )
+    monkeypatch.setattr(dedupe, "load_settings", lambda: settings)
+    monkeypatch.setattr(dedupe, "configure_sdk_model_defaults", lambda _: None)
+    monkeypatch.setattr(dedupe, "resolve_dedupe_model", lambda *_: model)
+    return model
+
+
+async def _file_finding() -> dict[str, Any]:
+    return await _do_create(
+        title="New finding",
+        description="The endpoint reflects unencoded input.",
+        impact="The reflected script executes in a victim's browser.",
+        target="https://example.invalid",
+        technical_analysis="Input is interpolated into HTML without encoding.",
+        poc_description="Open the endpoint with the reflected parameter.",
+        poc_script_code="print('synthetic reproduction')",
+        remediation_steps="Encode output for its HTML context.",
+        evidence="The synthetic response contains the unencoded parameter.",
+        assumptions="A victim follows the crafted link.",
+        counterevidence="No encoding or content security policy is present.",
+        confidence="high",
+        severity_change_conditions="A strict content security policy would lower the impact.",
+        fix_effort="low",
+        cvss_breakdown={
+            "attack_vector": "N",
+            "attack_complexity": "L",
+            "privileges_required": "N",
+            "user_interaction": "R",
+            "scope": "U",
+            "confidentiality": "L",
+            "integrity": "L",
+            "availability": "N",
+        },
+        endpoint="/search",
+        method="GET",
+        cve=None,
+        cwe="CWE-79",
+        code_locations=None,
+    )
+
+
+@pytest.mark.parametrize("value", ["false", "true", 0, 1, None, [], {}])
+def test_dedupe_parser_requires_a_boolean(value: Any) -> None:
+    with pytest.raises(TypeError, match="is_duplicate"):
+        dedupe._parse_dedupe_response(json.dumps({"is_duplicate": value}))
+
+
+@pytest.mark.parametrize(
+    "decision",
+    [
+        {"is_duplicate": "false", "duplicate_id": "vuln-0001"},
+        {"is_duplicate": "true", "duplicate_id": "vuln-0001"},
+        {"is_duplicate": 1, "duplicate_id": "vuln-0001"},
+        {"is_duplicate": True, "duplicate_id": "vuln-9999"},
+        {"is_duplicate": True, "duplicate_id": ""},
+        {"is_duplicate": True, "duplicate_id": None},
+        {"is_duplicate": True, "duplicate_id": ["vuln-0001"]},
+        {"is_duplicate": True},
+    ],
+)
+async def test_invalid_dedupe_decision_does_not_suppress_finding(
+    report_state: ReportState, monkeypatch: pytest.MonkeyPatch, decision: dict[str, Any]
+) -> None:
+    model = _mock_judge(monkeypatch, decision)
+
+    result = await _file_finding()
+
+    assert result["success"] is True
+    model.get_response.assert_awaited_once()
+    saved = json.loads((report_state.get_run_dir() / "vulnerabilities.json").read_text())
+    assert [report["title"] for report in saved] == ["Existing finding", "New finding"]
+    assert (report_state.get_run_dir() / "vulnerabilities" / "vuln-0002.md").is_file()
+
+
+@pytest.mark.parametrize("is_duplicate", [False, True])
+async def test_valid_dedupe_decisions_keep_their_meaning(
+    report_state: ReportState, monkeypatch: pytest.MonkeyPatch, is_duplicate: bool
+) -> None:
+    # An extraneous ID must not turn a valid false decision into a duplicate.
+    _mock_judge(monkeypatch, {"is_duplicate": is_duplicate, "duplicate_id": "vuln-0001"})
+
+    result = await _file_finding()
+
+    assert result["success"] is not is_duplicate
+    saved = json.loads((report_state.get_run_dir() / "vulnerabilities.json").read_text())
+    assert len(saved) == (1 if is_duplicate else 2)
+    if is_duplicate:
+        assert result["duplicate_of"] == "vuln-0001"
+
+
+async def test_dedupe_id_is_matched_without_truncation(monkeypatch: pytest.MonkeyPatch) -> None:
+    existing_id = "x" * 64
+    _mock_judge(monkeypatch, {"is_duplicate": True, "duplicate_id": existing_id + "-unknown"})
+
+    result = await dedupe.check_duplicate(
+        {"title": "New finding"}, [{"id": existing_id, "title": "Existing finding"}]
+    )
+
+    assert result["is_duplicate"] is False
+    assert "error" in result
@@ -0,0 +1,32 @@
+"""``STRIX_DOCKER_SANDBOX_DEVICES`` passes host devices through to the sandbox container."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from strix.runtime import docker_client
+
+
+if TYPE_CHECKING:
+    import pytest
+
+
+def test_unset_adds_no_devices(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv(docker_client._SANDBOX_DEVICES_ENV, raising=False)
+    kwargs: dict[str, object] = {}
+    docker_client._apply_sandbox_devices(kwargs)
+    assert kwargs == {}
+
+
+def test_devices_are_mapped_to_the_same_path_rwm(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv(docker_client._SANDBOX_DEVICES_ENV, " /dev/net/tun, /dev/kvm ,,")
+    kwargs: dict[str, object] = {}
+    docker_client._apply_sandbox_devices(kwargs)
+    assert kwargs["devices"] == ["/dev/net/tun:/dev/net/tun:rwm", "/dev/kvm:/dev/kvm:rwm"]
+
+
+def test_existing_devices_are_kept_and_not_duplicated(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv(docker_client._SANDBOX_DEVICES_ENV, "/dev/fuse,/dev/net/tun")
+    kwargs: dict[str, object] = {"devices": ["/dev/fuse"]}
+    docker_client._apply_sandbox_devices(kwargs)
+    assert kwargs["devices"] == ["/dev/fuse", "/dev/net/tun:/dev/net/tun:rwm"]
@@ -2,11 +2,14 @@

 from __future__ import annotations

-from typing import Any
+from types import SimpleNamespace
+from typing import TYPE_CHECKING, Any
 from unittest.mock import MagicMock, patch

 import pytest
+from agents.usage import Usage

+from strix.config import loader
 from strix.core.agents import AgentCoordinator
 from strix.core.hooks import (
    BudgetExceededError,
@@ -15,6 +18,11 @@ from strix.core.hooks import (
    SubagentBudgetReservedError,
    recomputed_budget_flags,
 )
+from strix.report.usage import LLMUsageLedger
+
+
+if TYPE_CHECKING:
+    from pathlib import Path


 def _make_hooks(max_budget: float | None) -> ReportUsageHooks:
@@ -85,6 +93,38 @@ async def test_over_budget_raises() -> None:
        await hooks.on_llm_end(_make_context(), MagicMock(), MagicMock())


+@pytest.mark.asyncio
+async def test_manual_rates_make_unpriced_model_trip_budget(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    monkeypatch.setenv("STRIX_LLM", "test-model")
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "10")
+    monkeypatch.delenv("STRIX_LLM_OUTPUT_COST_PER_MTOK", raising=False)
+    monkeypatch.delenv("STRIX_LLM_CACHED_INPUT_COST_PER_MTOK", raising=False)
+    monkeypatch.setattr(loader, "_override", tmp_path / "missing.json")
+    monkeypatch.setattr(loader, "_cached", None)
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = 1000
+    usage.total_tokens = 1000
+    ledger = LLMUsageLedger()
+
+    class ReportState:
+        def record_sdk_usage(self, **kwargs: Any) -> None:
+            ledger.record(**kwargs)
+
+        def get_total_llm_cost(self) -> float:
+            return ledger.total_cost
+
+    hooks = _make_hooks(0.005)
+    response: Any = SimpleNamespace(usage=usage)
+    with (
+        patch("strix.core.hooks.get_global_report_state", return_value=ReportState()),
+        pytest.raises(BudgetExceededError),
+    ):
+        await hooks.on_llm_end(_make_context(), MagicMock(), response)
+
+
@pytest.mark.asyncio
 async def test_budget_check_uses_live_cost_accessor() -> None:
    # The check must read the live ledger, not the persisted run-record snapshot,
@@ -314,6 +314,12 @@ def test_make_model_settings_disables_parallel_tool_calls_by_default() -> None:
    assert make_model_settings("none", model_name="gpt-4o").parallel_tool_calls is False


+def test_make_model_settings_enables_parallel_tool_calls_when_requested() -> None:
+    settings = make_model_settings("none", model_name="gpt-4o", parallel_tool_calls=True)
+
+    assert settings.parallel_tool_calls is True
+
+
 def test_make_model_settings_omits_parallel_tool_calls_without_tools() -> None:
    settings = make_model_settings("none", model_name="gpt-4o", has_tools=False)

@@ -468,6 +474,11 @@ def test_openrouter_attribution_rides_on_the_request_headers() -> None:
    }


+def test_vercel_ai_gateway_attribution_rides_on_the_request_headers() -> None:
+    headers = make_model_settings(None, model_name="vercel_ai_gateway/zai/glm-5.3").extra_headers
+    assert headers == {"HTTP-Referer": "https://strix.ai", "X-Title": "Strix"}
+
+
 def test_openrouter_attribution_absent_for_other_providers() -> None:
    assert make_model_settings(None, model_name="anthropic/claude-sonnet-4-5").extra_headers is None

@@ -1,14 +1,50 @@
 from __future__ import annotations

+import json
+from typing import TYPE_CHECKING
 from unittest.mock import patch

 import litellm
+import pytest
 from agents.usage import Usage
+from pydantic import ValidationError

+from strix.config import loader
+from strix.config.settings import LlmSettings
 from strix.report.pricing import resolve_litellm_model
 from strix.report.usage import LLMUsageLedger


+if TYPE_CHECKING:
+    from pathlib import Path
+
+
+PRICE_ENV = (
+    "STRIX_LLM_INPUT_COST_PER_MTOK",
+    "STRIX_LLM_OUTPUT_COST_PER_MTOK",
+    "STRIX_LLM_CACHED_INPUT_COST_PER_MTOK",
+)
+
+
+@pytest.fixture(autouse=True)
+def _isolated_settings(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    for name in PRICE_ENV:
+        monkeypatch.delenv(name, raising=False)
+    monkeypatch.setenv("STRIX_LLM", "gateway/unpriced-model")
+    monkeypatch.setattr(loader, "_override", tmp_path / "missing.json")
+    monkeypatch.setattr(loader, "_cached", None)
+
+
+def _usage(input_tokens: int, output_tokens: int = 0, cached_tokens: int = 0) -> Usage:
+    usage = Usage()
+    usage.requests = 1
+    usage.input_tokens = input_tokens
+    usage.output_tokens = output_tokens
+    usage.total_tokens = input_tokens + output_tokens
+    usage.input_tokens_details.cached_tokens = cached_tokens
+    return usage
+
+
 def test_resolves_common_bare_model_names() -> None:
    resolve_litellm_model.cache_clear()
    assert resolve_litellm_model("deepseek-v4-flash") == "deepseek/deepseek-v4-flash"
@@ -60,6 +96,112 @@ def test_ledger_prefers_observed_cost_over_estimate() -> None:
    assert ledger.total_cost == 0.17


+def test_unpriced_model_without_manual_rates_costs_zero() -> None:
+    ledger = LLMUsageLedger()
+
+    ledger.record(agent_id="a", usage=_usage(1000, 200), model="gateway/unpriced-model")
+
+    assert ledger.total_cost == 0.0
+
+
+def test_manual_rates_price_usage_and_override_observed_cost(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "2")
+    monkeypatch.setenv("STRIX_LLM_OUTPUT_COST_PER_MTOK", "8")
+    ledger = LLMUsageLedger()
+
+    ledger.record(agent_id="a", usage=_usage(1000, 200), model="gateway/unpriced-model")
+    ledger.record_observed_cost(0.17)
+
+    assert ledger.total_cost == 0.0036
+    assert ledger.to_record()["agents"][0]["cost"] == 0.0036
+
+
+def test_manual_rates_from_config_file(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    path = tmp_path / "config.json"
+    path.write_text(
+        json.dumps(
+            {
+                "env": {
+                    "STRIX_LLM_INPUT_COST_PER_MTOK": "2",
+                    "STRIX_LLM_OUTPUT_COST_PER_MTOK": "8",
+                }
+            }
+        ),
+        encoding="utf-8",
+    )
+    monkeypatch.setattr(loader, "_override", path)
+    ledger = LLMUsageLedger()
+
+    ledger.record(agent_id="a", usage=_usage(1000, 200), model="gateway/unpriced-model")
+
+    assert ledger.total_cost == 0.0036
+
+
+def test_manual_rates_only_price_the_configured_model(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "2")
+    monkeypatch.setenv("STRIX_LLM_OUTPUT_COST_PER_MTOK", "8")
+    ledger = LLMUsageLedger()
+
+    ledger.record(agent_id="a", usage=_usage(1000, 200), model="gateway/unpriced-model")
+    with patch("litellm.completion_cost", return_value=0.42):
+        ledger.record(agent_id="dedupe", usage=_usage(500, 50), model="deepseek/deepseek-v4-flash")
+    record = ledger.to_record()
+
+    assert ledger.total_cost == 0.4236
+    assert {agent["agent_id"]: agent["cost"] for agent in record["agents"]} == {
+        "a": 0.0036,
+        "dedupe": 0.42,
+    }
+
+
+def test_cached_tokens_fall_back_to_input_rate(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "5")
+    monkeypatch.setenv("STRIX_LLM_OUTPUT_COST_PER_MTOK", "7")
+    ledger = LLMUsageLedger()
+
+    ledger.record(
+        agent_id="a", usage=_usage(10, 1, cached_tokens=2), model="gateway/unpriced-model"
+    )
+
+    assert ledger.total_cost == 0.000057
+
+
+def test_hydrated_usage_uses_configured_rates_not_persisted_cost(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "2")
+    monkeypatch.setenv("STRIX_LLM_OUTPUT_COST_PER_MTOK", "8")
+    original = LLMUsageLedger()
+    original.record(agent_id="a", usage=_usage(1000, 200), model="gateway/unpriced-model")
+    persisted = original.to_record()
+    persisted["cost"] = 42.0
+    persisted["agents"][0]["cost"] = 42.0
+    hydrated = LLMUsageLedger()
+
+    hydrated.hydrate(persisted)
+
+    assert hydrated.total_cost == 0.0036
+
+
+@pytest.mark.parametrize("value", ["invalid", "-1", "nan", "inf"])
+def test_invalid_manual_rate_is_rejected(value: str, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", value)
+    with pytest.raises(ValidationError):
+        LlmSettings()
+
+
+def test_zero_cost_overrides_manual_rates(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("STRIX_LLM_INPUT_COST_PER_MTOK", "10")
+    ledger = LLMUsageLedger()
+    ledger.zero_cost = True
+
+    ledger.record(agent_id="a", usage=_usage(1000), model="gateway/unpriced-model")
+
+    assert ledger.total_cost == 0.0
+
+
 def test_hydrated_estimate_continues_accumulating_new_estimates() -> None:
    usage = Usage()
    usage.requests = 1
@@ -668,6 +668,39 @@ async def test_dependency_report_records_reachability(report_state: ReportState)
    assert report["severity"] == "high"


+async def test_dependency_report_records_not_reached(report_state: ReportState) -> None:
+    result = await _do_create_dependency(
+        title="CVE-2021-23337 in lodash 4.17.20",
+        description="Command injection via template.",
+        target="repo/package.json",
+        cve="CVE-2021-23337",
+        package_name="lodash",
+        installed_version="4.17.20",
+        impact="Command injection where template is used.",
+        remediation_steps="Upgrade to 4.17.21.",
+        assumptions="Static search only; no dynamic dispatch into lodash was found.",
+        package_ecosystem="npm",
+        manifest_path="package-lock.json",
+        fixed_version="4.17.21",
+        cwe=None,
+        advisory_cvss=7.2,
+        technical_analysis=None,
+        fix_effort="low",
+        reachability="not_reached",
+        reachability_evidence=(
+            "src/util/strings.ts:3 imports lodash; a complete search of src/ finds no "
+            "call to _.template, the only API the advisory affects."
+        ),
+        contextual_cvss_breakdown=_DEP_CONTEXT,
+        contextual_cvss_reasoning=_DEP_REASONING,
+    )
+
+    assert result["success"] is True
+    report = report_state.vulnerability_reports[0]
+    assert report["dependency_metadata"]["reachability"] == "not_reached"
+    assert "the vulnerable code of this advisory is not reached" in report["evidence"]
+
+
 async def test_dependency_report_rejects_reachability_without_evidence(
    report_state: ReportState,
 ) -> None:
@@ -25,6 +25,7 @@ def _wire_runner(monkeypatch: pytest.MonkeyPatch, tmp_path: Any) -> None:
            model="openai/gpt-4o",
            reasoning_effort="high",
            force_required_tool_choice=False,
+            parallel_tool_calls=False,
            timeout=300,
            prompt_cache=True,
            extra_headers=None,
@@ -41,6 +41,7 @@ def _settings() -> Any:
            model="openai/gpt-4o",
            reasoning_effort="high",
            force_required_tool_choice=False,
+            parallel_tool_calls=False,
            timeout=300,
            prompt_cache=True,
            extra_headers=None,
@@ -39,6 +39,7 @@ async def test_persistent_rate_limit_stops_gracefully(
            model="openai/gpt-4o",
            reasoning_effort="high",
            force_required_tool_choice=False,
+            parallel_tool_calls=False,
            timeout=300,
            prompt_cache=True,
            extra_headers=None,
@@ -61,12 +61,14 @@ def _patch_engine_scaffold(
            model="openai/gpt-4o",
            reasoning_effort="high",
            force_required_tool_choice=False,
+            parallel_tool_calls=False,
            timeout=300,
            prompt_cache=True,
            extra_headers=None,
            extra_body=None,
        ),
        runtime=types.SimpleNamespace(max_context_images=3),
+        telemetry=types.SimpleNamespace(run_record_timings=False),
    )
    monkeypatch.setattr(runner, "load_settings", lambda: settings)
    monkeypatch.setattr(runner, "configure_sdk_model_defaults", lambda _settings: None)
@@ -160,6 +162,36 @@ async def test_root_prompt_options_flow_into_root_agent(
    }


+@pytest.mark.asyncio
+async def test_replace_root_scan_prompt_drops_the_scan_prompt(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Any,
+) -> None:
+    scope_context = {
+        "scope_source": "user_scan_config",
+        "authorized_targets": [
+            {"type": "web_application", "value": "https://example.com", "workspace_path": ""},
+        ],
+    }
+    captured = _patch_engine_scaffold(monkeypatch, tmp_path, scope_context)
+
+    await runner.run_strix_scan(
+        scan_config={"targets": [], "scan_mode": "deep"},
+        scan_id="scan-replace",
+        image="img",
+        coordinator=AgentCoordinator(),
+        root_instructions_override="CUSTOM SCAN PROMPT",
+        replace_root_scan_prompt=True,
+    )
+
+    instructions_override = captured["kwargs"]["instructions_override"]
+    assert instructions_override.startswith("CUSTOM SCAN PROMPT")
+    assert instructions_override.count("SCOPE:") == 1
+    assert "https://example.com" in instructions_override
+    assert "<root_scan_instructions_override>" not in instructions_override
+    assert "application security validation agent" not in instructions_override
+
+
@pytest.mark.asyncio
 async def test_extra_system_prompt_context_cannot_override_scope_context(
    monkeypatch: pytest.MonkeyPatch,
@@ -285,6 +317,19 @@ async def test_unknown_tool_calls_are_returned_to_the_model(
    assert captured["run_config"].tool_not_found_behavior == "return_error_to_model"


+@pytest.mark.parametrize("interactive", [False, True])
+def test_parallel_tool_calls_prompt_is_opt_in(interactive: bool) -> None:
+    default = render_system_prompt(include_scope=False, interactive=interactive)
+    parallel = render_system_prompt(
+        include_scope=False, interactive=interactive, parallel_tool_calls=True
+    )
+
+    assert "Read-only calls can go together" not in default
+    assert ("Read-only calls can go together" in parallel) is not interactive
+    assert ("exactly one tool call" in default) is interactive
+    assert "exactly one tool call" not in parallel
+
+
 def test_scope_is_rendered_once_at_the_end_of_the_prompt() -> None:
    prompt = render_system_prompt(
        system_prompt_context={
@@ -49,6 +49,7 @@ def _settings() -> Any:
            model="openai/gpt-4o",
            reasoning_effort="high",
            force_required_tool_choice=False,
+            parallel_tool_calls=False,
            timeout=300,
            prompt_cache=True,
            extra_headers=None,
Author	SHA1	Message	Date
yoni	ba5cab24c2	docs(skill): plain wording for the unclear-chain case	2026-11-08 19:36:51 +00:00
yoni	0b78002686	fix(skill): walk every introducing chain before not_reached For a transitive package the caller of the affected API is the package one hop above it on each chain (body-parser for express > body-parser > qs), not the direct dependency. Inspect that package on every chain; any chain left unclear keeps the finding at imported.	2026-11-08 19:27:15 +00:00
yoni	7c490203ab	feat(dependency): add the not_reached usage level A dependency CVE can now be filed with reachability=not_reached: the package is imported, but the usage analysis showed that the vulnerable code of this advisory does not run in the codebase. It sits between imported and not_imported on the evidence ladder and is decided per advisory, with the check that showed it in reachability_evidence. The dependency-cve-scanning skill explains when the level applies (govulncheck finds no path, the parent of a transitive package never references the affected API, the only call site is behind a condition that is false here, build-only tooling) and keeps a partial or skipped search at imported.	2026-11-06 12:20:53 +00:00
Samir Hanna Verza	09536cf148	fix(report): validate deduplication decisions	2026-11-04 21:57:34 +02:00
alex s	cb581bf93d	Feat/str 1646 opengrep (#1625 ) * feat(sandbox): replace Semgrep with Opengrep and bundle offline rules * fix(sandbox): keep opengrep-rules LICENSE and drop proprietary-licensed rules * feat(sandbox): add Strix rule for GitHub-owned actions not pinned to a commit SHA * chore: bump sandbox image to 1.4.0 (opengrep) * fix(sandbox): resolve the gitleaks release tag without the GitHub API * fix(sandbox): install a pinned, sha256-verified trivy release	2026-11-04 10:26:08 -05:00
Ahmed Allam	6b7ae2ebe4	fix(STR-1654): read manual LLM prices from settings and apply them to STRIX_LLM only	2026-10-29 04:30:48 +03:00
Alex Schapiro	92b30bcad5	fix(STR-1654): price LLM usage from model config	2026-10-29 04:30:48 +03:00
alex s	dfdbe4b2ab	fix(image): patch brotli decoder (CVE-2026-111497) in sandbox, bump tag 1.3.0 -> 1.3.1 (#1617 ) kali-rolling still ships libbrotli 1.2.0-4; node, curl and python3 in the sandbox all load the shared libbrotlidec, so a malicious Content-Encoding: br response would hit the vulnerable decoder. Install Debian's fixed 1.2.0-4.1 packages over Kali's and fail the build if the version is not applied.	2026-10-28 19:58:04 -04:00
Ahmed Allam	98e3df1425	feat(llm): send app attribution headers to Vercel AI Gateway	2026-10-28 06:04:41 +03:00
Thibault Jaigu	ffa35e22d3	docs(config): describe the current STRIX_API_TYPE default Since `99c0711` the API route follows the model's catalog entry, not LLM_API_BASE, so the doc saying a custom base URL defaults to chat completions was out of date.	2026-10-27 22:24:15 +03:00
Ian	ef5d810047	refactor(runner): default replace_scan_prompt to False in _compose_root_instructions_override	2026-10-26 22:56:57 +03:00
ian-at-strix	d6f4bc5ced	feat(runner): let root_instructions_override replace the root's scan prompt (#1592 )	2026-10-26 15:49:51 -04:00
Ahmed Allam	4ac5dc65e4	docs: show Vercel AI Gateway and OpenRouter in the README setup	2026-10-26 19:44:33 +03:00
Ahmed Allam	4bf353fb7b	docs: use the vercel_ai_gateway provider for the Vercel AI Gateway setup	2026-10-26 18:58:20 +03:00
Ahmed Allam	f121ab03fb	docs: update the recommended models in the repo markdown	2026-10-26 18:48:14 +03:00
Ahmed Allam	2c9e9f3444	docs: recommend Vercel AI Gateway alongside OpenRouter and update model ids Quickstart, providers overview and docs index show GLM-5.3 through Vercel AI Gateway and OpenRouter side by side, Vercel first. Model ids across the provider pages move to the current releases, including DeepSeek V4.1 Flash 1017, and the OpenAI, Anthropic and Gemini pages note the Daybreak, CVP and Fairwind access programs. Docs only, README unchanged. Closes STR-1686	2026-10-26 18:48:14 +03:00
Ahmed Allam	200a501f15	feat(runtime): pass host devices from STRIX_DOCKER_SANDBOX_DEVICES through to the sandbox container	2026-10-25 01:59:21 +03:00
Ahmed Allam	dc40345e26	test(agents): check the diff skill's own coverage lines, not the word	2026-10-24 21:02:27 +03:00
Ahmed Allam	fa3539cb75	fix(skills): the diff skill drops its coverage ledger instructions when the coverage tools are off	2026-10-24 21:02:27 +03:00
Ahmed Allam	aaf1f8c823	fix(agents): finish_scan drops its coverage reconciliation step when the coverage tools are off	2026-10-24 21:02:27 +03:00
Ian	bac386e509	refactor(skills): put the coverage conditionals on their own lines	2026-10-24 21:02:27 +03:00
Ian	898bbb4741	feat(agents): drop the coverage sections from skills and tool descriptions when the tools are off	2026-10-24 21:02:27 +03:00
Ian	5418c767a2	feat(agents): let callers turn off the coverage ledger tools	2026-10-24 21:02:27 +03:00
Ian	fff22045ae	refactor(prompts): put the parallel-tool-calls conditionals on their own lines	2026-10-24 20:32:27 +03:00
Ian	dca9482c82	feat(core): opt-in parallel tool calls via STRIX_PARALLEL_TOOL_CALLS	2026-10-24 20:32:27 +03:00
Ian	f46bed6740	refactor(hooks): name the soft turn-limit warning after the soft limit	2026-10-24 20:19:38 +03:00
Ian	b8d586a39b	fix(agents): an agent with several limited skills gets the largest turn limit	2026-10-24 20:19:38 +03:00
Ian	b17c0275a9	feat(agents): soft per-skill turn limits for sub-agents run_strix_scan(subagent_turn_limits={skill: turns}) gives each sub-agent the smallest limit among its skills. The agent is warned 10 turns before the limit and reminded every 5 turns past it, but never stopped.	2026-10-24 20:19:38 +03:00