deepagents/libs/code/THREAT_MODEL.md

Threat Model: deepagents-code

Note: deepagents-code was forked from deepagents-cli at v0.1.0. References to "the CLI" throughout this document describe the deepagents-code runtime.

Generated: 2026-03-28 | Commit: e859077f | Scope: libs/code only

Disclaimer: This threat model is automatically generated to help developers and security researchers understand where trust is placed in this system and where boundaries exist. It is experimental, subject to change, and not an authoritative security reference — findings should be validated before acting on them. The analysis may be incomplete or contain inaccuracies. We welcome suggestions and corrections to improve this document.

Scope

In Scope

• deepagents_code/ — all Python source modules shipped as the deepagents-code package
• CLI entry point (main.py, __init__.py)
• Interactive TUI (app.py, tui/textual_adapter.py, tui/widgets/)
• Non-interactive pipeline runner (client/non_interactive.py)
• Agent creation (agent.py)
• Built-in tools (tools.py: http_request, web_search, fetch_url)
• MCP config loader and per-server allow/deny lists (mcp_tools.py, model_config.py)
• Hook dispatcher (hooks.py)
• Sandbox integration factory (integrations/sandbox_factory.py, integrations/sandbox_provider.py)
• Session persistence (sessions.py)
• Configuration system (config.py, model_config.py)
• Unicode/URL safety helpers (unicode_security.py)
• LangGraph dev server subprocess management (client/launch/server.py, client/launch/server_manager.py, server_graph.py)
• Remote agent client (client/remote_client.py)
• Local context middleware (local_context.py)
• Custom subagent loader (subagents.py, agent.py:load_async_subagents)
• Conversation offload (offload.py)
• Skill management (skills/commands.py)

Out of Scope

• libs/deepagents/ (SDK library) — separate package with its own threat model
• libs/acp/, libs/evals/, libs/partners/ — separate packages
• tests/ — not shipped code; used during analysis only
• scripts/, examples/ — developer tooling, not shipped
• Deployment infrastructure, CI/CD pipelines
• LLM provider behavior (model outputs, jailbreaks) — user-controlled
• Sandbox provider internals (Daytona, LangSmith, Modal, Runloop, AgentCore) — third-party
• LangGraph server internals — consumed as a subprocess dependency

Assumptions

1. The CLI runs locally on the user's machine; the user is a developer who invoked deepagents themselves.
2. The project provides the HITL approval framework; users control model selection, API keys, and whether to disable approval gates.
3. ~/.deepagents/ is only writable by the authenticated local user — no multi-user shared home directories.
4. Sandbox backends are trusted third-party services. CLI responsibility ends at correctly constructing and dispatching requests to them.
5. LangSmith tracing, if enabled, is user-opted-in via environment variables.
6. The LangGraph dev server subprocess binds to 127.0.0.1 by default (client/launch/server.py:_DEFAULT_HOST) and is ephemeral — started and stopped per CLI session.
7. DA_SERVER_* environment variables are readable only by the CLI process and its child server subprocess (OS process isolation assumption).
8. Users who set class_path in config.toml accept the same trust model as pyproject.toml build scripts — they control their own machine.

---

System Overview

deepagents-code is a terminal-based AI coding assistant. It wraps the deepagents SDK in an interactive TUI (Textual) and a headless non-interactive mode. Both modes route agent execution through a local langgraph dev subprocess: the CLI spawns a server, passes configuration via DA_SERVER_* environment variables, and communicates via a RemoteAgent HTTP+SSE client. The agent receives user prompts, reasons with a configurable LLM, and executes side-effecting tools (file read/write, shell commands, web search, HTTP requests) subject to a human-in-the-loop (HITL) approval gate. Sessions are persisted in a local SQLite checkpoint database. Users can extend the agent with MCP servers (stdio processes or remote HTTP/SSE endpoints), hooks (event-driven subprocesses), custom subagents (AGENTS.md files in .deepagents/agents/), async remote subagents (LangGraph deployments configured in config.toml), and pluggable sandbox backends for remote code execution.

Architecture Diagram

┌──────────────────────────────────────────────────────────────────────┐
│                        User (local machine)                          │
│                                                                      │
│  CLI Args / Env Vars ──► C1: CLI Entry Point (main.py)               │
│                                  │                                   │
│                 ┌────────────────┼─────────────────────┐             │
│                 ▼                ▼                     ▼             │
│        C2: TUI (app.py)  C2b: Non-interactive   C13: Config Channel  │
│                 │      (client/non_interactive.py) (DA_SERVER_* vars) │
│ - - - - - - - - - - TB8: CLI / Server IPC - - - - - │ - - - - - - - │
│                 │                                     ▼              │
│                 │        C11: Server Manager (client/launch)          │
│                 │                      │ spawns                      │
│                 │                      ▼                             │
│                 │           C11b: LangGraph Dev Server               │
│                 │     (client/launch/server.py:ServerProcess)        │
│                 │           LANGGRAPH_AUTH_TYPE=noop                 │
│ - - - - - - - - │ - - - - - TB10: RemoteAgent / Dev Server - - - -  │
│                 │                      ▲                             │
│                 └─►C12: RemoteAgent────┘ (HTTP+SSE on 127.0.0.1)    │
│                     (client/remote_client.py)                        │
│                            │                                         │
│                     C3: Agent Engine (server_graph.py)               │
│                     (create_cli_agent, deepagents SDK)               │
│                            │                                         │
│  User Prompt ──────────────┘                                         │
│                            │                                         │
│ - - - - - - - - - TB1: User→Agent Input - - - - - - - - - - - - -   │
│                            │                                         │
│                     LLM Decision                                     │
│ - - - - - - - - - TB2: LLM → Tool Execution (HITL) - - - - - - - -  │
│                            │                                         │
│   ┌──────────┬─────────┬───┴────────┬──────────┬──────────────┐     │
│   ▼          ▼         ▼            ▼          ▼              ▼     │
│  C4:Tools  C5:MCP   C6:Hooks     C8:Sessions C7:Sandbox  C14:Async  │
│  (file,    (procs/  (subprocs    (SQLite)    (Daytona/   Subagents   │
│  shell,    remote)  hooks.json)             Modal/etc.) (LangGraph   │
│  HTTP)                                                   remotes)   │
│   │          │                     │          │              │       │
│ - │ - - - - -│- - - - TB3 - - - - -│- - - - - │ - - - - - - -│ - -  │
│   │          ▼                     ▼          ▼              │       │
│   │      External               Local FS   Remote     External LG   │
│   ▼      MCP Server            (~/.deep   Sandbox    Deployment     │
│ External  (proc/net)           agents/)   API                       │
│ Web/APIs                                                             │
│ - - - TB4: Web content → Context - - - - - - - - - - - - - - - - -  │
│   Tool results re-enter agent context window                         │
│ - - - TB9: LocalContextMiddleware / Host environment - - - - - - -   │
│   C15: LocalContextMiddleware runs bash detect script                │
│                                                                      │
│  C9: Config System ──► C17: Model Config (class_path → importlib)   │
│  (config.toml, .env)                                                │
└──────────────────────────────────────────────────────────────────────┘

---

Components

ID	Component	Description	Trust Level	Default?	Entry Points
C1	CLI Entry Point	Parses argv, loads config/env, bootstraps session	framework-controlled	Yes	main.cli_main, main.parse_args
C2	TUI / Non-interactive	Textual UI for interactive chat; client/non_interactive.py for headless pipelines (both use RemoteAgent)	framework-controlled	Yes	app.DeepAgentsApp.run, client.non_interactive.run_non_interactive
C3	Agent Engine	LangGraph agent graph running inside langgraph dev server, assembled by create_cli_agent	framework-controlled	Yes	agent.create_cli_agent, agent._add_interrupt_on, server_graph.make_graph
C4	Built-in Tools	http_request, web_search (Tavily), fetch_url (HTML→markdown)	framework-controlled	Partial¹	tools.http_request, tools.web_search, tools.fetch_url
C5	MCP Loader & Trust	Discovers/loads .mcp.json, validates server configs, applies per-server allow/deny lists + interactive approval	framework-controlled	No²	mcp_tools.resolve_and_load_mcp_tools, model_config.load_mcp_server_trust_lists, main._check_mcp_project_trust
C6	Hook Dispatcher	Fires subprocess commands on agent lifecycle events	framework-controlled	No³	hooks.dispatch_hook, hooks._run_single_hook
C7	Sandbox Integration	Creates/destroys remote sandboxes (Daytona, LangSmith, Modal, Runloop, AgentCore)	framework-controlled	No⁴	integrations.sandbox_factory.create_sandbox
C8	Session Persistence	SQLite checkpoint store for LangGraph thread state	framework-controlled	Yes	sessions.get_db_path, sessions.generate_thread_id
C9	Configuration System	TOML config, env vars, AGENTS.md system prompts, model config	user-controlled	N/A	config.settings, model_config.ModelConfig, ~/.deepagents/config.toml
C10	Unicode/URL Safety	Detects hidden Unicode, checks URL domain spoofing for approval UI warnings	framework-controlled	Yes	unicode_security.detect_dangerous_unicode, unicode_security.check_url_safety
C11	LangGraph Dev Server	Subprocess running langgraph dev with LANGGRAPH_AUTH_TYPE=noop; managed by ServerProcess	framework-controlled	Yes⁵	client.launch.server.ServerProcess.start, client.launch.server.generate_langgraph_json, client.launch.server_manager.start_server_and_get_agent
C12	Remote Agent Client	HTTP+SSE client wrapping RemoteGraph; connects to C11 on localhost	framework-controlled	Yes⁵	client.remote_client.RemoteAgent.astream, client.remote_client.RemoteAgent.aget_state
C13	Server Config Channel	Passes CLI config to server subprocess via DA_SERVER_* environment variables	framework-controlled	Yes	_server_config.ServerConfig.to_env, _server_config.ServerConfig.from_env
C14	Async Subagent Config	Loads remote LangGraph deployment specs from [async_subagents] in config.toml	user-controlled	No	agent.load_async_subagents
C15	LocalContext Middleware	Runs a bash detection script via backend; injects git/project/env context into system prompt each turn	framework-controlled	Yes⁶	local_context.LocalContextMiddleware.before_agent, local_context.build_detect_script
C16	Custom Subagent Loader	Reads {dir}/{name}/AGENTS.md YAML frontmatter from .deepagents/agents/ and project .agents/ directories	user-controlled	No	subagents.list_subagents, subagents._parse_subagent_file
C17	Model Config Loader	Resolves model provider, supports class_path for arbitrary BaseChatModel instantiation via importlib	user-controlled	N/A	config.create_model, config._create_model_from_class, model_config.ModelConfig.load

Notes:

1. http_request and fetch_url enabled by default; web_search requires TAVILY_API_KEY.
2. MCP servers only load if .mcp.json config files are present.
3. Hooks only execute if ~/.deepagents/hooks.json exists.
4. Sandbox mode requires explicit --sandbox CLI flag.
5. Both TUI and non-interactive modes now always spawn a local LangGraph dev server and connect via RemoteAgent.
6. LocalContextMiddleware is added whenever LocalShellBackend or an _AsyncExecutableBackend is in use (agent.py:create_cli_agent).

---

Data Classification

ID	PII Category	Specific Fields	Sensitivity	Storage Location(s)	Encrypted at Rest	Retention	Regulatory
DC1	API Keys / Credentials	ANTHROPIC_API_KEY, OPENAI_API_KEY, TAVILY_API_KEY, LANGSMITH_API_KEY, LANGGRAPH_API_KEY	Critical	Process environment only; never written to disk by CLI code	N/A (in-memory)	Process lifetime	All — breach trigger
DC2	Conversation Messages	User prompts, LLM responses, tool args/results	High	SQLite (~/.deepagents/*.db) via LangGraph checkpointer	No (local file, unencrypted)	Unbounded (session files persist)	GDPR if personal data is discussed
DC3	System Prompt Content	DA_SERVER_SYSTEM_PROMPT env var; custom AGENTS.md contents	Medium	Process environment (transient); ~/.deepagents/{agent}/AGENTS.md on disk	No	Config lifetime	None direct
DC5	Offloaded Conversation History	Summarized + raw conversation messages written to sandbox backend	High	Sandbox filesystem at /conversation_history/{thread_id}.md	Depends on sandbox provider	Sandbox session lifetime	GDPR if personal data is discussed

Data Classification Details

DC1: API Keys / Credentials

• Fields: ANTHROPIC_API_KEY, OPENAI_API_KEY, TAVILY_API_KEY, LANGSMITH_API_KEY, LANGGRAPH_API_KEY, and any provider-specific keys in the user's environment.
• Storage: Loaded from environment at process start (config.py). The CLI explicitly strips LANGGRAPH_CLOUD_LICENSE_KEY and related auth vars from the server subprocess environment (client/launch/server.py:_build_server_env) but passes the remaining env vars (including provider API keys) to the server subprocess via os.environ.copy().
• Access: Available to both the CLI process and the server subprocess (which inherits the full environment minus stripped vars).
• Encryption: Not encrypted — in-memory process environment only.
• Retention: Process lifetime; cleared on CLI exit.
• Logging exposure: Provider SDK error messages may include partial key information. The CLI does not log keys directly.
• Gaps: API keys are passed to the server subprocess via _build_server_env which does os.environ.copy() — all keys in the parent's environment become available to the child.

DC2: Conversation Messages

• Fields: Full conversation history (HumanMessage, AIMessage, ToolMessage) stored as LangGraph checkpoint state.
• Storage: SQLite at ~/.deepagents/{agent}/{thread_id}.db (via sessions.get_db_path).
• Access: Local filesystem; readable by any process running as the same user.
• Encryption: None — plaintext SQLite.
• Retention: Unbounded — session files persist until manually deleted.
• Logging exposure: Tool call arguments and results (including fetched web content) are in the checkpoint. File contents read by the agent are stored there too.
• Gaps: Unencrypted on disk; no retention policy enforced by the CLI.

DC5: Offloaded Conversation History

• Fields: Timestamped, formatted conversation messages written by offload.offload_messages_to_backend.
• Storage: Sandbox backend filesystem at path /conversation_history/{thread_id}.md where thread_id is a UUID7 (via sessions.generate_thread_id).
• Access: Accessible within the sandbox session; depends on provider access controls.
• Encryption: Depends on sandbox provider storage backend.
• Retention: Sandbox session lifetime (destroyed when sandbox is deleted).
• Logging exposure: Contains full message history including tool results.
• Gaps: Thread ID is UUID7 (no path injection risk), but offloaded content is unstructured markdown containing raw conversation data.

---

Trust Boundaries

ID	Boundary	Description	Controls (Inside)	Does NOT Control (Outside)
TB1	User Input → Agent Engine	Where user-typed prompts enter the agent graph	Prompt routing, session threading, UI rendering	Prompt content — any text accepted
TB2	LLM Decision → Tool Execution	HITL gate on all side-effecting tool calls	Interrupt map, allow-list check, auto-approve toggle	LLM reasoning; what user approves
TB3	Tool Result → LLM Context	Tool outputs re-enter the context window	Unicode warnings on URLs in args; markdownify HTML conversion	Content of fetched web pages, MCP responses, search results
TB4	MCP Config → Process / Network	.mcp.json triggers subprocess spawn or network connection	Schema validation; per-server allow/deny lists + interactive approval prompt	What the MCP process does once trusted and running
TB5	Hooks Config → Subprocess	hooks.json commands execute as local subprocesses	JSON structure validation, 5-second timeout	Command content (user-authored); env vars available to child
TB6	Setup Script → Sandbox Execution	User-supplied script runs inside sandbox at startup	shlex.quote() wrapping; string.Template.safe_substitute	Script content (user-authored); sandbox network access
TB7	External LLM API → Agent State	LLM API responses drive tool call decisions	LLM client configuration, model selection, request params	LLM output content; provider-side safety
TB8	CLI → Server Subprocess IPC	Config passed from CLI to langgraph dev subprocess via DA_SERVER_* env vars	ServerConfig.to_env() serialization; env var scoping to parent+child process	Subprocess environment post-fork; /proc visibility to same-uid processes
TB9	LocalContextMiddleware → Host Env	Bash detect script output (git info, project files, Makefile) injected into system prompt	Script is framework-generated static code; 30s timeout; exit-code check	Content of Makefile, pyproject.toml, git branch names, directory listing
TB10	RemoteAgent → LangGraph Dev Server	CLI communicates with agent via HTTP+SSE on localhost	Server bound to 127.0.0.1 (client/launch/server.py:_DEFAULT_HOST); ephemeral per session	No authentication (LANGGRAPH_AUTH_TYPE=noop); any localhost process can reach the API
TB11	Config File → Code Execution	class_path in config.toml triggers importlib.import_module(); project/global .env values are loaded into the process environment and can reach Bash startup hooks	Format validation (module:ClassName); issubclass(BaseChatModel) check; dotenv loading denies shell startup / environment-hijack keys (BASH_ENV, ENV)	Module-level side effects execute during import; user controls config file; project files in the working directory influence execution

Boundary Details

TB1: User Input → Agent Engine

• Inside: app.DeepAgentsApp routes keystrokes to message queue; client.non_interactive.run_non_interactive reads from stdin/argv. Both paths now go through RemoteAgent.astream to the server. Session ID assigned per thread (sessions.generate_thread_id).
• Outside: Prompt content — the agent accepts any text the user types. No content filtering at this layer (HITL handles downstream tool calls, not prompt intent).
• Crossing mechanism: HTTP POST to 127.0.0.1:{port} via client.remote_client.RemoteAgent.astream.

TB2: LLM Decision → Tool Execution Gate (HITL)

• Inside: agent._add_interrupt_on registers interrupt configs for execute, write_file, edit_file, web_search, fetch_url, task, compact_conversation, launch_async_subagent, update_async_subagent, cancel_async_subagent. In non-interactive mode, client.non_interactive._handle_action_request enforces the shell allow-list via config.is_shell_command_allowed.
• Outside: Once the user clicks "approve" (interactive) or a command passes the allow-list check (non-interactive), the tool executes with no further framework-level gating.
• Crossing mechanism: LangGraph HITL interrupt routed through RemoteAgent SSE stream.
• Key note: auto_approve mode bypasses all HITL approval prompts while still displaying Unicode/URL warnings.

TB3: Tool Result → LLM Context

• Inside: agent._format_execute_description / _format_fetch_url_description scan tool arguments (not results) for hidden Unicode and suspicious URLs. fetch_url converts HTML to markdown via markdownify before returning.
• Outside: The content of tool results (fetched web pages, web search snippets, MCP tool responses, execute stdout) is passed verbatim into the LLM context window. No prompt-injection scanning of results.
• Crossing mechanism: LangGraph ToolMessage returned to agent graph state; streamed to CLI via SSE.

TB4: MCP Config → Process / Network

• Inside: mcp_tools._validate_server_config validates JSON structure and field types. Project-level servers (stdio and remote) require approval: an interactive prompt (main._check_mcp_project_trust) that offers allow-for-this-session ("y"), remember a chosen subset of servers ("r"), or deny ("N") — unless --trust-project-mcp is set, which trusts the whole config for the run. Whole-config trust is never persisted; it exists only as in-memory run state, set by --trust-project-mcp or by an interactive allow-once/remember decision. The remembered subset governs only what is persisted for future runs — both allow-once and remember load every prompted server for the current session. Persistent "remember" (a.k.a. "always allow") decisions are stored in [mcp].enabled_project_server_approvals with project root, server name, and server definition fingerprint, so a different project or changed command/URL under the same name requires re-approval. Users can reject individual project server names via [mcp].disabled_project_servers (or DEEPAGENTS_CODE_DISABLED_PROJECT_MCP_SERVERS). There is also an explicit process-wide escape hatch, DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS, that approves matching names globally and intentionally bypasses the project/fingerprint binding. Reject wins over approval and over full trust, with one documented exception: if the user's config.toml is unreadable (see below), any deny defined only there is lost, so a name that is both TOML-disabled and exported in DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS survives — an accepted footgun (load_mcp_server_trust_lists documents it inline) that requires a self-contradicting config plus the explicit dangerous opt-in, and the read error is surfaced to the user. These policies are read by model_config.load_mcp_server_trust_lists only from user-controlled sources — the user-level ~/.deepagents/config.toml, the global ~/.deepagents/.env, and shell-exported env — never from .mcp.json or any repo-committed file, so a committed config cannot self-approve its own servers. In particular, the env forms (DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS / ..._DISABLED_...) are added to config._PROJECT_DOTENV_DENIED_ENV_KEYS, so a committed project .env cannot inject them into os.environ even though it is loaded at bootstrap. The dangerous enable env var replaces TOML approvals when set, while disabled unions TOML and env so a deny can never be silently emptied by the other source. If the user's config.toml exists but cannot be read or parsed, the loader fails closed (project configs are treated as untrusted and the error is surfaced) rather than proceeding with an empty deny list. Only scoped-approved, dangerously env-enabled, or fully trusted names survive into the merged config, so a non-approved remote entry is never preflighted and its interpolated headers are never resolved.
• Outside: Stdio server command, args, and env fields are user-controlled strings passed directly to StdioConnection. The env dict from MCP config is forwarded without filtering — users can set arbitrary environment variables (including PATH, LD_PRELOAD, PYTHONPATH) for the MCP subprocess.
• Crossing mechanism: subprocess.Popen (via langchain_mcp_adapters) for stdio; HTTP/SSE for remote servers.

TB8: CLI → Server Subprocess IPC

• Inside: ServerConfig.to_env() serializes all config (model, assistant_id, system_prompt, sandbox settings, cwd, MCP config path, shell-enable flags) to DA_SERVER_* env vars. client.launch.server_manager._apply_server_config writes these to os.environ before subprocess.Popen. client/launch/server.py:_build_server_env strips cloud auth vars (LANGGRAPH_CLOUD_LICENSE_KEY, LANGSMITH_CONTROL_PLANE_API_KEY, LANGSMITH_TENANT_ID, LANGGRAPH_AUTH).
• Outside: The full parent environment including provider API keys flows to the child via os.environ.copy(). The system prompt string becomes DA_SERVER_SYSTEM_PROMPT. After process start, the env is fixed — no runtime mutation across processes.
• Crossing mechanism: subprocess.Popen env kwarg; subsequent os.environ reads in _server_config.ServerConfig.from_env.

TB9: LocalContextMiddleware → Host Environment

• Inside: local_context.build_detect_script generates a static bash heredoc that runs git commands, checks for lock files, reads Makefile headings, and produces a structured markdown summary. Script is static framework code — not constructed from user input. Exit code checked via _handle_detect_result. 30-second timeout.
• Outside: The script reads and includes the first 20 lines of Makefile (_section_makefile) and a directory listing. These file contents are not sanitized before injection into the system prompt. An attacker who can write to the CWD's Makefile can influence the system prompt.
• Crossing mechanism: backend.execute(DETECT_CONTEXT_SCRIPT) → result appended to system_prompt via LocalContextMiddleware._get_modified_request.

TB10: RemoteAgent → LangGraph Dev Server

• Inside: Server bound to 127.0.0.1 by default; client/launch/server.py:_DEFAULT_HOST = "127.0.0.1". RemoteAgent only connects to the URL returned by ServerProcess.url. Server is ephemeral — started at session start, stopped at session end. Binds a free ephemeral port by default (client/launch/server.py:_EPHEMERAL_PORT); an explicit port is honored but still falls back to a free port if occupied.
• Outside: LANGGRAPH_AUTH_TYPE=noop disables all LangGraph server authentication. Any process on localhost that discovers the port can submit requests, read thread state, or inject messages.
• Crossing mechanism: HTTP POST/GET to http://127.0.0.1:{port} using langgraph.pregel.remote.RemoteGraph.

TB11: Config File → Code Execution

• Inside: config._create_model_from_class validates class_path format (module:ClassName), imports the module via importlib.import_module(), and checks issubclass(cls, BaseChatModel) before instantiation.
• Outside: Module-level code in the imported module executes unconditionally during import_module(). The issubclass check only runs after import. Any side effects (file I/O, network calls, subprocess spawning) in the module's top-level scope execute before the type check.
• Crossing mechanism: importlib.import_module(module_path) in config._create_model_from_class.
• Inside (dotenv): config._load_dotenv loads project and global .env values with override=False and drops shell startup / environment-hijack keys (BASH_ENV, ENV, and related) so a project .env cannot register a script that Bash would source at startup. The preview path (_preview_dotenv_environ) applies the same denylist so a dry-run config change cannot report a value a real reload would reject.
• Outside (dotenv): All other .env keys are applied to the process environment, and any project file in the working directory (.env, Makefile, build scripts) can still influence execution. See T12.

---

Data Flows

ID	Source	Destination	Data Type	Classification	Crosses Boundary	Protocol
DF1	User	C2 TUI	User prompt text	—	TB1	Keystrokes / stdin
DF2	C2 TUI	C12 RemoteAgent	Human message + thread config	—	TB1	Function call
DF3	C12 RemoteAgent	C11 LangGraph Dev Server	Input messages, thread ID	DC2	TB10	HTTP POST (localhost)
DF4	C11 LangGraph Dev Server	C12 RemoteAgent	SSE stream (AI responses, tool calls, interrupts)	DC2	TB10	HTTP+SSE (localhost)
DF5	C3 Agent	External LLM	System prompt + message history	DC1, DC2	TB7	HTTPS / LangChain
DF6	External LLM	C3 Agent	AI response + tool call decisions	—	TB7	HTTPS / LangChain
DF7	C3 Agent	C4 Tools	Tool call arguments (file/URL/command)	—	TB2	Function call + HITL
DF8	External	C4 Tools	HTTP response bodies (web/API content)	—	TB3	HTTPS
DF9	C4 Tools	C3 Agent	Tool results (file content, web pages)	—	TB3	ToolMessage
DF10	C9 Config	C1 Entry	TOML config, env vars, API keys	DC1	None	File + environ
DF11	C5 MCP	C3 Agent	MCP tool call results	—	TB3, TB4	MCP protocol
DF12	C9 Config	C5 MCP	.mcp.json server definitions (command, args, env)	—	TB4	File read
DF13	C3 Agent	C8 Sessions	Agent state snapshots	DC2	None	SQLite async write
DF14	C8 Sessions	C3 Agent	Restored thread state on resume	DC2	None	SQLite async read
DF15	C9 Config	C6 Hooks	hooks.json command definitions	—	TB5	File read
DF16	C3 Agent	C6 Hooks	Event payload (JSON)	—	TB5	subprocess stdin
DF17	User	C7 Sandbox	Setup script path + content	—	TB6	File read + execute
DF18	C1 Entry	C11 Server	DA_SERVER_* env vars (config + credentials)	DC1, DC3	TB8	Process environment
DF19	Host FS	C15 LocalContext	Makefile, project file contents (first 20 lines), directory listing	DC3	TB9	bash subprocess stdout
DF20	C15 LocalContext	C3 Agent	Project context markdown appended to system prompt	DC3	TB9	string append to prompt
DF21	C16 Subagent Loader	C3 Agent	AGENTS.md body (raw text) used as subagent system_prompt	DC3	None	YAML parse + dict
DF22	C14 Async Config	C3 Agent	AsyncSubAgent specs (URL, graph_id, headers) from config.toml	—	None	TOML parse + dict
DF23	C9 Config	C17 Model Config	class_path string from config.toml	—	TB11	TOML parse → importlib
DF24	C5 MCP Config	MCP Subprocess	env dict from .mcp.json forwarded to stdio subprocess	DC1	TB4	subprocess environment
DF25	C3 Agent	C7 Sandbox	Conversation messages for offload	DC5	TB6	backend.awrite()

Flow Details

DF8/DF9: External Web Content → Agent Context

• Data: Arbitrary HTML/JSON from the internet, converted to markdown by markdownify. Can be megabytes.
• Validation: URL domain checked for Unicode spoofing / script mixing (unicode_security.check_url_safety); displayed as warning in approval dialog. Content not scanned for prompt-injection patterns.
• Trust assumption: User approved the fetch. Content is data — but the LLM may interpret adversarial content as instructions.

DF11: MCP Tool Results → Agent Context

• Data: Arbitrary strings/objects from MCP tool calls.
• Validation: MCP config approved at load time (per-server allow-list or interactive prompt). Tool result content not validated after server is trusted.
• Trust assumption: User trusted the MCP server; its outputs are as reliable as the server.

DF18: DA_SERVER_* Env Vars

• Data: Model name, system prompt text, sandbox settings, CWD, MCP config path, shell-enable flags — plus inherited provider API keys.
• Validation: No validation on server side beyond TOML/JSON decoding for structured fields (_read_env_json). The system prompt and model name are passed through verbatim.
• Trust assumption: The server subprocess is trusted with the same access as the CLI process.

DF19/DF20: LocalContextMiddleware Bash Script

• Data: Git branch/status, project language/structure, Makefile first 20 lines, directory listing (up to 20 files), runtime versions.
• Validation: Script exit code checked (_handle_detect_result). 30-second timeout. Script itself is static framework code — not interpolated from user input.
• Trust assumption: Files in the working directory are trustworthy. A malicious Makefile could inject content into the system prompt (requires write access to CWD).

DF23: class_path Config → importlib Code Execution

• Data: Fully-qualified Python class path string (e.g., my_package.models:MyChatModel) from [models.providers.<name>] section of config.toml.
• Validation: Format check (module:ClassName with : separator). After import, issubclass(cls, BaseChatModel) check. No validation of module contents before import.
• Trust assumption: User controls ~/.deepagents/config.toml (same trust model as pyproject.toml build scripts).

DF24: MCP Stdio Env Dict → Subprocess

• Data: Arbitrary key-value pairs from the "env" field of stdio server definitions in .mcp.json.
• Validation: Type check only — env must be a dict (mcp_tools._validate_server_config). No filtering of key names or values. Forwarded directly to StdioConnection which passes to subprocess.Popen.
• Trust assumption: User authored or approved the MCP config. Project-level configs go through the approval gate (allow-list or interactive prompt) before loading.

---

Threats

ID	Data Flow	Classification	Threat	Boundary	Severity	Validation	Code Reference
T1	DF8, DF9	—	Prompt injection via fetched web content causes LLM to request harmful actions	TB3	Medium	Likely	tools.fetch_url, agent._add_interrupt_on
T2	DF7	—	--shell-allow-list all removes pattern checks; LLM-injected shell commands execute without approval in non-interactive mode	TB2	Medium	Verified	config.is_shell_command_allowed, client.non_interactive._handle_action_request
T3	DF7	—	Unicode-homoglyph URL in LLM-generated tool args deceives user during approval	TB2	Low	Disproven	unicode_security.check_url_safety, agent._format_fetch_url_description
T4	DF5, DF9	—	Auto-approve mode bypasses all HITL gates; any LLM-initiated tool call executes	TB2	Low	Verified	agent.create_cli_agent (auto_approve param), agent._add_interrupt_on
T5	DF13, DF14	DC2	Local SQLite checkpoint file tampered with to inject adversarial content into future LLM context	None	Low	Unverified	sessions.get_db_path
T6	DF3, DF4	DC2	Unauthenticated LangGraph dev server on localhost can be accessed by any local process	TB10	Medium	Verified	server._build_server_env, server._DEFAULT_HOST
T7	DF19, DF20	DC3	Makefile or project file content injected into system prompt via LocalContextMiddleware	TB9	Low	Verified	local_context._section_makefile, local_context.LocalContextMiddleware._get_modified_request
T8	DF21	DC3	Custom subagent AGENTS.md body used verbatim as system_prompt without content validation	None	Low	Verified	subagents._parse_subagent_file, agent.create_cli_agent
T9	DF23	—	class_path in config.toml triggers arbitrary Python code execution via importlib.import_module()	TB11	Low	Verified	config._create_model_from_class, model_config.ProviderConfig
T10	DF24	DC1	MCP stdio subprocess env dict accepts arbitrary keys including PATH, LD_PRELOAD, PYTHONPATH without filtering	TB4	Low	Verified	mcp_tools._validate_server_config, mcp_tools._load_tools_from_config
T12	DF10	—	Project .env sets shell startup-hook variables (BASH_ENV, ENV) that run attacker-controlled scripts when dcode spawns Bash, before any HITL approval	TB11	High	Verified	config._load_dotenv, local_context.build_detect_script

Threat Details

T1: Prompt Injection via Fetched Web Content

• Flow: DF8 (external web) → DF9 (tool result) → C3 Agent context
• Description: When the agent calls fetch_url or web_search, the response body enters the LLM's context window as a ToolMessage. A maliciously crafted web page or search snippet can embed natural-language instructions that the LLM may interpret as authoritative commands, leading to unexpected tool call requests in the next turn.
• Preconditions: (1) User or LLM-initiated call to fetch_url/web_search reaches a malicious page; (2) LLM interprets injected instructions as directives; (3) In interactive mode, user must still approve the resulting tool call.

T2: Shell Allow-List Bypass via SHELL_ALLOW_ALL

• Flow: DF7 (LLM tool call) → C4 Tools (execute)
• Description: When --shell-allow-list all (or DEEPAGENTS_SHELL_ALLOW_LIST=all) is set, is_shell_command_allowed returns True for any non-empty command without invoking contains_dangerous_patterns. In non-interactive mode, any shell command the LLM requests executes unconditionally. Combined with T1, an attacker-controlled page could cause arbitrary command execution.
• Preconditions: (1) User has configured --shell-allow-list all; (2) Non-interactive mode; (3) Successful prompt injection via DF8/DF9.

T3: Unicode Homoglyph URL in Approval Dialog

• Flow: DF7 (LLM-generated fetch_url args) → C2 TUI approval dialog
• Description: An LLM influenced by adversarial input could generate a fetch_url call with a URL containing mixed-script or confusable characters visually identical to ASCII.
• Preconditions: LLM generates a confusable URL (requires adversarial steering). check_url_safety detects mixed-script domain labels and strip_dangerous_unicode removes invisible BiDi/zero-width characters; warnings displayed in approval dialog. Classified Disproven as a project vulnerability — the UI warnings are the intended control.

T4: Auto-Approve Removes All Execution Safeguards

• Flow: DF7 (all tool calls) when auto_approve=True
• Description: When auto-approve is enabled (via --auto-approve flag or Shift+Tab in TUI), all tool calls including execute, write_file, edit_file, fetch_url, launch_async_subagent execute without user confirmation.
• Preconditions: User explicitly enables auto-approve. Default is approval-required.

T5: Local SQLite Checkpoint Tampering

• Flow: DF13/DF14 (session persist/restore)
• Description: LangGraph checkpoints stored in ~/.deepagents/*.db contain the full conversation history and agent state. An attacker with local filesystem write access could inject adversarial messages that re-enter the LLM context on session resume.
• Preconditions: Attacker has write access to the user's home directory — equivalent to a fully compromised user account.

T6: Unauthenticated LangGraph Dev Server on Localhost

• Flow: DF3/DF4 (CLI ↔ LangGraph dev server)
• Description: The CLI spawns a langgraph dev server subprocess with LANGGRAPH_AUTH_TYPE=noop (client/launch/server.py:_build_server_env). This disables all server-side authentication. The server binds to 127.0.0.1:{port} (a free ephemeral port by default, so it no longer squats the well-known langgraph dev port 2024). Any local process that discovers the port can: send arbitrary inputs to the running agent thread, read the agent's conversation state (including tool results that may contain file contents or secrets), inject messages into the conversation history, or trigger state updates. The server is ephemeral — it lives only for the duration of the CLI session — but this is the entire attack window. Port discovery is feasible via localhost port scanning or by reading /proc/{pid}/cmdline which contains the --port argument.
• Preconditions: (1) Attacker has a local process running as the same user (or as root); (2) Attacker discovers the server port (port scan on localhost, or reads process arguments).

T7: LocalContextMiddleware Injects Host File Contents into System Prompt

• Flow: DF19 → DF20
• Description: LocalContextMiddleware runs a bash script (build_detect_script) that reads the first 20 lines of Makefile (_section_makefile) and a filtered directory listing, then injects this output verbatim into the system prompt on every turn. An attacker with write access to the project's working directory could craft Makefile content designed to manipulate the agent's behavior.
• Preconditions: Attacker has write access to the Makefile in the agent's working directory. The agent must be running in that directory (local mode, not sandbox mode).

T8: Custom Subagent Body Used as System Prompt Without Validation

• Flow: DF21
• Description: subagents._parse_subagent_file reads AGENTS.md files from .deepagents/agents/{name}/AGENTS.md and project-level .agents/{name}/AGENTS.md. The markdown body after the YAML frontmatter is used verbatim as the subagent's system_prompt. No content filtering is applied.
• Preconditions: Attacker has write access to ~/.deepagents/agents/ or the project's .agents/ directory. User or LLM must invoke the malicious subagent via the task tool.

T9: Arbitrary Python Code Execution via class_path Config

• Flow: DF23 (config.toml → importlib)
• Description: The class_path field in [models.providers.<name>] config triggers importlib.import_module() in config._create_model_from_class. While the imported class is validated as a BaseChatModel subclass, module-level code executes unconditionally during import — before the type check runs. A malicious or compromised config.toml pointing to a hostile module causes arbitrary code execution at model initialization time. This applies to both class_path and the _load_provider_profiles path that uses exec_module() to load _profiles.py from provider packages.
• Preconditions: Attacker has write access to ~/.deepagents/config.toml AND a malicious Python package installed in the user's environment (or on sys.path). The code comments document this as intentional: "same trust model as pyproject.toml build scripts — the user controls their own machine."

T10: MCP Stdio Env Dict Forwarded Without Filtering

• Flow: DF24 (MCP config → subprocess environment)
• Description: The "env" field in stdio MCP server definitions (.mcp.json) accepts an arbitrary key-value dict. mcp_tools._validate_server_config only checks that the field is a dict — it does not filter key names or values. The dict is forwarded directly to StdioConnection(env=...) which passes it to the subprocess. An attacker who can modify a project-level .mcp.json could set PATH to redirect command resolution, LD_PRELOAD to inject shared libraries, or PYTHONPATH to hijack Python imports in the MCP subprocess.
• Preconditions: (1) Attacker has write access to a project-level .mcp.json; (2) The project MCP config must be approved by the user (the interactive prompt, --trust-project-mcp, or the server approved via [mcp].enabled_project_server_approvals / the DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS env var). For user-level ~/.deepagents/.mcp.json, the attacker already has home directory write access. Note: the env dict from MCP config is passed to StdioConnection — whether it replaces or merges with os.environ depends on the langchain_mcp_adapters library implementation.

T11: Auto-Installed ripgrep Binary from Upstream Release

• Flow: Download performed by managed_tools.ensure_ripgrep when rg is not on PATH — either on first run, or eagerly at install time via dcode tools install (invoked by scripts/install.sh).
• Description: Without a system rg, Deep Agents Code fetches the pinned ripgrep release tarball from github.com/BurntSushi/ripgrep/releases/..., verifies it against an in-tree SHA-256 (RIPGREP_ASSETS), extracts it under a TemporaryDirectory, and atomically moves the binary into ~/.deepagents/bin/rg. The binary then runs unsandboxed, inheriting the same trust as a user-installed rg (the SDK invokes it via subprocess.run(["rg", ...])). The same verified path backs the dcode tools install verb, so the install script reuses it rather than re-encoding the version + checksum table in bash.
• Mitigations: (1) SHA-256 verified against the pinned hash table before move — a mismatch aborts the install and leaves BIN_DIR clean. (2) Network egress is limited to github.com. (3) Opt-out via DEEPAGENTS_CODE_OFFLINE for air-gapped environments, or DEEPAGENTS_CODE_RIPGREP_INSTALLER=system to defer to the OS package manager instead of the managed binary. (4) Pinned version + checksums are bumped in-tree, so a compromised upstream release is detected on the next Deep Agents Code release rather than silently propagating. (5) Atomic move-into-place avoids partial installs when concurrent CLI invocations race. (6) The eager install-script path is non-sudo (no system package manager is invoked in the default managed mode).
• Preconditions: User has not installed rg via their package manager, DEEPAGENTS_CODE_OFFLINE is unset, DEEPAGENTS_CODE_RIPGREP_INSTALLER is not system, and the host can reach github.com. The pinned SHA-256 in RIPGREP_ASSETS would need to be incorrect (a supply-chain compromise of the deepagents-code release) for a tampered binary to be installed.

T12: Project .env Injects Shell Interpreter Startup Hooks

• Flow: DF10 (project .env → process environment) → bash subprocess startup (DF19)
• Description: config._load_dotenv discovers the nearest project .env by walking up from the working directory and applies its values to the process environment (override=False, so shell-exported values still win). Bash treats several environment variables as startup hooks — BASH_ENV and ENV name a file that is sourced when a non-interactive shell starts. Because Deep Agents Code runs its own local-context detection through Bash at startup (local_context.build_detect_script) and spawns shells for the execute tool, a project .env that sets one of these keys could run attacker-controlled scripts inside the dcode process before any model-requested tool call or HITL approval prompt. The broader trust boundary is that running dcode in a project directory lets that project influence the process environment.
• Preconditions: (1) User launches dcode in a directory containing an attacker-controlled .env (for example, a freshly cloned untrusted repository); (2) the .env sets a shell startup-hook key that is not already exported in the user's shell (dotenv uses override=False).

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Input Source Coverage

Input Source	Data Flows	Threats	Validation Points	Responsibility	Gaps
User direct input	DF1, DF2	None (TB1)	None — prompts accepted verbatim	User	No content filtering — intentional; HITL gates downstream tool calls
LLM output	DF6, DF7	T1, T2, T3, T4	HITL gate; shell allow-list; Unicode/URL warnings on tool args	Project	LLM-generated tool args not scanned for injection beyond Unicode/URL
Tool/function results	DF9, DF11	T1	Unicode warning on URL args; markdownify HTML conversion	Shared	Tool results pass to context without prompt-injection scan
URL-fetched content	DF8, DF9	T1	check_url_safety on URL arg; HTML→markdown conversion	Shared	Markup-embedded instructions survive markdownify; no LLM-layer guardrail
Configuration	DF10, DF12, DF15, DF23	T9, T10, T12	Dotenv shell-env precedence; TOML schema; MCP schema + allow/deny lists; JSON structure check; class_path format check	User	Project .env can set shell startup-hook vars; class_path executes module code before type check; MCP env dict unfiltered
Session restore	DF14	T5	OS file permissions; SQLite	Project	Unencrypted at rest
Server IPC (env vars)	DF18	T6	ServerConfig serialization; parent env passed to child	Project	Provider API keys flow to server subprocess; system prompt in env
Host environment	DF19, DF20	T7	Static script; exit code check; 30s timeout	Shared	Makefile content injected into system prompt without sanitization
Custom subagents (FS)	DF21	T8	yaml.safe_load; HITL on task tool	User	Subagent body text not content-filtered
Async subagent config	DF22	None direct	TOML parse; type validation in load_async_subagents	User	URL and headers for remote subagents are user-controlled; no URL validation
MCP subprocess env	DF24	T10	Dict type check only (_validate_server_config)	User	No key/value filtering; arbitrary env vars forwarded to subprocess
Offloaded history	DF25	None direct	Thread ID is UUID7 (no path injection); backend handles storage	Shared	Raw conversation content written to sandbox filesystem

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Out-of-Scope Threats

Threats that appear valid in isolation but fall outside project responsibility because they depend on conditions the project does not control.

Pattern	Why Out of Scope	Project Responsibility Ends At
Prompt injection leading to arbitrary code execution (interactive mode)	In interactive mode, every side-effecting tool call requires explicit user approval via the HITL dialog. The user is the final gatekeeper.	Providing HITL for all side-effecting tools (agent._add_interrupt_on) and Unicode/URL warnings in the approval dialog.
API key exfiltration via LLM-directed http_request	http_request requires HITL in interactive mode. Keys it could exfiltrate are user-supplied env vars. In non-interactive mode, user has opted into autonomous operation.	Providing HITL gate for HTTP tools. Users control which env vars are in scope.
Malicious MCP server injecting prompt instructions	Users configure MCP servers and explicitly trust project-level configs. Once trusted, MCP tool outputs are data from a system the user controls.	Interactive approval prompt + per-server allow/deny lists for project-level configs (main._check_mcp_project_trust, model_config.load_mcp_server_trust_lists).
LLM jailbreak / safety bypass	Model selection and safety configuration are user-controlled. The project routes prompts to the configured LLM but cannot guarantee model behavior.	Correctly routing prompts to the configured LLM; applying the system prompt from agent.get_system_prompt.
Sandbox provider security vulnerabilities	Daytona, LangSmith, Modal, Runloop, and AgentCore are third-party services. Their internal security is not this project's responsibility.	Correctly initializing sandbox sessions via integrations.sandbox_factory.create_sandbox.
Hook commands doing harmful things	Hooks in ~/.deepagents/hooks.json are 100% user-authored. The payload is data-only (JSON on stdin).	JSON structure validation (hooks._load_hooks); 5-second timeout.
Async subagent traffic interception / MitM	Async subagents connect to user-configured LangGraph deployment URLs. The project does not control those endpoints or their TLS certificates.	Accepting URL/headers from user config and passing them to the LangGraph SDK (agent.load_async_subagents).
LangGraph dev server port enumeration / discovery	Discovering the local dev server port requires local access. Port scanning localhost is a general OS security concern, not a framework vulnerability.	Binding to 127.0.0.1 by default (server._DEFAULT_HOST); ephemeral server lifetime; OS-assigned ephemeral port (server._EPHEMERAL_PORT) is not predictable across runs.
.env file from parent directory changes app/API configuration	config._find_dotenv_from_start_path walks up the directory tree to find .env files. Discovering ordinary configuration values (API keys, DEEPAGENTS_CODE_* settings) this way is standard python-dotenv behavior, and the user controls their filesystem. The code-execution implication of a project .env (shell startup hooks) is tracked in-scope as T12.	Finding .env from the project root (config._find_dotenv_from_start_path); override=False by default (existing env vars preserved); shell startup / environment-hijack keys (BASH_ENV, ENV) denied during dotenv loading.

Rationale

Prompt injection in interactive mode: The HITL interrupt means every file write, shell command, web search, URL fetch, task delegation, and async subagent action shows the user a confirmation dialog with full tool arguments. Even a successful prompt injection can only execute what the user explicitly approves. The project's responsibility is to make that dialog accurate — hence the Unicode/URL warning layer in unicode_security.py.

LangGraph dev server without auth: The LANGGRAPH_AUTH_TYPE=noop setting is intentional for local dev server use. Adding authentication would require users to manage tokens for a locally-spawned ephemeral process, creating more friction than security benefit in this context. The 127.0.0.1 binding limits exposure to the local machine. T6 documents this as an accepted risk for the threat model.

Custom subagent system prompts: Subagent definitions in .deepagents/agents/ are user-authored files. The framework correctly treats them as user-controlled content. The HITL gate on task tool calls ensures the user approves subagent delegation before it occurs.

class_path code execution: This follows the same trust model as pyproject.toml build scripts — the user edits their own config file on their own machine. The issubclass(BaseChatModel) check provides a post-import guard, though module-level side effects execute before it. Documented as intentional in model_config.py.

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Investigated and Dismissed

ID	Original Threat	Investigation	Evidence	Conclusion
D1	Unsafe msgpack deserialization in langgraph checkpoint loading	Verified fix status — confirmed fixed and closed upstream.	Users on current langgraph versions are not exposed.	Upstream langgraph has patched the unsafe msgpack deserialization. No longer an active risk.
D2	Unicode URL homoglyph as project vulnerability	Traced check_url_safety + strip_dangerous_unicode + format_warning_detail → approval dialog display	unicode_security.check_url_safety, agent._format_fetch_url_description	Warning system is the intended control — the project correctly surfaces the risk to the user in the approval dialog. Not a project vulnerability; classified as mitigated by design (UI warning).
D3	SSRF via http_request / fetch_url to internal services	Traced tools.http_request and tools.fetch_url — no URL scheme or host blocklist. However, both tools require HITL approval in interactive mode. In non-interactive mode, only shell commands are auto-approved via the allow-list; HTTP tools still go through the HITL interrupt gate.	tools.http_request, tools.fetch_url, agent._add_interrupt_on	Not a project vulnerability in isolation — the HITL gate is the intended control for all HTTP tool calls. The user sees the full URL before approving. SSRF is only reachable if the user approves the request (interactive) or enables auto-approve (explicit opt-in). Classified as out-of-scope for the same reason as prompt injection in interactive mode.
D4	Offload path injection via thread_id	Checked sessions.generate_thread_id — returns UUID7 string (alphanumeric + hyphens only, no path separators).	sessions.generate_thread_id, offload.offload_messages_to_backend	Thread IDs are UUID7 strings generated by the framework. No user-controlled path components reach the file path. Not exploitable.

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Revision History

Date	Author	Changes
2026-03-10	langster-threat-model (automated)	Initial threat model
2026-03-27	langster-threat-model (automated)	Deep expansion: added C11-C16 (server subprocess, RemoteAgent, LocalContextMiddleware, async subagent config, custom subagent loader); added TB8-TB10 (CLI/server IPC, LocalContext/host env, RemoteAgent/dev server); added DF18-DF22; added T6 (unauthenticated dev server), T7 (Makefile injection), T8 (subagent body injection); updated T5 (upstream msgpack fix confirmed); added data classification; added Investigated and Dismissed section; updated architecture diagram to reflect server-subprocess model
2026-03-28	langster-threat-model (automated)	Deep validation pass: added C17 (Model Config Loader with class_path), TB11 (Config→Code Execution); added DC5 (offloaded conversation history); added DF23-DF25 (class_path flow, MCP env dict flow, offload flow); added T9 (class_path arbitrary code execution), T10 (MCP env dict unfiltered); added D3 (SSRF dismissed — HITL is intended control), D4 (offload path injection dismissed — UUID7); removed Status column and Mitigations/Residual Risk fields from all threats (open source visibility compliance — mitigation status must not appear in public threat models); updated T6 validation from Likely to Verified (port is deterministic at 2024 default, discoverable via /proc); updated external context (no published advisories found); updated architecture diagram
2026-06-25	manual update	Added T12 (project .env injects shell interpreter startup hooks) under TB11; extended the TB11 boundary controls and details with the dotenv shell-startup-hook denylist (BASH_ENV, ENV); clarified the .env out-of-scope row to separate ordinary config discovery from the in-scope code-execution implication
2026-06-25	manual update	Updated T6 and TB10 to reflect the dev server binding an OS-assigned ephemeral port by default (server._EPHEMERAL_PORT) instead of squatting the well-known langgraph dev port 2024; noted unpredictable-port-across-runs as added defense-in-depth in the port-discovery dismissal row
2026-07-08	manual update	Removed the SHA-256 config fingerprint trust store (mcp_trust.py, ~/.deepagents/.state/mcp_trust.json, DC4). Project MCP trust is now the interactive approval prompt (allow-for-session / always-allow scoped to project root + server-definition fingerprint / deny), the --trust-project-mcp run flag, the [mcp].enabled_project_server_approvals and [mcp].disabled_project_servers lists, and the process-wide DEEPAGENTS_CODE_DANGEROUSLY_ENABLE_PROJECT_MCP_SERVERS name-based escape hatch. The legacy flat [mcp].enabled_project_servers key is ignored. Persisted approvals bind to a server definition's fingerprint rather than a whole-config fingerprint. Updated C5, TB4, T10, the configuration input-coverage row, and the malicious-MCP-server dismissal accordingly