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a3be157768 |
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@@ -10,9 +10,9 @@ Eyeballer is meant for large-scope network penetration tests where you need to f
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| ------ |:-----:|
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|  |  |
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| Homepages | Custom 404's |
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| Webapp | Custom 404's |
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| ------ |:-----:|
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|  |  |
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## What the Labels Mean
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@@ -22,8 +22,8 @@ Blocky frames, broken CSS, that certain "je ne sais quoi" of a website that look
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**Login Pages**
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Login pages are valuable to pen testing, they indicate that there's additional functionality you don't currently have access to. It also means there's a simple follow-up process of credential enumeration attacks. You might think that you can set a simple heuristic to find login pages, but in practice it's really hard. Modern sites don't just use a simple input tag we can grep for.
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**Homepages**
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A "Homepage" is the landing page to a larger application. This tells you that there is a larger group of pages and functionality available here that can serve as surface area to attack. This is in contrast to a simple login page, with no other functionality. Or a default IIS landing page which has no other functionality. A "Homepage" should indicate to you that there is a web application here to attack.
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**Webapp**
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This tells you that there is a larger group of pages and functionality available here that can serve as surface area to attack. This is in contrast to a simple login page, with no other functionality. Or a default IIS landing page which has no other functionality. This label should indicate to you that there is a web application here to attack.
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**Custom 404**
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Modern sites love to have cutesy custom 404 pages with pictures of broken robots or sad looking dogs. Unfortunately, they also love to return HTTP 200 response codes while they do it. More often, the "404" page doesn't even contain the text "404" in it. These pages are typically uninteresting, despite having a lot going on visually, and Eyeballer can help you sift them out.
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+1
-1
@@ -54,7 +54,7 @@ def predict(ctx, screenshot, heatmap, threshold):
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print(results)
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else:
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with open("results.csv", "w", newline="") as csvfile:
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fieldnames = ["filename", "custom404", "login", "homepage", "oldlooking"]
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fieldnames = ["filename", "custom404", "login", "webapp", "oldlooking"]
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labelwriter = csv.DictWriter(csvfile, fieldnames=fieldnames)
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labelwriter.writeheader()
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labelwriter.writerows(results)
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+3
-3
@@ -17,7 +17,7 @@ from tensorflow.keras.applications.mobilenet import preprocess_input
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from sklearn.metrics import classification_report, accuracy_score, hamming_loss
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from eyeballer.augmentation import EyeballerAugmentation
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DATA_LABELS = ["custom404", "login", "homepage", "oldlooking"]
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DATA_LABELS = ["custom404", "login", "webapp", "oldlooking"]
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class EyeballModel:
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@@ -205,7 +205,7 @@ class EyeballModel:
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result["filename"] = "custom-image"
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result["custom404"] = prediction[0][0]
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result["login"] = prediction[0][1]
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result["homepage"] = prediction[0][2]
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result["webapp"] = prediction[0][2]
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result["oldlooking"] = prediction[0][3]
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return result
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@@ -249,7 +249,7 @@ class EyeballModel:
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result["filename"] = screenshot
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result["custom404"] = prediction[0][0]
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result["login"] = prediction[0][1]
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result["homepage"] = prediction[0][2]
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result["webapp"] = prediction[0][2]
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result["oldlooking"] = prediction[0][3]
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results.append(result)
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return results
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@@ -7,7 +7,7 @@
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<button class="btn all" onclick="filterSelection('all')"> Show all</button>
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<button class="btn" onclick="filterSelection('custom404')"> 404</button>
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<button class="btn" onclick="filterSelection('login')"> Login</button>
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<button class="btn" onclick="filterSelection('homepage')"> Home Page</button>
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<button class="btn" onclick="filterSelection('webapp')"> Web Application</button>
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<button class="btn" onclick="filterSelection('oldlooking')"> Old Looking</button>
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<select onchange="numOnPage=this.value; filterSelection([]);" style="float:right; margin:5px; margin-right:30px; height:30px; width:auto;">
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<option value="25">25</option>
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