ajt-lancet

v26.5.12.0 suspicious
6.0
Medium Risk

OCR snipping tool for reading manga.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple concerning behaviors including potential unauthorized network communication, risky process execution practices, and signs of code obfuscation. These factors suggest a need for further investigation before considering it safe.

  • network risk due to potential unauthorized data transmission
  • shell risk from executing processes with Popen
  • code obfuscation indicating hidden functionality
Per-check LLM notes
  • Network: The HTTP connection pattern may indicate the package is designed to communicate with an external server, which could be for legitimate purposes like updates but also raises concerns about potential unauthorized data transmission.
  • Shell: Executing processes using Popen without shell=True can be legitimate, but the incomplete 'start_new_session' parameter and potential for arbitrary command execution raise suspicion regarding possible unintended or malicious use.
  • Obfuscation: The code shows signs of obfuscation with unusual patterns and truncations, suggesting potential attempts to hide functionality.
  • Credentials: No clear indicators of credential harvesting or secret handling are present.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Ajatt-Tools/lancet#readme
  • Detailed PyPI description (5395 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 132 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Ajatt-Tools/lancet
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • (IPC_ENCODING) conn = http.client.HTTPConnection(IPC_HOST, self._cfg.bind_port, timeout=IPC_TIMEOUT_SEC)
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ion) yolov5s_backbone.eval() out_indices = [1, 3, 5, 7, 9] yolov5s_back
  • ET_MASK self.backbone.eval() self.seg_net.train() def initialize_db(self,
  • DET_DET self.backbone.eval() self.seg_net.eval() self.dbnet.train()
  • e.eval() self.seg_net.eval() self.dbnet.train() def forward(self, x):
  • half: return blk_det.eval().half(), text_seg.eval().half(), text_det.eval().half()
  • k_det.eval().half(), text_seg.eval().half(), text_det.eval().half() return blk_det.eval().t
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • application exit.""" _ = subprocess.Popen( args, shell=False, start_new_sessio
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Ajatt-Tools/lancet appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ajatt-Tools and contributors" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ajt-lancet 
Develop a manga-reading utility called 'MangaReader' that leverages the 'ajt-lancet' Python package for OCR functionality. This utility will enable users to capture and read text from manga images or scanned pages. Here's a step-by-step guide on what your application should include:

1. **User Interface Design**: Create a simple yet intuitive GUI using PyQt or Tkinter where users can interact with the application. The UI should allow users to select a manga image file, capture regions of interest (ROI) from the image, and view the recognized text.

2. **Image Selection**: Implement a feature within the UI that lets users upload a manga image file. Ensure the application supports common image formats such as JPEG, PNG, and GIF.

3. **Region Snipping Tool**: Utilize 'ajt-lancet' to provide an easy-to-use snipping tool for capturing text-rich regions within the manga image. Users should be able to draw a rectangle over the area of interest and have the text extracted automatically.

4. **Text Recognition**: Integrate the OCR capabilities of 'ajt-lancet' to recognize and display the captured text in real-time. The recognized text should be displayed below the selected region or in a separate window for clarity.

5. **Customization Options**: Allow users to adjust settings like language detection, font size, and color themes through a settings menu within the UI.

6. **Export Functionality**: Enable users to export the recognized text into a text file or directly copy it to the clipboard for further use.

7. **Error Handling**: Implement error handling to manage cases where the OCR process fails or when the input image is not supported.

8. **Testing and Optimization**: Test the application thoroughly with various manga images to ensure accurate text recognition and smooth user interaction. Optimize performance based on feedback and testing results.

By following these steps, you'll create a useful and engaging manga-reading utility that enhances the experience of manga enthusiasts by making text more accessible.

💬 Discussion Feed

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