anycv-sdk

v0.1.0 suspicious
6.0
Medium Risk

适用于AnyCV视觉识别算法的统一SDK和FastAPI服务

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to its potential for network and shell command execution, which could lead to unauthorized actions if misused.

  • High shell risk indicating potential for arbitrary code execution
  • Moderate network risk suggesting possible unauthorized data transfer
Per-check LLM notes
  • Network: Network calls to external endpoints could indicate legitimate functionality like API interactions but may also suggest unauthorized data transfer.
  • Shell: Executing shell commands can be necessary for some applications but poses a high risk if not properly controlled, potentially allowing arbitrary code execution.
  • Obfuscation: The base64 decoding might indicate some level of obfuscation, but it is also commonly used for handling image data or other binary formats.
  • Credentials: No suspicious patterns related to credential harvesting were found.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. test_core.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8974 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

  • 86 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • e 等配置。 response = requests.post( endpoint, files={"file": ("
  • 的启动负载。 response = requests.post( endpoint, json={**request._
  • e/msg。 response = requests.delete(endpoint, timeout=10) response.raise_for_status(
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • decoded = decode_image(base64.b64decode(encoded)) self.assertEqual(decoded.shape[0], 16)
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: process = subprocess.Popen(command, **popen_kwargs) except Exception as exc:
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000/`
  • Non-HTTPS external link: http://127.0.0.1:8000/api/alg/v1/health`
  • Non-HTTPS external link: http://127.0.0.1:8000/docs`
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 anycv-sdk 
Create a web-based image recognition tool using the 'anycv-sdk' Python package. This tool will allow users to upload images and receive visual analysis results, such as object detection, face recognition, and scene classification. The application should be built on FastAPI and deployed on a local server for testing purposes. Here are the steps and features to include:

1. **Setup**: Install necessary packages including 'anycv-sdk', FastAPI, and any required dependencies.
2. **Image Upload**: Implement a simple UI where users can upload their images. Ensure that the application supports various image formats.
3. **Processing**: Use 'anycv-sdk' to process the uploaded images. Focus on three main tasks - Object Detection, Face Recognition, and Scene Classification.
4. **Results Display**: Present the results of the analysis back to the user through the UI. For each task, display relevant information like bounding boxes for objects detected, faces identified, and descriptions of scenes.
5. **Error Handling**: Add robust error handling to manage cases where the image upload fails or the analysis cannot be completed due to issues with the SDK or image content.
6. **Documentation**: Provide comprehensive documentation on how to run the application locally, including setup instructions and usage examples.
7. **Testing**: Test the application thoroughly with different types of images to ensure it works correctly across various scenarios.
8. **Deployment**: Although deployment is optional for this project, consider discussing potential deployment strategies if you were to make the app available publicly.

By following these steps, you'll create a powerful yet accessible tool that showcases the capabilities of 'anycv-sdk'.

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