annihilation-llm

v1.3.0 suspicious
6.0
Medium Risk

Fully automatic censorship removal for language models

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential credential harvesting activities and lack of repository information. While there is no direct evidence of malicious intent, the combination of signals raises concern.

  • Potential credential harvesting activities via getpass.getpass and questionary.password
  • Repository not found, raising suspicion about the maintainer's credibility
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell commands appear to be checking GPU driver versions and might be related to the package's functionality rather than malicious activity.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The presence of getpass.getpass and questionary.password indicates potential credential harvesting activities.
  • Metadata: The repository is not found, and the maintainer seems new with limited history, which raises suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/annihilation-llm/annihilation
  • Detailed PyPI description (5775 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ne: try: output = subprocess.check_output( ["nvidia-smi", "--query-gpu=driver_version", "-
  • ne: try: output = subprocess.check_output( ["amd-smi", "version"], stderr=subp
  • ss try: output = subprocess.check_output( ["rocm-smi", "--showdriverversion"],
  • ne: try: output = subprocess.check_output( ["xpu-smi", "discovery"], stderr=su
  • ne: try: output = subprocess.check_output( ["npu-smi", "info", "-t", "board", "-i", "0"],
  • ne: try: output = subprocess.check_output( ["sw_vers", "-productVersion"], std
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • print() return getpass.getpass(message) else: return questionary.password(messa
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: worldwidemann.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Philipp Emanuel Weidmann" 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 annihilation-llm 
Develop a Python-based web application named 'FreedomSpeak' that leverages the 'annihilation-llm' package to remove censorship from text inputs provided by users. This application will serve as a tool for individuals living in regions with strict internet censorship, enabling them to express themselves more freely. Here are the steps and features you should include:

1. **User Interface**: Design a simple yet intuitive user interface using Flask or Django, where users can input their censored texts.
2. **Censorship Removal Engine**: Utilize the 'annihilation-llm' package to process these texts and remove any detected censorship patterns. Ensure that the package is integrated seamlessly into your backend logic.
3. **Output Display**: After processing, display the uncensored version of the text back to the user, along with an option to download it.
4. **User Feedback Mechanism**: Implement a feature allowing users to rate the effectiveness of the censorship removal on a scale of 1 to 5 stars, providing valuable feedback for improving the model.
5. **Security Measures**: Incorporate basic security measures such as rate limiting and input validation to protect against malicious use.
6. **Documentation**: Provide comprehensive documentation explaining how to use the application, including setup instructions and API usage details if applicable.
7. **Testing**: Conduct thorough testing of the application, focusing on both functionality and performance under various conditions.
8. **Deployment**: Finally, deploy the application on a cloud platform like AWS or Heroku, ensuring it is accessible worldwide.

By following these steps, you'll create a powerful tool that not only demonstrates the capabilities of 'annihilation-llm' but also serves a meaningful purpose in supporting free speech.

💬 Discussion Feed

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