aetherproof

v0.2.0 suspicious
6.0
Medium Risk

Cryptographic, tamper-evident execution receipts for any AI model

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to significant network and shell command usage, which could potentially lead to unauthorized data transmission and arbitrary code execution. The low metadata risk score is mitigated by the new maintainer status and lack of community engagement.

  • High network risk
  • High shell risk
  • New maintainer with limited history
Per-check LLM notes
  • Network: The presence of network calls without clear documentation can indicate potential unauthorized data transmission.
  • Shell: Execution of shell commands can be high risk if not properly controlled and documented, suggesting possible execution of arbitrary code.
  • Metadata: The package is from a new maintainer with limited history and no community engagement, raising some suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ad).encode("utf-8") req = urllib.request.Request( url, data=data, headers={"Content-Type": "a
  • method="POST" ) with urllib.request.urlopen(req, timeout=timeout) as resp: return json.l
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • orm encoding.""" result = subprocess.run(args, capture_output=True, **kwargs) result._stdout_text
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Pulkit" 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 aetherproof 
Create a mini-application called 'AI Receipt Verifier' that leverages the 'aetherproof' package to generate and verify cryptographic receipts for AI model executions. This application should serve as a trustless mechanism to ensure that AI models have been executed correctly without any tampering. Here are the steps and features your application should include:

1. **Setup**: Initialize your project with the necessary dependencies including 'aetherproof'. Ensure you have a Python environment set up.
2. **Model Execution**: Integrate an existing AI model (such as one from Hugging Face's Transformers library) into your application. This will serve as the base AI model that users can execute.
3. **Receipt Generation**: When a user requests an execution of the AI model, your application should use 'aetherproof' to create a cryptographic receipt that includes details about the input data, output data, and the model itself.
4. **Verification Process**: Provide functionality within the application to allow users to upload a previously generated receipt and verify its authenticity. This process should confirm that the receipt has not been altered since it was created.
5. **User Interface**: Develop a simple web interface using Flask or Django where users can interact with the application. They should be able to select a model, input their data, request a receipt, and later upload a receipt for verification.
6. **Documentation**: Write comprehensive documentation explaining how to install and use the application, including examples of generating and verifying receipts.
7. **Security Considerations**: Ensure that all sensitive information, such as cryptographic keys, is handled securely. Discuss security best practices in your documentation.
8. **Testing**: Implement unit tests and integration tests to ensure that receipt generation and verification processes work as expected.

By completing these steps, your application will provide a practical demonstration of how 'aetherproof' can be used to enhance the transparency and reliability of AI model executions.