attestplane

v1.10.0 safe
2.0
Low Risk

Apache-2.0 attestation and audit substrate for AI agent evidence chains.

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on low risk scores across all categories, with no suspicious activities detected.

  • Low network and obfuscation risks
  • No shell execution or credential harvesting attempts
Per-check LLM notes
  • Network: Network calls are likely legitimate if the package is designed to interact with external services.
  • Shell: No shell execution patterns detected.
  • Obfuscation: Base64 decoding with validation is commonly used for handling encoded data, likely not malicious.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags were found.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/attestplane/attestplane/tree/main/sdk/pyt
  • Detailed PyPI description (7555 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 272 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in attestplane/attestplane
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • 30.0) -> bytes: req = urllib.request.Request( # noqa: S310 (URL schemes validated upstream in c
  • try: with urllib.request.urlopen(req, timeout=timeout_seconds) as resp: # noqa: S310
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: public_key_der = base64.b64decode(der_b64_raw, validate=True) except Exception as exc:
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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 attestplane/attestplane appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "The Attestplane Authors" 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 attestplane 
Develop a mini-application named 'AI Audit Tracker' that leverages the 'attestplane' package to manage and verify the integrity of AI-generated content across various platforms. This application will serve as a proof-of-concept for tracking, auditing, and ensuring the trustworthiness of AI-produced data and models. Here’s a detailed breakdown of the project requirements and steps:

1. **Setup Project Environment**: Begin by setting up your development environment with Python 3.x, and install the necessary packages including 'attestplane'. Ensure you have a virtual environment set up for dependency management.

2. **Define Core Functionality**: The core functionality of 'AI Audit Tracker' revolves around creating and managing 'evidence chains' for AI-generated content. Each chain should consist of multiple nodes, where each node represents a piece of data (e.g., text, images, models) along with metadata about its origin, transformations, and current state.

3. **Implement Attestation Mechanisms**: Utilize 'attestplane' to implement mechanisms for creating attestations. These attestations should cryptographically sign each node in the evidence chain, proving the integrity and origin of the data at each stage. Additionally, provide a way to verify these attestations using the public key associated with the signer.

4. **Audit Trail Feature**: Integrate an audit trail feature that logs all changes made to the evidence chains, including who made the change, when it was made, and what exactly was altered. This will help in maintaining transparency and accountability.

5. **User Interface**: Develop a simple web-based UI using Flask or Django to interact with the 'AI Audit Tracker'. The UI should allow users to upload new pieces of data, view existing evidence chains, and inspect the audit trail. For simplicity, focus on basic CRUD operations for managing evidence chains.

6. **Security Considerations**: Since the application deals with cryptographic signatures and sensitive data, ensure that security is a top priority. Implement best practices such as secure key storage, proper handling of user inputs, and encryption of sensitive data.

7. **Testing and Documentation**: Write comprehensive tests for each module of your application to ensure reliability and correctness. Also, document your code thoroughly, explaining how each component works and how 'attestplane' is integrated into the system.

8. **Deployment**: Once developed and tested, deploy your application to a cloud platform like AWS or Heroku. Ensure that the deployment process is automated using CI/CD pipelines for easy maintenance and scaling.

By following these steps, you'll create a robust, secure, and useful tool for managing and verifying AI-generated content. This project not only showcases the capabilities of 'attestplane' but also provides a practical solution for enhancing trust in AI systems.

💬 Discussion Feed

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