awesome-ai-governance-toolkit

v1.0.1 suspicious
4.0
Medium Risk

Runtime firewall for LLMs — policy-as-code, PII scrubbing, SHA-256 audit chain and HITL dashboard. EU AI Act + NIST AI RMF compliant.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse and lacks thorough documentation for its network calls and shell commands, raising suspicion despite the lack of direct evidence of malicious intent.

  • Network risk due to undocumented external API calls
  • Shell risk from running services like Uvicorn and Streamlit
Per-check LLM notes
  • Network: The network call to an external API might be legitimate if the package is designed to interact with a service, but it should be thoroughly documented.
  • Shell: Running services like Uvicorn and Streamlit could be part of the package's functionality, but it raises concerns about potential unintended side effects or misuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The short commit history and new maintainer account suggest potential risk, but there's no clear evidence of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

○ 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/Aryanshanu/awesome-ai-governance-toolkit#
  • Detailed PyPI description (17903 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

  • 20 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in Aryanshanu/awesome-ai-governance-toolkit
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: resp = requests.post( f"{API_BASE}/v1/intercept",
  • None: try: resp = requests.get( url, timeout=8, headers
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • uvicorn on port 8000).""" subprocess.run( [sys.executable, "-m", "uvicorn", "src.main:app", "
  • reamlit on port 8501).""" subprocess.run( [sys.executable, "-m", "streamlit", "run", "dashboa
✓ 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: All 10 commits happened within 24 hours

  • All 10 commits happened within 24 hours
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Aryanshanu" 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 awesome-ai-governance-toolkit 
Create a privacy-focused document review tool named 'PrivacyGuard' using the Python package 'awesome-ai-governance-toolkit'. This tool will serve as a comprehensive solution for businesses and organizations to ensure compliance with data protection regulations such as the EU AI Act and NIST AI RMF standards while reviewing sensitive documents. PrivacyGuard will integrate the following core functionalities provided by the 'awesome-ai-governance-toolkit' package:

1. **Policy Enforcement**: Implement a runtime firewall that enforces policies defined via code. Users should be able to create and manage policies that restrict the use of sensitive information within the documents being reviewed.
2. **PII Scrubbing**: Develop a feature that automatically identifies and removes personally identifiable information (PII) from documents before they are reviewed. Ensure that this process is reversible if needed, allowing users to restore the original content under strict conditions.
3. **Audit Trail**: Utilize SHA-256 hashing to maintain an immutable audit trail of all actions performed on the documents, including who accessed them, when, and what changes were made. This will help in maintaining accountability and ensuring compliance.
4. **Human-in-the-Loop (HITL) Dashboard**: Create an intuitive dashboard where human reviewers can interact with the AI's suggestions and decisions. This dashboard should allow reviewers to override the AI's actions if necessary and provide feedback to improve future performance.

To achieve these objectives, you will need to install and configure the 'awesome-ai-governance-toolkit' package properly. Your task is to design and implement PrivacyGuard from scratch, ensuring it adheres to best practices in software development and leverages the capabilities of the 'awesome-ai-governance-toolkit' to its fullest extent. Additionally, include a user guide and documentation that explains how each feature works and how to set up and use PrivacyGuard effectively.

💬 Discussion Feed

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