ai-audit-trail

v0.4.7 suspicious
4.0
Medium Risk

Cryptographically verifiable audit trail for AI systems — Ed25519 signed, hash-chained Decision Receipts with ISO 42001 / NIST AI RMF compliance mappings

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some suspicious behaviors, particularly concerning shell execution and obfuscation techniques, despite not showing strong indicators of malicious intent or credential harvesting.

  • Shell execution within the 'ai_audit' module
  • Use of base64 decoding for potential code or data obfuscation
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution is used to run internal commands, which seems legitimate but requires further investigation into the purpose and context of 'ai_audit' module.
  • Obfuscation: The use of base64 decoding might indicate an attempt to hide code or data, but without additional context, it could also be legitimate for data storage or transmission purposes.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The package shows some red flags such as an author with no name and a single package, but there are no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

  • Test runner config found: pyproject.toml
  • 17 test file(s) detected (e.g. test_backends_s3.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (18452 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
  • 225 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 42 commits in sundsoffice-tech/ai-audit-trail
  • Single author but highly active (42 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • """ try: raw = base64.b64decode(zip_b64, validate=True) except Exception as exc: # noqa
  • self._encrypted_seed = base64.b64decode(encrypted_seed_b64) self._region_name = region_name
  • ig(level=logging.INFO, stream=__import__("sys").stderr) logger.info("ai-audit-trail MCP server starting
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • letedProcess[str]: return subprocess.run( [sys.executable, "-m", "ai_audit", *args],
  • import sys result = subprocess.run( [sys.executable, "-m", "ai_audit", "verify", st
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://mypy-lang.org/
✓ Git Repository History

Repository sundsoffice-tech/ai-audit-trail appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 ai-audit-trail 
Create a mini-application named 'AI Integrity Monitor' that leverages the 'ai-audit-trail' package to ensure the integrity and accountability of AI decision-making processes. This application should serve as a tool for developers and auditors to monitor and verify the decisions made by AI models in real-time. The application should have the following core functionalities:

1. **Decision Logging**: Automatically log every decision made by an AI model into the audit trail. Each decision should be associated with a unique identifier, timestamp, input data, output data, and a context description.

2. **Cryptographic Verification**: Use 'ai-audit-trail' to cryptographically sign each decision record using Ed25519 signatures and chain them together using hash chaining to ensure the integrity of the audit trail.

3. **Compliance Mapping**: Map each decision to relevant sections of ISO 42001 and NIST AI RMF compliance standards to demonstrate adherence to these frameworks.

4. **Audit Trail Visualization**: Provide a user-friendly interface where users can visualize the audit trail, including the ability to filter decisions based on time, input data, output data, or compliance status.

5. **Alert System**: Implement an alert system that notifies users when a decision does not comply with the specified standards or if there are discrepancies in the audit trail.

6. **Report Generation**: Allow users to generate comprehensive reports detailing the compliance status of the AI model over a specified period.

The application should be designed to integrate seamlessly with existing AI systems, making it easy for developers to incorporate 'ai-audit-trail' functionality without significant changes to their workflow. Additionally, the application should provide clear documentation and examples to help users understand how to use and benefit from its features.