ainfera-verify

v0.1.0 suspicious
6.0
Medium Risk

Offline verifier for Ainfera AuditChains. Trust no one — verify the chain yourself.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and has metadata risks such as a lack of maintainer history and minimal repository engagement, suggesting it may not be trustworthy.

  • High metadata risk
  • Potential obfuscation
Per-check LLM notes
  • Network: Network calls to external services are present but without clear context, raising some suspicion.
  • Shell: No shell execution patterns detected, indicating low risk.
  • Obfuscation: The presence of base64 decoding suggests possible obfuscation, but without more context, it could also be legitimate use such as handling encoded data.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The package is suspicious due to lack of maintainer history, minimal repository engagement, and a newly registered author with limited package involvement.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://ainfera.ai/docs/verify
  • Detailed PyPI description (2369 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

  • 22 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 22 commits in ainfera-ai/verify
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • s None client = client or httpx.Client(timeout=15.0) try: resp = client.get(well_known_
  • s None client = client or httpx.Client(timeout=30.0) events: list[AuditEvent] = [] cursor:
  • s None client = client or httpx.Client(timeout=15.0) try: try: resp = clien
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • mac_key_b64'") hmac_key = base64.b64decode(key_b64) events: list[AuditEvent] = [] for line_num
  • {well_known_url}") return base64.b64decode(key_b64) def fetch_chain( agent_id: str, *, ba
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ainfera.ai>

✓ 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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 ainfera-verify 
Create a mini-application named 'AuditChainVerifier' using the Python package 'ainfera-verify'. This tool will serve as an offline auditor for verifying the integrity of AuditChains provided by Ainfera. Your goal is to develop a command-line interface (CLI) tool that allows users to input an AuditChain and receive a verified status indicating whether the chain is authentic or not. Additionally, the application should provide detailed insights into any discrepancies found within the chain.

Steps to complete the project:
1. Set up a virtual environment and install 'ainfera-verify'.
2. Design the CLI structure allowing users to input an AuditChain file or URL.
3. Implement a verification function that uses 'ainfera-verify' to validate the AuditChain.
4. Develop a reporting feature that outputs the verification result and highlights any issues found.
5. Add error handling to manage incorrect inputs or network failures gracefully.
6. Include documentation and examples on how to use the tool effectively.

Suggested Features:
- Support for multiple file formats (JSON, XML).
- Option to save the verification report to a file.
- Real-time progress updates during the verification process.
- Compatibility with both local files and remote URLs.

The 'ainfera-verify' package is crucial here as it provides the necessary functions to parse, analyze, and authenticate AuditChains without relying on external services, ensuring privacy and security.

💬 Discussion Feed

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