annexkit

v0.1.3 suspicious
4.0
Medium Risk

EU AI Act compliance pipeline for developers — Python SDK

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package annexkit v0.1.3 is deemed suspicious due to its new maintainer account and lack of detailed author information, despite showing no signs of malicious activity within its codebase.

  • Metadata risk due to new/inactive maintainer account
  • Lack of detailed author information
Per-check LLM notes
  • Network: The observed network patterns are consistent with the use of HTTPX for testing purposes, which is not inherently suspicious but should be reviewed for context.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential unreliability.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://annexkit.dev/docs
  • Detailed PyPI description (7898 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 51 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 34 commits in annexkit/annexkit
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • r tests — pass # an ``httpx.Client(transport=httpx.MockTransport(...))`` and you # get
  • self._client = client or httpx.Client(timeout=timeout) # Headers are applied per-request (
  • ansport(handler) client = httpx.Client(transport=transport) exporter = HttpExporter( ap
  • "unauthorised") client = httpx.Client(transport=httpx.MockTransport(handler)) exporter = HttpE
  • ction refused") client = httpx.Client(transport=httpx.MockTransport(handler)) exporter = HttpE
  • x.Response(202) client = httpx.Client(transport=httpx.MockTransport(handler)) exporter = HttpE
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: annexkit.dev>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository annexkit/annexkit 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 annexkit 
Create a mini-application named 'ComplianceChecker' that leverages the 'annexkit' Python package to help developers ensure their AI applications comply with the EU AI Act. This tool should serve as a preliminary check before deploying AI models in regions where the EU AI Act applies. The application will guide users through a series of checks and provide feedback on areas needing improvement to meet regulatory standards.

Step 1: User Input - The application starts by prompting the user to input details about their AI model such as its purpose, intended use, and any data sources.

Step 2: Compliance Check - Using 'annexkit', the application performs a series of automated checks against the EU AI Act requirements. These checks include assessing data quality, ensuring transparency, evaluating robustness, and verifying accountability measures.

Step 3: Feedback Report - Based on the results of the compliance checks, the application generates a detailed report highlighting any discrepancies and offering suggestions on how to address them. The report should also include references to relevant sections of the EU AI Act for further reading.

Suggested Features:
- A user-friendly interface for easy data entry.
- Integration with popular AI frameworks like TensorFlow or PyTorch to streamline the process.
- Option to save and export compliance reports for record-keeping.
- Real-time feedback during the input phase to guide users towards compliant practices.

How to Utilize 'annexkit':
- Import necessary functions from 'annexkit' to perform the compliance checks.
- Use 'annexkit' APIs to validate data quality and model robustness according to EU AI Act guidelines.
- Leverage 'annexkit' documentation and examples to structure the feedback report and ensure accuracy in compliance advice.

💬 Discussion Feed

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