auditize-cli

v0.1.1 suspicious
4.0
Medium Risk

Deterministic security scanner for AI-generated projects. Scan any project, get a prioritized action plan — so you review 3 files instead of 300.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its newly created repository with limited activity, which raises concerns about potential supply-chain attacks despite no direct evidence of malicious activities.

  • Recent repository creation and low activity
  • Potential supply-chain attack concern
Per-check LLM notes
  • Network: No network calls detected, indicating low risk of data exfiltration or C2.
  • Shell: Git commands are likely used for version control purposes and don't inherently suggest malicious intent, but further investigation into their usage context is recommended.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's recent creation and low activity suggest potential risk, but lack of evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Low (4.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/yizhizhu222/auditize/tree/main/cli/rules
  • Detailed PyPI description (7217 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
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 14 commits in yizhizhu222/auditize
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • try: r = subprocess.run( ["git", "-C", str(root), "ls-files", ".env"
  • in patterns: r = subprocess.run( ["git", "-C", str(root), "log", "--diff-fil
  • try: r = subprocess.run( ["git", "-C", str(root), "ls-files", dirnam
✓ 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 5.0

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-03T14:27:33Z)

  • Repository created very recently: 4 day(s) ago (2026-06-03T14:27:33Z)
  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Auditize" 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 auditize-cli 
Create a Python-based desktop application named 'AI-SafeGuard' which leverages the 'auditize-cli' package to scan local machine learning projects for potential security vulnerabilities. The application should be user-friendly and efficient, allowing users to input the path to their project directory and receive a prioritized list of actions they need to take to secure their project. The core functionalities of the app should include:

1. User Interface: Design a simple GUI using PyQt5 where users can select the project directory they want to scan.
2. Project Scanning: Utilize the 'auditize-cli' package to scan the selected project for security issues. Ensure that the scanning process is automated within the app.
3. Prioritization Algorithm: Implement a feature that takes the output from 'auditize-cli' and applies a custom algorithm to prioritize the identified issues based on severity and impact. This will help users focus on the most critical fixes first.
4. Action Plan Generation: Based on the prioritization, generate an actionable report for the user that outlines steps to address each issue, including links to relevant documentation or tutorials.
5. Notifications: Integrate a notification system that alerts the user once the scan is complete and provides a summary of the findings.
6. Save & Export: Allow users to save the generated action plan as a PDF or Markdown file for future reference.
7. Continuous Updates: Ensure the application can check for updates to 'auditize-cli' and notify users if there's a newer version available.

The goal of 'AI-SafeGuard' is to make it easier for developers working on AI projects to ensure their work is secure without needing deep knowledge about security practices. The use of 'auditize-cli' ensures that the scanning process is thorough yet manageable, focusing on the most impactful changes first.

💬 Discussion Feed

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