auditml

v0.1.0 suspicious
5.0
Medium Risk

A privacy auditing toolkit for PyTorch machine learning models.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package auditml v0.1.0 has a moderate risk score due to its newly created status with limited maintainer activity, which raises concerns about its reliability and long-term support.

  • Metadata risk indicates a new package with limited maintainer activity
  • Obfuscation patterns detected but deemed non-malicious
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package relies on external services.
  • Shell: No shell execution detected, indicating the package does not execute system commands.
  • Obfuscation: The obfuscation patterns detected appear to be related to model evaluation in machine learning contexts and do not indicate malicious intent.
  • Credentials: No credential harvesting patterns were detected.
  • Metadata: The package shows signs of being newly created with limited maintainer activity, which could indicate potential risk.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://eemanasghar.github.io/AuditML-Privacy-Toolkit/
  • Detailed PyPI description (5719 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 41 commits in EemanAsghar/AuditML-Privacy-Toolkit
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ) model.eval() return model # ------------------------------
  • """ self.attack_model.eval() x = torch.tensor(probs, dtype=torch.float32).to(se
  • ") self.attack_model.eval() x = torch.tensor(probs, dtype=torch.float32).to(se
  • being attacked. Must be in ``eval()`` mode. config: Optional AuditML configuration
  • del self.target_model.eval() # always eval mode for attacks self.config = conf
  • tience=0) shadow.eval() self.trained_shadows.append(shadow)
✓ 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: student.uet.edu.pk>

✓ 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 auditml 
Develop a privacy-preserving data analysis tool using the 'auditml' Python package. This tool will help researchers and developers ensure that their machine learning models trained on sensitive data do not leak information about individual records during inference. Here’s a detailed plan on how to build this application:

1. **Project Overview**: Create a command-line interface (CLI) tool named 'PrivacyGuard'. This tool will allow users to load their pre-trained PyTorch models, analyze them for potential privacy risks, and generate reports summarizing the findings.

2. **Core Features**:
   - **Model Loading**: Users should be able to load any PyTorch model from a specified path.
   - **Privacy Audit**: Utilize 'auditml' to perform various privacy audits on the loaded model, such as membership inference attacks, property inference attacks, etc.
   - **Report Generation**: Automatically generate a detailed report that includes the results of the privacy audits, along with recommendations for mitigating identified risks.

3. **Detailed Steps**:
   - Step 1: Install necessary packages including 'auditml', 'torch', and 'numpy'.
   - Step 2: Design the CLI structure allowing for commands like 'load_model', 'run_audit', and 'generate_report'.
   - Step 3: Implement a function within 'PrivacyGuard' that uses 'auditml' to conduct membership inference attacks on the loaded model.
   - Step 4: Similarly, implement other types of audits supported by 'auditml', such as property inference attacks.
   - Step 5: Develop a reporting mechanism that summarizes the audit results and suggests ways to improve the model's privacy posture.

4. **Example Use Case**: A researcher has developed a facial recognition model using PyTorch and wants to ensure that the model does not inadvertently reveal whether a specific person was part of the training dataset. They would use 'PrivacyGuard' to run a series of privacy audits and receive a comprehensive report detailing any potential vulnerabilities.

5. **Utilization of 'auditml'**: Throughout the development process, 'auditml' will be the backbone of the privacy auditing functionality. It will provide the necessary tools and methods to assess the privacy risks associated with the machine learning models, ensuring that the tool is both effective and reliable.

By following these steps, you will create a powerful and user-friendly tool that leverages 'auditml' to enhance the privacy of machine learning models.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!