ares-redteamer

v0.2.1 suspicious
6.0
Medium Risk

AI Robustness Evaluation System

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits medium risk due to potential code injection via eval() and use of external tools for security analysis. Further investigation is required.

  • High obfuscation risk due to eval() usage
  • Use of external tools for security analysis
Per-check LLM notes
  • Network: The network call attempts to fetch content from a GitHub repository which seems standard for legitimate purposes like fetching documentation or configuration files.
  • Shell: Executing commands to check the version of tools like codeql, semgrep, spotbugs, and horusec suggests the package might be using these tools for security analysis, which is not inherently malicious but requires further scrutiny.
  • Obfuscation: The presence of eval() with user input suggests potential for code injection and obfuscation, indicating high risk.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer seems to be new and has only one package, which could indicate a low-risk scenario but warrants further investigation.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • 15 test file(s) detected (e.g. test_autodan.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (27996 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

  • 200 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in IBM/ares
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • = {} try: lines = requests.get( "https://raw.githubusercontent.com/IBM/ares/ref
  • try: readme = requests.get(url, timeout=10) except requests.exceptions.Time
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • , "origin_code": "eval(user_input)", "pattern_id": "CWE-95",
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ql": result = subprocess.run( ["codeql", "--version"], capture_output
  • ep": result = subprocess.run( ["semgrep", "--version"], capture_outpu
  • gs": result = subprocess.run( [self.spotbugs_path, "-version"], captu
  • ec": result = subprocess.run(["horusec", "version"], capture_output=True, text=True, time
  • yk": result = subprocess.run(["snyk", "--version"], capture_output=True, text=True, timeo
  • er": result = subprocess.run( ["insider", "--version"], capture_outpu
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ibm.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository IBM/ares appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Giandomenico Cornacchia" 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 ares-redteamer 
Create a robustness evaluation tool using the 'ares-redteamer' package in Python. This tool will serve as a comprehensive framework for assessing the resilience of machine learning models against adversarial attacks. The application should include the following key features:

1. **Model Importation**: Users should be able to import various types of pre-trained ML models (e.g., from TensorFlow, PyTorch).
2. **Attack Simulation**: Implement different types of adversarial attack methods (such as FGSM, PGD, etc.) to test model robustness.
3. **Performance Metrics**: Provide metrics such as accuracy, precision, recall, F1 score before and after the attacks.
4. **Visualization Tools**: Include visualizations that help users understand how adversarial examples affect model predictions.
5. **Custom Attack Creation**: Allow users to define their own adversarial attack strategies based on specific criteria.
6. **Report Generation**: Automatically generate detailed reports summarizing the evaluation results.

The 'ares-redteamer' package will be utilized for its core functionalities related to generating adversarial examples and evaluating the robustness of the models. Your task is to design a user-friendly interface that guides users through each step of the process, from importing a model to generating and analyzing adversarial attacks. Ensure that your application is well-documented and includes examples for common use cases.

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