aiwaf

v0.1.9.7.4 suspicious
5.0
Medium Risk

AI-driven, self-learning Web Application Firewall for Python web applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and code injection risks, raising concerns about its integrity and safety. However, there's no concrete evidence of malicious activity or supply-chain attack.

  • High obfuscation risk due to use of eval() and unusual import patterns
  • No direct evidence of credential harvesting or network/shell risks
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, which is expected unless the package requires executing external commands.
  • Obfuscation: The presence of eval() and unusual import patterns suggests potential for code injection or obfuscation, indicating a higher risk.
  • Credentials: No clear evidence of direct credential harvesting is observed, but caution is advised due to the suspicious obfuscation techniques.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (25664 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

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

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in aayushgauba/aiwaf
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • r=", "${", "{{", "eval(", ) PROBE_PATH_PATTERNS = ( r"(^|/)\.(env|git|htaccess
  • 'export_timestamp': __import__('time').time() } def __repr__(self) -> str:
  • r) -> Any: whois_module = __import__("whois") domain = _resolve_domain(target) return whois_modul
✓ 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: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://your-app.example/
✓ Git Repository History

Repository aayushgauba/aiwaf appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aayush Gauba" 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 aiwaf 
Create a fully-functional mini-web application using Flask that integrates the 'aiwaf' package as its primary security measure. This application will serve as a simple blog platform where users can post articles and comments. The core functionality of the app should include user registration, article posting, and commenting. However, the main focus will be on demonstrating how 'aiwaf' enhances security by automatically learning from traffic patterns and blocking malicious activities.

Steps to build the application:
1. Set up a Flask environment and install necessary packages including Flask and aiwaf.
2. Design the database schema to store user information, articles, and comments.
3. Implement user authentication and authorization functionalities to ensure only registered users can post articles and comments.
4. Integrate 'aiwaf' into your Flask application to monitor incoming requests and protect against SQL injection, XSS attacks, and other common web vulnerabilities.
5. Test the application by simulating various attack scenarios to observe how 'aiwaf' adapts and learns over time to improve security.
6. Document the process of integrating 'aiwaf', explaining how each feature of the package contributes to the overall security of the application.
7. Deploy the application on a local server or a cloud service provider for further testing and demonstration purposes.

Suggested Features:
- User Registration and Login: Allow users to create accounts and log in to post content.
- Article Posting: Users can write and publish articles.
- Commenting System: Readers can leave comments on articles.
- Real-time Monitoring: Display a dashboard showing real-time traffic and security alerts.
- Learning Mode: Enable 'aiwaf' to learn from normal traffic and adapt its rules accordingly.
- Attack Simulation: Include a feature to simulate common web attacks and demonstrate 'aiwaf's response.

How to Utilize 'aiwaf':
- Configure 'aiwaf' to scan all incoming HTTP requests and responses.
- Use 'aiwaf' to automatically detect and block suspicious activities based on predefined rules and machine learning models.
- Regularly review the logs generated by 'aiwaf' to understand its decision-making process and refine security policies if necessary.

💬 Discussion Feed

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