AI Analysis
Final verdict: SAFE
The package shows no signs of malicious intent or obfuscation, and does not pose immediate risks such as credential harvesting. However, the metadata suggests a potentially unreliable maintainer.
- Low obfuscation risk
- No credential harvesting detected
- Unreliable maintainer metadata
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential unreliability.
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 (8838 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
85 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 33 commits in NayanKanaparthi/aigisTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository NayanKanaparthi/aigis appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 aigis-cli
Create a Python-based utility named 'ComplianceGuard' that integrates the 'aigis-cli' package to enforce security and compliance standards on AI-driven applications. This utility should serve as a tool for developers to ensure their code adheres to industry-standard frameworks such as NIST AI RMF, OWASP Top 10 for LLMs, and ISO/IEC 42001. The application will perform the following tasks: 1. **Code Analysis**: Implement a feature where users can input or upload their Python code files. ComplianceGuard will analyze these files using 'aigis-cli' to identify potential security vulnerabilities and compliance issues. 2. **Risk Assessment**: Based on the analysis, generate a risk assessment report detailing any identified risks and their severity according to the chosen framework(s). 3. **Remediation Suggestions**: Provide actionable remediation suggestions for each identified issue to help developers improve their code's security and compliance. 4. **Integration with CI/CD Pipelines**: Allow integration with popular CI/CD tools (e.g., Jenkins, GitHub Actions) so that ComplianceGuard can automatically run its checks during the build process, ensuring continuous compliance. 5. **User Interface**: Develop a simple command-line interface (CLI) for easy interaction with the tool, including options to specify the input file(s), selected compliance frameworks, and output formats for reports. 6. **Customization**: Enable customization of the compliance checks based on specific project requirements or additional guidelines not covered by default frameworks. In your development process, utilize the 'aigis-cli' package by calling its functions to perform the code analysis and generate reports. Ensure you document how 'aigis-cli' is integrated into each major functionality of the ComplianceGuard utility.
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