🤖 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/aigis
Two 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 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 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.