AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate risk due to potential network and shell execution risks, although there are no clear signs of malicious activity such as obfuscation or credential harvesting.
- Moderate network risk
- High shell execution risk
Per-check LLM notes
- Network: Network calls could be legitimate if the package involves API interactions, but require further investigation to ensure they are not used for unauthorized data transmission.
- Shell: Shell executions pose a higher risk as they can execute arbitrary commands on the system, which may indicate potential misuse or vulnerabilities.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Medium (5.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/Principled-Evolution/aicertify#readmeDetailed PyPI description (10602 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
243 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in Principled-Evolution/aicertifySmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
server response = requests.post( query_url, json=request_pary: response = requests.put(url, data=policy.content, headers=headers) i
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 8.0
Found 4 shell execution pattern(s)
( subprocess.check_output(["wslpath", "-w", abs_path]) .de: subprocess.run( ["xdg-open", file_url], checmd)}") result = subprocess.run(cmd, capture_output=True, text=True, check=True)(cmd)}") result = subprocess.run(cmd, capture_output=True, text=True, check=False)
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Principled-Evolution/aicertify appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Kapil Madan" 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 aicertify
Develop a Python-based mini-application called 'AIComplianceChecker' that leverages the 'aicertify' package to help users ensure their AI systems comply with various international regulations. The application should allow users to input details about their AI system, such as the type of AI, its purpose, and the data it processes. It will then use 'aicertify' to automatically audit the AI system against the EU AI Act, NIST AI RMF, and other relevant regulatory frameworks. Users should be able to generate compliance reports in multiple formats, including PDF, Markdown, JSON, and HTML, which they can use for internal audits or external compliance reviews. Additionally, the application should provide recommendations for improving compliance based on the audit results. The application should include a user-friendly interface, possibly through a command-line tool or a simple web front-end, to facilitate interaction and ease of use.