auditai-client

v1.0.1 safe
3.0
Low Risk

AuditAI – AI Reliability & Compliance Auditor SDK

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all checks with no signs of obfuscation or credential harvesting. The metadata risk is slightly elevated due to low activity and missing classifiers, but there are no clear malicious indicators.

  • No obfuscation patterns detected
  • No credential harvesting patterns detected
  • Low metadata activity
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized data collection.
  • Metadata: Low activity and lack of classifiers suggest low effort, but no clear malicious indicators.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (1110 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

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • to urllib req = urllib.request.Request( url, data=json.du
  • try: with urllib.request.urlopen(req, timeout=30) as resp: retur
  • else: req = urllib.request.Request(url, headers=self._headers()) with urll
  • _headers()) with urllib.request.urlopen(req, timeout=30) as resp: return js
  • requests: resp = requests.post(url, json=data, headers=self._headers(), timeout=30)
  • requests: resp = requests.get(url, headers=self._headers(), timeout=30) resp.
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "AuditAI Team" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with auditai-client 
Create a Python-based mini-application named 'ComplianceChecker' that leverages the 'auditai-client' package to evaluate the reliability and compliance of AI models used within your organization. This application should serve as a tool for developers and data scientists to ensure their AI models adhere to internal standards and external regulations.

**Features:**
1. **Model Evaluation:** Users should be able to input details about an AI model (such as the model name, version, and deployment environment) into the application. The application will then use the 'auditai-client' package to assess the model's compliance with specified criteria.
2. **Report Generation:** Upon evaluation, the application should generate a detailed report outlining any issues found with the model's compliance and reliability. This report should include recommendations for improvement and should be exportable as a PDF or HTML file.
3. **Integration with CI/CD Pipelines:** The application should have the capability to integrate seamlessly with Continuous Integration/Continuous Deployment (CI/CD) pipelines, automatically evaluating models at specific stages of the development process.
4. **User Interface:** Develop a simple web interface using Flask or Django that allows users to submit model information, view evaluation results, and download reports.
5. **Customizable Criteria:** Allow users to define their own compliance criteria through a configuration file, ensuring the application can adapt to different regulatory environments.

**Utilization of 'auditai-client':** The 'auditai-client' package will be used primarily for assessing the reliability and compliance of AI models. Specifically, you will utilize its functions to perform audits on the models based on user-defined criteria, retrieve detailed audit results, and generate compliance reports. Additionally, explore how 'auditai-client' can assist in automating the compliance checking process during the continuous integration phase.

💬 Discussion Feed

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