auditorai

v0.2.0 suspicious
6.0
Medium Risk

Universal auditor model that suppresses unreliable AI predictions in human-AI systems. Works with sklearn, PyTorch, HuggingFace, OpenAI, Anthropic, and any custom model.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to high network and metadata risks, suggesting potential data exfiltration or unusual developer behavior.

  • High network risk indicating possible data exfiltration
  • High metadata risk due to recent and rapid commits
Per-check LLM notes
  • Network: The presence of network calls suggests the package may be sending data to an external endpoint, which could indicate data exfiltration or other forms of communication.
  • Shell: No shell execution patterns were detected, indicating a lower risk of direct system compromise through command execution.
  • Obfuscation: The obfuscation patterns detected seem to be related to common practices in machine learning code for setting the model to evaluation mode and disabling gradient calculations, which are not inherently malicious.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The repository's recent creation and rapid commit history suggest potential suspicious activity.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_auditor.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Apurva0614/Auditorai#readme
  • Detailed PyPI description (10038 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

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

Active multi-contributor project

  • 3 unique contributor(s) across 34 commits in Apurva0614/Auditorai
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • der(text) response = requests.post( self.endpoint_url, json=body,
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • lf.device) self.model.eval() all_probas = [] with torch.no_grad():
  • lf.device) self.model.eval() def _get_device(self, device: str) -> str: ""
  • as numpy. Sets model.eval() and uses torch.no_grad() automatically. """
  • tensor(X) self.model.eval() all_outputs = [] with torch.no_grad():
✓ 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

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 6 day(s) ago (2026-06-01T20:12:18Z)

  • Repository created very recently: 6 day(s) ago (2026-06-01T20:12:18Z)
  • Repository has zero stars and zero forks
  • All 34 commits happened within 24 hours
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 auditorai 
Create a Python-based mini-application called 'ReliableChat' that leverages the 'auditorai' package to ensure the reliability of responses from an AI chatbot system. The goal is to build a chatbot that can interact with users in a natural language setting while ensuring that the responses provided are reliable and trustworthy.

Step 1: Set up the environment
- Install necessary packages including auditorai, transformers, and flask.
- Configure auditorai to work seamlessly with a pre-trained HuggingFace transformer model for generating responses.

Step 2: Design the chatbot architecture
- Develop a simple Flask web server that accepts user inputs and sends them to the chatbot model.
- Implement the auditorai package to evaluate the output of the chatbot model before sending it back to the user.
- If auditorai flags a response as unreliable, the system should generate an alternative response or notify the user about the unreliability.

Step 3: User interface
- Create a basic HTML/CSS frontend for the chatbot where users can input their queries and receive responses.
- Ensure the UI reflects the status of the response evaluation by auditorai, e.g., displaying a loading spinner while waiting for auditorai's verdict.

Suggested Features:
- Ability to flag specific types of unreliable responses (e.g., nonsensical answers, offensive content).
- Logging of all interactions for auditing purposes.
- Option for users to rate the reliability of responses, which could be used to improve the auditorai model over time.

Utilizing auditorai involves integrating its prediction suppression mechanism into the chatbot's workflow. This means running each generated response through auditorai's evaluation process. If auditorai deems a response unreliable, the application should handle this by either requesting a new response from the model or providing a fallback message to the user indicating the issue.

💬 Discussion Feed

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