🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential usage, but the metadata lacks critical information such as repository and author details, raising concerns about its origin and purpose.

Missing repository and author details
Suspicious metadata

Per-check LLM notes

Network: The network call pattern suggests the package is likely making legitimate HTTP requests to an API endpoint, which is common for SDKs.
Shell: No shell execution patterns were detected, indicating no risk from this perspective.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is suspicious due to the missing repository and author details, indicating potential lack of transparency.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

self._http_client = httpx.AsyncClient( base_url=self._base_url,

✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 aegis-ai-sdk

Develop a Python-based mini-application called 'AI Compliance Monitor' using the Aegis-AI-SDK package, which focuses on monitoring and ensuring compliance of AI models within an organization. This tool will serve as a critical component in an organization's governance infrastructure, enabling users to audit, evaluate, and manage their AI systems effectively.

The application should have the following core functionalities:
1. **Model Audit**: Allow users to input details about their AI models (e.g., model name, version, type of data it processes, etc.) and run compliance checks based on predefined criteria.
2. **Bias Detection**: Utilize the Aegis-AI-SDK to perform bias detection on AI models, identifying any potential biases in the model's decision-making process.
3. **Explainability Reports**: Generate reports that provide explanations for the model's decisions, helping stakeholders understand how the model arrived at certain conclusions.
4. **Compliance Dashboard**: Create a user-friendly dashboard where users can view the status of all monitored models, including compliance scores, detected issues, and recommended actions.
5. **Alert System**: Implement an alert system that notifies users via email or SMS when a model fails a compliance check or when new updates to compliance regulations become available.

**How to Use Aegis-AI-SDK**:
- For Model Audit, use the Aegis-AI-SDK's auditing tools to assess whether the model adheres to specified standards.
- To detect bias, leverage the SDK's bias detection algorithms provided in the package.
- For generating explainability reports, utilize the SDK's explanation generation capabilities to create detailed reports.
- Integrate the SDK's monitoring features into the dashboard to keep track of compliance statuses.
- Use the SDK's notification services to set up the alert system.

This project aims to demonstrate how the Aegis-AI-SDK can be effectively utilized to ensure the ethical and compliant use of AI technologies within an organization.