🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several red flags such as a non-existent git repository, a single version release, and limited author information, raising concerns about its legitimacy and potential for supply-chain attacks.

Non-existent git repository
Single version release
Limited author information

Per-check LLM notes

Network: The use of HTTPX for network calls is common and does not inherently indicate malicious activity.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several red flags including a non-existent git repository, a single version release, and an author with limited information.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

i_key else {} async with httpx.AsyncClient(timeout=5.0) as client: try: r = await c
timeout self._http = httpx.AsyncClient( base_url=self._base, timeout=timeou

✓ 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-ring12

Develop a security monitoring tool named 'ChakraGuard' using the Python package 'aegis-ring12'. This tool will serve as an agentic trajectory verifier, ensuring that all actions within a specified system align with predefined ethical and operational guidelines. The tool will monitor real-time data streams from various sources, such as network traffic logs, user activity logs, and system performance metrics. It will use 'aegis-ring12' to analyze these streams and flag any anomalous behavior that could indicate security threats or policy violations.

Key Features:
1. Real-time Monitoring: Continuously analyze incoming data streams to detect potential threats.
2. Agentic Trajectory Verification: Utilize 'aegis-ring12' to verify the legitimacy of observed trajectories within the monitored systems.
3. Alert System: Notify administrators via email or SMS when suspicious activities are detected.
4. Historical Analysis: Store and review past data for forensic analysis.
5. Customizable Policies: Allow users to define their own security policies based on specific needs.
6. Integration Capabilities: Support integration with existing security tools and platforms.

How 'aegis-ring12' is Utilized:
- For real-time monitoring, 'aegis-ring12' will process live data feeds to identify patterns and deviations from normal behavior.
- During historical analysis, it will apply its verification algorithms to stored datasets to uncover previously undetected issues.
- In custom policy creation, 'aegis-ring12' provides a framework for defining rules that align with the principles of ethical governance in AI systems.
- Through integration capabilities, the package enables seamless interaction with other security solutions, enhancing overall system protection.