agentassert-typec-core

v0.6.2 suspicious
4.0
Medium Risk

AgentAssert Type-C kernel — formal behavioral contracts for AI agent harnesses. Provider-blind, transport-blind runtime enforcement.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual behavior in its metadata and commit history, suggesting potential risks that need further investigation.

  • Rapid commit history and limited maintainer presence on PyPI.
  • Potential network communication that could be used for data exfiltration or command and control.
Per-check LLM notes
  • Network: The use of an HTTP client suggests network communication, which may be for legitimate purposes like fetching data, but could also indicate potential data exfiltration or command and control activities.
  • Shell: No shell execution patterns detected, indicating a low risk of direct system compromise through shell commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets.
  • Metadata: The rapid commit history and the maintainer's limited presence on PyPI suggest potential risk, but insufficient evidence to conclusively determine malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • , 0.0 async with httpx.AsyncClient(timeout=15.0) as client: response = await cl
✓ 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 score 2.5

Git history flags: All 16 commits happened within 24 hours

  • All 16 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Varun Pratap Bhardwaj" 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 agentassert-typec-core 
Create a mini-application called 'AgentBehaviorMonitor' that leverages the 'agentassert-typec-core' package to monitor and enforce behavioral contracts for AI agents in real-time. This application will serve as a robust tool for developers and researchers to ensure that AI agents adhere to specified formal behavioral contracts regardless of their underlying implementation or communication protocol.

Step 1: Define the Core Functionality
- Implement a feature that allows users to define formal behavioral contracts for AI agents using the 'agentassert-typec-core' package. These contracts should specify the expected behavior of the agents under various conditions.
- Ensure that the application can dynamically load and enforce these contracts at runtime.

Step 2: Develop the Monitoring System
- Build a monitoring system within the application that continuously checks the behavior of the AI agents against the defined contracts.
- Use the 'agentassert-typec-core' package's provider-blind and transport-blind capabilities to make the monitoring system versatile enough to work with different types of AI agents and communication protocols.

Step 3: Provide Real-Time Feedback
- Integrate a real-time feedback mechanism that alerts users when an AI agent violates a behavioral contract.
- This feedback should include details about which contract was violated, the context of the violation, and suggestions on how to rectify the issue.

Step 4: Enhance User Experience
- Design a user-friendly interface where users can easily view the status of their AI agents and the enforcement of behavioral contracts.
- Include visualizations to help users understand the compliance trends over time.

Suggested Features:
- Support for multiple AI agents running concurrently.
- Customizable alert systems (e.g., email notifications, SMS alerts).
- Detailed logs for auditing and troubleshooting.
- Integration with popular AI frameworks and platforms.

Utilization of 'agentassert-typec-core':
- Utilize the 'agentassert-typec-core' package to define and enforce behavioral contracts. This involves creating contract specifications that align with the desired behaviors of the AI agents and using the package's enforcement mechanisms to ensure these contracts are upheld during runtime.
- Leverage the package's provider-blind and transport-blind characteristics to seamlessly integrate with various AI agent implementations and communication methods without needing to modify the core enforcement logic.