agentassert-abc

v0.3.0 suspicious
6.0
Medium Risk

Formal behavioral specification and runtime enforcement for autonomous AI agents. Agent Behavioral Contracts (ABC).

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and credential harvesting risks, suggesting potential malicious intent. However, it lacks network and shell execution risks, which somewhat mitigates the overall threat level.

  • High obfuscation risk due to eval and encoded commands
  • Potential credential harvesting from /etc/passwd
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution detected, indicating no direct system command execution.
  • Obfuscation: The use of eval and encoded commands suggests potential for executing arbitrary code, indicative of malicious activity.
  • Credentials: Reading from '/etc/passwd' without a clear purpose may indicate an attempt to harvest credentials.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags are present.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • try: value = se.eval(expr) return ExprResult( value=v
  • result = ev.evaluate("eval('1+1')", {}) assert result.error is not None or resu
  • result = ev.evaluate("__import__('os').system('echo hacked')", {}) assert result.error is
  • straintCheck(field="x", expr="__import__('os')") # Should return False (fail-closed), not crash
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • result = ev.evaluate("open('/etc/passwd').read()", {}) assert result.error is not None or r
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository qualixar/agentassert-abc appears legitimate

⚠ 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-abc 
Create a mini-application that simulates a simple autonomous AI agent managing a virtual library system. This application will utilize the 'agentassert-abc' package to define and enforce formal behavioral specifications for the agent's actions. Here’s a detailed step-by-step guide on how to implement this project:

1. **Define the Library System**: Design a basic library management system where books can be added, removed, borrowed, and returned. Each book has a unique ID, title, author, and availability status.

2. **Agent Behavioral Contracts (ABC)**: Use the 'agentassert-abc' package to specify the behavior of your autonomous agent. Define contracts that ensure the agent follows rules such as not allowing the borrowing of unavailable books, ensuring books are returned within a specified period, and maintaining an accurate record of all transactions.

3. **Implement the Agent**: Write the code for your autonomous agent. This agent should interact with the library system based on predefined tasks like checking out books, returning them, and updating records. Ensure the agent adheres to the ABCs defined in step 2.

4. **Testing and Validation**: Utilize the 'agentassert-abc' package to test and validate the agent’s behavior against the specified ABCs. This involves running scenarios where the agent performs various operations and verifying that it complies with the rules set forth in its contracts.

5. **User Interface (Optional)**: For enhanced user interaction, consider developing a simple command-line interface or a graphical user interface (GUI) that allows users to interact directly with the library system through the agent.

Suggested Features:
- Detailed logging of all transactions for auditing purposes.
- Automated reminders for overdue books.
- A reporting feature that summarizes library activity over a given period.
- An option to manually override certain rules for special cases (e.g., emergency access).

By following these steps, you'll create a functional mini-application that demonstrates the power of using 'agentassert-abc' for defining and enforcing the behavior of autonomous AI agents in a real-world context.