🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate metadata risk due to its novelty and lack of maintainer information. While other specific risks such as network, shell, obfuscation, and credential risks are low, the overall context raises concerns about potential supply-chain risks.

Moderate metadata risk due to minimal maintainer history and no author details.
Potential legitimacy issues given the package's newness and lack of background information.

Per-check LLM notes

Network: The observed network calls appear to be typical for making HTTP requests and could be part of the package's functionality, but further investigation is needed to confirm legitimate use.
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 is new with minimal maintainer history and no author details provided, raising suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

) -> None: async with httpx.AsyncClient() as client: # Register agents agent
) -> None: async with httpx.AsyncClient() as client: resp = await client.get(f"{url}/hea
ponse. """ async with httpx.AsyncClient(timeout=timeout) as client: return await client.requ
" try: async with httpx.AsyncClient(timeout=timeout) as client: response = await cli

✓ 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: aiagentobservatory.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 agenthive-sim

Create a security assessment tool named 'HiveGuard' using the 'agenthive-sim' Python package. This tool will simulate multi-agent attacks on AI systems to identify potential vulnerabilities and weaknesses. The application should include the following core functionalities:

1. **User Interface**: Develop a simple and intuitive web interface where users can input details about their AI system, such as network topology, data flow, and critical assets.
2. **Agent Configuration**: Allow users to configure different types of attackers (agents) with varying capabilities and strategies, including but not limited to phishing, SQL injection, and DDoS attacks.
3. **Simulation Engine**: Utilize the 'agenthive-sim' package to run simulations based on user-defined configurations. The simulation engine should be capable of handling multiple simultaneous attacks and provide real-time updates on the status of each attack.
4. **Report Generation**: After the simulation, generate comprehensive reports detailing the outcomes of each attack, including the success rate, impact on the system, and recommended countermeasures.
5. **Security Recommendations**: Based on the simulation results, provide actionable recommendations to improve the security posture of the AI system.
6. **Visualization Tools**: Implement visual tools within the application to help users understand the dynamics of the attacks and the effectiveness of their current defenses.

To achieve these goals, you will need to leverage key features of the 'agenthive-sim' package, such as agent behavior modeling, attack strategy implementation, and system response analysis. Ensure that the application is scalable and can handle complex scenarios involving numerous agents and sophisticated attack vectors.