🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network calls, obfuscation, and credential harvesting but has a medium risk due to potential shell execution and very low community engagement.

Shell risk detected
Low community engagement

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Detection of shell execution suggests potential for executing arbitrary code, indicating some level of risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package is newly created with minimal activity and no community engagement, raising suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

sys.modules """ result = subprocess.run( [sys.executable, "-c", code], check=False,

✓ 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 5.0

Git history flags: Repository created very recently: 7 day(s) ago (2026-05-30T10:50:20Z)

Repository created very recently: 7 day(s) ago (2026-05-30T10:50:20Z)
Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 3 day(s) ago
Author "AgentChaos contributors" 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 agentchaos-core

Your task is to develop a small but comprehensive application that leverages the 'agentchaos-core' package to perform chaos engineering experiments on AI agents. This application will serve as a diagnostic tool for understanding how different AI agents respond under various failure scenarios. Here’s a detailed breakdown of what your application should achieve:

1. **Setup and Configuration**: Start by setting up your development environment. Ensure you have Python installed along with 'agentchaos-core'. Use pip to install the package if it isn't already available.

2. **Application Design**: Your application should allow users to define AI agents and specify chaos scenarios (e.g., network failures, data corruption). Each scenario should simulate a specific type of failure to test the robustness of the AI agent.

3. **Chaos Experiment Execution**: Implement functionality within your application that allows for the execution of these chaos experiments. The user should be able to select which AI agent and chaos scenario to run, and the application should then execute the experiment according to the specified conditions.

4. **Monitoring and Reporting**: During and after each experiment, the application must monitor the AI agent's behavior and performance. It should collect relevant metrics and generate a report summarizing the results of the experiment. This report should include details such as how the agent handled the failure, any errors encountered, and suggestions for improvement based on the observed behavior.

5. **User Interface**: Develop a simple yet effective command-line interface (CLI) or a basic web-based UI for interacting with the application. The UI should allow users to input parameters for their AI agents and chaos scenarios, start experiments, and view reports.

6. **Integration with Existing Tools**: Consider integrating your application with existing tools for monitoring and logging, such as Prometheus for metrics collection or Grafana for visualization of experimental results.

7. **Documentation**: Provide clear documentation on how to use your application, including setup instructions, examples of chaos scenarios, and guidelines for interpreting the results.

In utilizing the 'agentchaos-core' package, focus on its core functionalities related to simulating failures and diagnosing the impact on AI agents. Your application should demonstrate a deep understanding of how to leverage these capabilities to enhance the reliability and resilience of AI systems.