Multi-Agent Pen-Testing
This challenge tasks you with building a multi-agent penetration testing system. You will orchestrate a team of specialized agents using CrewAI, each with distinct roles (e.g., 'Reconnaissance Agent', 'Vulnerability Scanner Agent', 'Report Generator Agent'). These agents must communicate securely via an A2A protocol and leverage Claude Opus 4.1 for advanced reasoning to identify and exploit vulnerabilities in a simulated web application. The system needs to integrate MCP-enabled tools for interacting with the target environment and dynamically adapt its thinking budget based on the complexity of findings.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge tasks you with building a multi-agent penetration testing system. You will orchestrate a team of specialized agents using CrewAI, each with distinct roles (e.g., 'Reconnaissance Agent', 'Vulnerability Scanner Agent', 'Report Generator Agent'). These agents must communicate securely via an A2A protocol and leverage Claude Opus 4.1 for advanced reasoning to identify and exploit vulnerabilities in a simulated web application. The system needs to integrate MCP-enabled tools for interacting with the target environment and dynamically adapt its thinking budget based on the complexity of findings.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master CrewAI for defining roles, tasks, and collaboration patterns in a multi-agent team for security operations.
Implement A2A protocol for secure, asynchronous communication and information sharing between specialized penetration testing agents.
Design MCP-enabled tool integration with Claude Opus 4.5, allowing agents to interact with mock security scanning tools and a simulated web application environment (e.g., OWASP Juice Shop).
Build extended thinking pipelines where agents iteratively refine their attack vectors and exploit strategies based on gathered information.
Deploy role-based agent teams to identify common web vulnerabilities like SQL injection, XSS, and broken authentication.
Utilize Claude Opus 4.5 for hybrid reasoning, combining instant analysis of scan results with deep reasoning for complex vulnerability chaining and exploit generation.
How this agent runs
The evaluation will assess the multi-agent system's ability to autonomously discover vulnerabilities in a simulated environment, communicate findings between agents, and generate a professional penetration test report.
Challenge input
{"target_url": "URL of the simulated vulnerable web application"}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{"vulnerabilities_found": [{"type": "string", "location": "string", "severity": "string", "exploit_path_summary": "string"}, ...]}
- Agent team identifies at least two distinct vulnerabilities in the target application.
- Communication logs clearly show agents sharing information relevant to their tasks.
- A penetration test report is generated with clear sections (summary, findings, recommendations).
- Vulnerability Coverage target: 75
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset
Versalist can run this agent on your behalf with your data. Tell us about your dataset and the result you need.
Discuss your dataset[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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