Adaptive Cyber Threat Response
This challenge focuses on building a proactive and adaptive multi-agent system for cybersecurity threat intelligence and incident response. Participants will use AutoGen to orchestrate a dynamic team of agents, leveraging OpenAI o3 for advanced threat analysis and decision-making. The agents will communicate using the A2A Protocol and integrate with simulated security tools (e.g., SIEM, SOAR, vulnerability scanners). The system will implement a robust RAG mechanism with a vector database (e.g., Qdrant) to pull real-time threat intelligence and historical incident data. Agents will dynamically form to analyze emerging threats, propose containment strategies, and simulate response actions, providing a comprehensive incident summary and actionable recommendations.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an adaptive multi-agent system with AutoGen and OpenAI o3 for real-time cyber threat analysis and incident response using A2A and RAG.
How work is evaluated
The solution will be evaluated on its ability to accurately detect and classify a simulated cyber threat, propose appropriate response actions, generate a comprehensive incident report, and demonstrate effective dynamic agent collaboration. The quality of RAG-driven threat intelligence will also be assessed.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Integrate real-time threat intelligence via RAG with vector search into agent workflows.
Develop tool-using agents for interacting with simulated security systems.
Orchestrate dynamic multi-agent collaboration for complex, real-time problem-solving using AutoGen.
Implement A2A Protocol for structured agent communication in a cybersecurity context.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The solution will be evaluated on its ability to accurately detect and classify a simulated cyber threat, propose appropriate response actions, generate a comprehensive incident report, and demonstrate effective dynam...
Challenge input
{"security_alert": "String (e.g., 'Suspicious login attempt from X IP', 'Malware detected on server Y')", "threat_intel_stub": ["String (relevant h...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"threat_identified": "String", "severity": "String (Low|Medium|High)", "recommended_actions": ["String"], "incident_report_summary": "String", "ag...
- Checks if the agent system correctly identifies the threat based on the alert and intel.
- Verifies if critical recommended actions (e.g., quarantine, block) are present.
- Assesses if the collaboration log demonstrates dynamic agent interaction using AutoGen.
- ThreatContextualizationScore target: 0.9
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- Checks if the agent system correctly identifies the threat based on the alert and intel.
- Verifies if critical recommended actions (e.g., quarantine, block) are present.
- Assesses if the collaboration log demonstrates dynamic agent interaction using AutoGen.
Metrics
- ThreatContextualizationScore · Target 0.9
- IncidentReportQuality · Target 4
Workflow tasks
- Task 1
Input: {"security_alert": "String (e.g., 'Suspicious login attempt from X IP', 'Malware detected on server Y')", "threat_intel_stub": ["String (relevant historical incidents, IOCs)"], "tool_stubs": {"quarantine_server": "python_callable", "block_ip": "python_callable"}}
Output: {"threat_identified": "String", "severity": "String (Low|Medium|High)", "recommended_actions": ["String"], "incident_report_summary": "String", "agent_collaboration_log": ["String"]}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.