AI Cyberattack Breakthrough Evaluator
Amidst expert skepticism regarding AI's 'cyberattack breakthroughs,' this challenge requires you to build an intelligent agent system to independently evaluate the efficacy of AI-aided security tools. Using AutoGen for multi-agent conversations, your system will employ Gemini 2.5 Pro (with its hybrid reasoning capabilities) to simulate both 'red team' (attack) and 'blue team' (defense) scenarios. You'll optimize agent prompts using DSPy for robust vulnerability identification and mitigation analysis. The goal is to objectively assess where AI truly provides 'modest gains' versus significant breakthroughs in cybersecurity, producing a detailed vulnerability report and recommendations.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an AutoGen-based multi-agent system with Gemini 2.5 Pro and DSPy to evaluate AI-aided cybersecurity tools.
How work is evaluated
The evaluation will assess the agent system's ability to identify vulnerabilities, suggest plausible exploits, propose effective mitigations, and objectively report on AI's contribution. Focus areas include AutoGen's conversational flow, Gemini 2.5 Pro's reasoning in security contexts, and DSPy's prompt optimization effectiveness.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Leverage Gemini 2.5 Pro's hybrid reasoning for code analysis and vulnerability identification.
Orchestrate a multi-agent system using AutoGen for collaborative cybersecurity analysis.
Integrate simulated security tools using Semantic Kernel for comprehensive evaluation.
Generate an objective report on AI-aided security tool efficacy.
Optimize agent prompts programmatically with DSPy for enhanced performance in security tasks.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the agent system's ability to identify vulnerabilities, suggest plausible exploits, propose effective mitigations, and objectively report on AI's contribution. Focus areas include AutoGen's...
Challenge input
JSON object with 'code_snippet' and 'target_context' (e.g., 'web application', 'network service')
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON object with 'vulnerabilities_found' (array of {name, description, severity}), 'exploit_scenario_outline'
- Verify that the AutoGen agents engage in a coherent and logical conversational flow, delegating tasks appro...
- Confirm that DSPy is correctly used to optimize prompts, demonstrating iterative refinement for security ta...
- Ensure Semantic Kernel is used to integrate and call simulated security tools effectively within agent work...
- Vulnerability Detection Accuracy 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
- Verify that the AutoGen agents engage in a coherent and logical conversational flow, delegating tasks appropriately.
- Confirm that DSPy is correctly used to optimize prompts, demonstrating iterative refinement for security tasks.
- Ensure Semantic Kernel is used to integrate and call simulated security tools effectively within agent workflows.
Metrics
- Vulnerability Detection Accuracy · Target 0.9
- Mitigation Strategy Effectiveness · Target 0.85
- Objectivity of AI Efficacy Report · Target 0.8
Workflow tasks
- Task 1
Input: JSON object with 'code_snippet' and 'target_context' (e.g., 'web application', 'network service')
Output: JSON object with 'vulnerabilities_found' (array of {name, description, severity}), 'exploit_scenario_outline'
Execution harness: Python
- Task 2
Input: JSON object containing 'vulnerabilities_found' from 'Vulnerability Identification' task.
Output: JSON object with 'mitigation_strategies' (array of {vulnerability, strategy, code_fix}), 'ai_efficacy_summary' (overall AI contribution, specific AI-aided gains, areas of modest gain)
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.