Red-Team AI Safety with GPT-5 and LangGraph Adaptive Thinking Agents
Inspired by recent concerns regarding AI's potential for large-scale destruction, this challenge tasks developers with building a sophisticated multi-agent red-teaming system. The system will simulate a 'red team' of adversarial agents and a 'blue team' of defensive agents, using advanced generative AI to identify and mitigate potential vulnerabilities in hypothetical AI systems or their societal deployments. This involves simulating complex scenarios, identifying emergent risks, and proposing robust safeguards. Participants will leverage GPT-5's advanced reasoning capabilities for scenario generation and risk assessment, orchestrated within a LangGraph-powered graph-based workflow. The core innovation lies in implementing extended thinking patterns with adaptive reasoning budgets, allowing agents to dynamically adjust their cognitive resources based on the complexity and novelty of encountered threats. Agent-to-agent (A2A) communication will be crucial for adversarial interaction and collaborative defense strategies.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by recent concerns regarding AI's potential for large-scale destruction, this challenge tasks developers with building a sophisticated multi-agent red-teaming system. The system will simulate a 'red team' of adversarial agents and a 'blue team' of defensive agents, using advanced generative AI to identify and mitigate potential vulnerabilities in hypothetical AI systems or their societal deployments. This involves simulating complex scenarios, identifying emergent risks, and proposing robust safeguards. Participants will leverage GPT-5's advanced reasoning capabilities for scenario generation and risk assessment, orchestrated within a LangGraph-powered graph-based workflow. The core innovation lies in implementing extended thinking patterns with adaptive reasoning budgets, allowing agents to dynamically adjust their cognitive resources based on the complexity and novelty of encountered threats. Agent-to-agent (A2A) communication will be crucial for adversarial interaction and collaborative defense strategies.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful Directed Acyclic Graph (DAG) agent workflows, incorporating persistence and dynamic routing.
Implement A2A protocol for structured and secure agent-to-agent communication, simulating adversarial and collaborative interactions.
Design advanced prompts and few-shot examples for GPT-5 to generate realistic and novel AI threat scenarios.
Build extended thinking pipelines with GPT-5 Pro, utilizing adaptive reasoning budgets to optimize computational resources for critical analysis.
Develop specialized 'Red Team' agents (e.g., Vulnerability Explorer, Attack Strategist) and 'Blue Team' agents (e.g., Defense Architect, Risk Assessor) within LangGraph.
Integrate a vector database (e.g., Milvus, Pinecone) for RAG (Retrieval Augmented Generation) to provide agents with up-to-date threat intelligence and safety guidelines.
Orchestrate complex feedback loops within LangGraph to allow agents to learn from simulated attacks and refine their strategies.
How this agent runs
The system will be evaluated on its ability to effectively simulate AI safety red-teaming, generate plausible threat scenarios, identify vulnerabilities, and propose concrete mitigation strategies, focusing on the sop...
Challenge input
{'ai_system_description': 'string', 'contextual_parameters': ['string']}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'scenario_title': 'string', 'threat_vector': 'string', 'catastrophic_risk_score': 'float', 'identified_vulnerabilities': ['string'], 'reasoning_pa...
- Red Team's generated scenario must be logically consistent and technically plausible.
- Blue Team's mitigation plan must address all identified vulnerabilities and include at least 2 high-priorit...
- Verify that A2A messages were properly formatted and exchanged between agents during the simulation (requir...
- ScenarioNoveltyScore target: 0.8
- Python execution harness
View technical recipe
Configured tools
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Evaluation contract
- The evaluation module defines the checks.
Recipe state
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Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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