# SoK: DARPA's AI Cyber Challenge (AIxCC): Competition Design, Architectures, and Lessons Learned

arXiv: https://arxiv.org/abs/2602.07666 · HTML: https://mlsanity.com/paper/2602.07666/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System](https://mlsanity.com/paper/2509.14589/) (2025)
- [autofz: Automated Fuzzer Composition at Runtime](https://mlsanity.com/paper/2302.12879/) (2023)
- [BandFuzz: An ML-powered Collaborative Fuzzing Framework](https://mlsanity.com/paper/2507.10845/) (2025)
- [PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair](https://mlsanity.com/paper/2601.17471/) (2026)

## Built on by (most cited first, 9 of 9)

- [Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generation](https://mlsanity.com/paper/2602.13574/) (2026)
- [PatchBench: Evaluating AI Agents for Vulnerability Patching](https://mlsanity.com/paper/2609.04075/) (2026)
- [Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation](https://mlsanity.com/paper/2603.17673/) (2026)
- [Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases](https://mlsanity.com/paper/2606.22263/) (2026)
- [Code-Augur: Agentic Vulnerability Detection via Specification Inference](https://mlsanity.com/paper/2606.18619/) (2026)
- [Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities](https://mlsanity.com/paper/2606.26933/) (2026)
- [The History Is the Detector: Executing CVE Patch History, End-to-End](https://mlsanity.com/paper/2609.05335/) (2026)
- [SelfOp: An Optimization Algorithm for Self-Improving Security Agents](https://mlsanity.com/paper/2609.22792/) (2026)
- [MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probes](https://mlsanity.com/paper/2609.23980/) (2026)

## Abstract (arXiv)

DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs) that leverage recent advances in AI -- particularly large language models (LLMs) -- to discover and remediate vulnerabilities in real-world open-source software. This paper presents the first systematic analysis of AIxCC. Drawing on design documents, source code, execution traces, and discussions with organizers and competing teams, we examine the competition's structure and key design decisions, characterize the architectural approaches of finalist CRSs, and analyze competition results beyond the final scoreboard. Our analysis reveals the factors that truly drove CRS performance, identifies genuine technical advances achieved by teams, and exposes limitations that remain open for future research. We conclude with lessons for organizing future competitions and broader insights toward deploying autonomous CRSs in practice.