用多智能体协作发现共识协议中的深层逻辑漏洞。
Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents
- 设计专用智能体协同探索协议状态空间，模拟攻击场景。
- 在4个共识协议中发现15个未被察觉的安全性漏洞。
- 适合区块链安全研究者和高阶开发者阅读。
共识协议是分布式系统与区块链的核心，实现缺陷可能导致数据损坏和经济损失。尽管基于大模型的方法在代码分析中展现出潜力，但对涉及复杂状态依赖行为的深层协议级逻辑漏洞仍难以应对。我们提出Agora，一种领域感知的多智能体框架，结合假设驱动测试与大模型能力，实现系统性协议验证。Agora通过专业智能体协作，探索协议状态空间，利用领域特定约束合成攻击场景，并通过迭代优化验证结果。这种显式的角色分工使系统能推理全局协议不变量，超越单函数代码分析。我们在四个共识实现（Raft、EPaxos、HotStuff、BullShark）上，使用四种前沿大模型进行评估，Agora发现了15个此前未知的违反安全性的协议级逻辑漏洞，而现有基于大模型的智能体未能发现任何此类漏洞。结果表明，领域感知的多智能体协作对于检测复杂协议中的深层逻辑错误至关重要。
原文摘要 · Abstract (English)
Consensus protocols form the backbone of distributed systems and blockchains, where implementation bugs can cause data corruption and financial losses. While LLM-based approaches show promise in code analysis, they struggle with deep protocol-level logic bugs involving complex state-dependent behaviors across multiple execution stages. We present Agora, a domain-aware multi-agent framework that integrates hypothesis-driven testing with LLM capabilities for systematic protocol verification. Agora employs specialized agents that collaboratively explore protocol state spaces, synthesize attack scenarios using domain-specific constraints, and validate findings through iterative refinement. This explicit role separation enables reasoning about global protocol invariants beyond single-function code analysis. We evaluate Agora on four consensus implementations (Raft, EPaxos, HotStuff, BullShark) using four state-of-the-art LLMs. Agora discovers 15 previously unknown protocol-level logic bugs that violate safety properties, while existing LLM-based agents fail to detect any such protocol-level logic bugs. Our results demonstrate that domain-aware multi-agent collaboration is essential for detecting deep logic bugs in complex protocols.
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