TxRay: Agentic Postmortem of Live Blockchain Attacks
Ziyue Wang, Jiangshan Yu, Kaihua Qin, Dawn Song, Arthur Gervais, Liyi Zhou

TL;DR
TxRay is an AI-powered system that automates the postmortem analysis of blockchain exploits, reconstructing attack sequences and generating reproducible proof-of-concept exploits from limited initial evidence.
Contribution
It introduces TxRay, a novel LLM-based system that automates attack reconstruction and PoC generation for DeFi exploits, significantly improving speed and accuracy over manual methods.
Findings
Achieves 92.11% success in reproducing incidents
Produces PoCs with 98.1% avoiding hard-coded attacker addresses
Reduces analysis time to under an hour for root cause and PoC generation
Abstract
Decentralized Finance (DeFi) has turned blockchains into financial infrastructure, allowing anyone to trade, lend, and build protocols without intermediaries, but this openness exposes pools of value controlled by code. Within five years, the DeFi ecosystem has lost over 15.75B USD to reported exploits. Many exploits arise from permissionless opportunities that any participant can trigger using only public state and standard interfaces, which we call Anyone-Can-Take (ACT) opportunities. Despite on-chain transparency, postmortem analysis remains slow and manual: investigations start from limited evidence, sometimes only a single transaction hash, and must reconstruct the exploit lifecycle by recovering related transactions, contract code, and state dependencies. We present TxRay, a Large Language Model (LLM) agentic postmortem system that uses tool calls to reconstruct live ACT attacks…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlockchain Technology Applications and Security · Security and Verification in Computing · Advanced Malware Detection Techniques
