TxRay: Agentic Postmortem of Live Blockchain Attacks
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Abstract (arXiv)
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 from limited evidence. Starting from one or more seed transactions, TxRay recovers the exploit lifecycle, derives an evidence-backed root cause, and generates a runnable, self-contained Proof of Concept (PoC) that deterministically reproduces the incident. TxRay self-checks postmortems by encoding incident-specific semantic oracles as executable assertions. To evaluate PoC correctness and quality, we develop PoCEvaluator, an independent agentic execution-and-review evaluator. On 114 incidents from DeFiHackLabs, TxRay produces an expert-aligned root cause and an executable PoC for 105 incidents, achieving 92.11% end-to-end reproduction. Under PoCEvaluator, 98.1% of TxRay PoCs avoid hard-coding attacker addresses, a +22.9pp lift over DeFiHackLabs. In a live deployment, TxRay delivers validated root causes in 40 minutes and PoCs in 59 minutes at median latency. TxRay's oracle-validated PoCs enable attack imitation, improving coverage by 15.6% and 65.5% over STING and APE.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 1 Feb 2026.
Builds on
19 of 35 references.
References · 19
- 2023Automatic Root Cause Analysis via Large Language Models for Cloud Incidentscited by 82
- 2023Do you still need a manual smart contract audit?cited by 34
- 2022SoK: Decentralized Finance (DeFi) Attackscited by 28
- 2023Blockchain Large Language Modelscited by 16
- 2024Large Language Models for Blockchain Security: A Systematic Literature Reviewcited by 12
- 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
- 2025AI Agent Smart Contract Exploit Generationcited by 8
- 2024GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Modelscited by 7
- 2025Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detectioncited by 7
- 2025PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contractscited by 7
- 2025SmartPoC: Generating Executable and Validated PoCs for Smart Contract Bug Reportscited by 5
- 2025DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Responsecited by 4
7 more
- 2025AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generationcited by 3
- 2023Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repaircited by 2
- 2025Decompiling Smart Contracts with a Large Language Modelcited by 2
- 2025LLM-Powered Detection of Price Manipulation in DeFicited by 2
- 2024Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?cited by 1
- 2025Blockchain Address Poisoningcited by 1
- 2025An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Modelscited by 1
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
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- 2026ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution
- 2026Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries
- 2025RISKTAGGER: Evidence-Guided LLM Agent for Post-Incident Forensic Analysis of Money Laundering in Web3 ↗
- 2026CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
- 2026When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling
- 2025Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining