CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
arXiv:2606.26216v1 Announce Type: cross Abstract: We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the benchmark provides end-to-end on-chain evaluation where agents interact with historical blockchain state through isolated evaluation environments orchestrated by Harbor, using tools to read code, trace transactions, and validate exploits on mainnet forks. Each c
The proliferation of smart contracts and their increasing exploitation has created an urgent need for advanced security solutions, coinciding with the rapid advancements in AI agent capabilities.
This benchmark directly addresses the critical security vulnerabilities prevalent in the blockchain ecosystem by leveraging AI, potentially transforming how smart contract risks are identified and mitigated.
The development of robust benchmarks like CyberChainBench enables more effective evaluation and improvement of AI agents for smart contract security, leading to potentially more secure and trustworthy decentralized finance (DeFi) platforms.
- · AI security firms
- · Smart contract platforms
- · DeFi users
- · Blockchain developers
- · Malicious actors/exploiters
- · Manual security auditors
- · Vulnerable smart contract projects
AI agents become a standard tool for smart contract vulnerability detection and exploit prevention.
Increased trust and adoption of decentralized applications (dApps) and DeFi due to enhanced security assurances.
The development of fully autonomous, AI-driven remediation systems for blockchain vulnerabilities, reducing human intervention significantly.
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Read at arXiv cs.AI