With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge.Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access.By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning.ETrace implements patternmatching to establish causal links between transaction behavior patterns and known attack behaviors.Furthermore, we validate the effectiveness of ETrace through preliminary experimental results.

Smart contracts are susceptible to various vulnerabilities that can be exploited by hackers via developing adversarial contracts. Existing vulnerability detection techniques often concentrate solely on vulnerable contracts, neglecting adversarial contracts, which may weaken the effectiveness of vulnerability detection and fail to meet practical needs.

Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how vulnerabilities can be exploited in attacks.