Retrieval-Augmented Generation systems face significant risks from corpus poisoning attacks that manipulate outputs through malicious documents. Existing detection methods often require auxiliary classifiers or additional LLM verification, which introduces substantial computational overhead. To address this, researchers introduced TRACE, a lightweight framework that identifies poisoning by tracing answer-related tokens via influence attribution. The system first discovers recurrent high-influence keywords across retrieved documents to flag potential threats. It then performs secondary verification to confirm the specific influence of these tokens on model predictions. Experiments conducted on three QA benchmarks and six LLMs demonstrate strong detection performance for the framework. Additionally, TRACE successfully uncovers attacker-specified target answers during the verification process.