Researchers have proposed an Evaluation Agent to detect misinformation and knowledge poisoning in Generative AI systems. The agent uses a combination of natural language inference, factual verification, and a poison detector to evaluate the trustworthiness of retrieved information. In experiments, the agent achieved high accuracy and precision on several datasets, including TruthfulQA and FEVER. However, it struggled with detecting subtle semantic weakening and contradiction. The authors also demonstrated the agent's effectiveness in a software-engineering use case, where it reliably blocked instruction injection of unsafe advice. --- Why it matters: This work matters to AI researchers because it addresses a critical security issue in Generative AI systems: knowledge poisoning. As these systems become more widespread, the risk of misinformation and malicious manipulation increases. The Evaluation Agent proposed here provides a potential solution for detecting and mitigating this threat. Source: https://arxiv.org/abs/2608.21095