Website: https://injecguard.github.io/ Paper: https://aclanthology.org/2025.acl long.1468.pdf Code Repo: https://github.com/leolee99/PIGuard News Due to some licensing issues, the model name has been changed from InjecGuard to PIGuard . We apologize for any inconvenience this may have caused. Abstract Prompt injection attacks pose a critical threat to large language models (LLMs), enabling goal hijacking and data leakage. Prompt guard models, though effective in defense, suffer from over defense—falsely flagging benign inputs as malicious due to trigger word bias. To address this issue, we introduce NotInject , an evaluation dataset that systematically measures over defense across various prompt guard models. NotInject contains 339 benign samples enriched with trigger words common in prompt injection attacks, enabling fine grained evaluation. Our results show that state of the art models suffer from over defense issues, with accuracy dropping close to random guessing levels (60\%). To mitigate this, we propose PIGuard , a novel prompt guard model that incorporates a new training strategy, Mitigating Over defense for Free (MOF), which significantly reduces the bias on trigger words.…
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