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PIGuard

leolee99

- Website: https://injecguard.github.io/ - Paper: https://aclanthology.org/2025.acl-long.1468.pdf - Code Repo: https://github.com/leolee99/PIGuard Due to some licensing issues, the model name has been changed from **InjecGuard** to **PIGuard**. We apologize for any inconvenience this may have caused. 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. InjecGuard demonstrates state-of-the-art performance on diverse benchmarks including NotInject, surpassing the existing best model by 30.8\%, offering a robust and open-source solution for detecting prompt injection attacks.

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PIGuard

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