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    <abstract>The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate strong problem-solving capabilities and adaptability to multifaceted scenarios. However, their use as agents also introduces significant risks, including task-specific risks, which are identified by the agent administrator based on the specific task requirements and constraints, and systemic risks, which stem from vulnerabilities in their design or interactions, potentially compromising confidentiality, integrity, or availability (CIA) of information and triggering security risks. Existing defense agencies fail to adaptively and effectively mitigate these risks. In this paper, we propose AGrail, a lifelong agent guardrail to enhance LLM agent safety, which features adaptive safety check generation, effective safety check optimization, and tool compatibility &amp; flexibility. Extensive experiments demonstrate that AGrail not only achieves strong performance against task-specific and system risks but also exhibits transferability across different LLM agents’ tasks.</abstract>
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%0 Conference Proceedings
%T AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection
%A Luo, Weidi
%A Dai, Shenghong
%A Liu, Xiaogeng
%A Banerjee, Suman
%A Sun, Huan
%A Chen, Muhao
%A Xiao, Chaowei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F luo-etal-2025-agrail
%X The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate strong problem-solving capabilities and adaptability to multifaceted scenarios. However, their use as agents also introduces significant risks, including task-specific risks, which are identified by the agent administrator based on the specific task requirements and constraints, and systemic risks, which stem from vulnerabilities in their design or interactions, potentially compromising confidentiality, integrity, or availability (CIA) of information and triggering security risks. Existing defense agencies fail to adaptively and effectively mitigate these risks. In this paper, we propose AGrail, a lifelong agent guardrail to enhance LLM agent safety, which features adaptive safety check generation, effective safety check optimization, and tool compatibility & flexibility. Extensive experiments demonstrate that AGrail not only achieves strong performance against task-specific and system risks but also exhibits transferability across different LLM agents’ tasks.
%R 10.18653/v1/2025.acl-long.399
%U https://aclanthology.org/2025.acl-long.399/
%U https://doi.org/10.18653/v1/2025.acl-long.399
%P 8104-8139

Markdown (Informal)

[AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection](https://aclanthology.org/2025.acl-long.399/) (Luo et al., ACL 2025)

• AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection (Luo et al., ACL 2025)

ACL

• Weidi Luo, Shenghong Dai, Xiaogeng Liu, Suman Banerjee, Huan Sun, Muhao Chen, and Chaowei Xiao. 2025. AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8104–8139, Vienna, Austria. Association for Computational Linguistics.