Paper
AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification
Authors
Tian Zhang, Yiwei Xu, Juan Wang, Keyan Guo, Xiaoyang Xu, Bowen Xiao, Quanlong Guan, Jinlin Fan, Jiawei Liu, Zhiquan Liu, Hongxin Hu
Abstract
Large language model (LLM) agents increasingly rely on external tools and retrieval systems to autonomously complete complex tasks. However, this design exposes agents to indirect prompt injection (IPI), where attacker-controlled context embedded in tool outputs or retrieved content silently steers agent actions away from user intent. Unlike prompt-based attacks, IPI unfolds over multi-turn trajectories, making malicious control difficult to disentangle from legitimate task execution. Existing inference-time defenses primarily rely on heuristic detection and conservative blocking of high-risk actions, which can prematurely terminate workflows or broadly suppress tool usage under ambiguous multi-turn scenarios. We propose AgentSentry, a novel inference-time detection and mitigation framework for tool-augmented LLM agents. To the best of our knowledge, AgentSentry is the first inference-time defense to model multi-turn IPI as a temporal causal takeover. It localizes takeover points via controlled counterfactual re-executions at tool-return boundaries and enables safe continuation through causally guided context purification that removes attack-induced deviations while preserving task-relevant evidence. We evaluate AgentSentry on the \textsc{AgentDojo} benchmark across four task suites, three IPI attack families, and multiple black-box LLMs. AgentSentry eliminates successful attacks and maintains strong utility under attack, achieving an average Utility Under Attack (UA) of 74.55 %, improving UA by 20.8 to 33.6 percentage points over the strongest baselines without degrading benign performance.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22724v1</id>\n <title>AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification</title>\n <updated>2026-02-26T07:59:10Z</updated>\n <link href='https://arxiv.org/abs/2602.22724v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22724v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Large language model (LLM) agents increasingly rely on external tools and retrieval systems to autonomously complete complex tasks. However, this design exposes agents to indirect prompt injection (IPI), where attacker-controlled context embedded in tool outputs or retrieved content silently steers agent actions away from user intent. Unlike prompt-based attacks, IPI unfolds over multi-turn trajectories, making malicious control difficult to disentangle from legitimate task execution. Existing inference-time defenses primarily rely on heuristic detection and conservative blocking of high-risk actions, which can prematurely terminate workflows or broadly suppress tool usage under ambiguous multi-turn scenarios. We propose AgentSentry, a novel inference-time detection and mitigation framework for tool-augmented LLM agents. To the best of our knowledge, AgentSentry is the first inference-time defense to model multi-turn IPI as a temporal causal takeover. It localizes takeover points via controlled counterfactual re-executions at tool-return boundaries and enables safe continuation through causally guided context purification that removes attack-induced deviations while preserving task-relevant evidence. We evaluate AgentSentry on the \\textsc{AgentDojo} benchmark across four task suites, three IPI attack families, and multiple black-box LLMs. AgentSentry eliminates successful attacks and maintains strong utility under attack, achieving an average Utility Under Attack (UA) of 74.55 %, improving UA by 20.8 to 33.6 percentage points over the strongest baselines without degrading benign performance.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CR'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-02-26T07:59:10Z</published>\n <arxiv:comment>23 pages, 8 figures. Under review</arxiv:comment>\n <arxiv:primary_category term='cs.CR'/>\n <author>\n <name>Tian Zhang</name>\n </author>\n <author>\n <name>Yiwei Xu</name>\n </author>\n <author>\n <name>Juan Wang</name>\n </author>\n <author>\n <name>Keyan Guo</name>\n </author>\n <author>\n <name>Xiaoyang Xu</name>\n </author>\n <author>\n <name>Bowen Xiao</name>\n </author>\n <author>\n <name>Quanlong Guan</name>\n </author>\n <author>\n <name>Jinlin Fan</name>\n </author>\n <author>\n <name>Jiawei Liu</name>\n </author>\n <author>\n <name>Zhiquan Liu</name>\n </author>\n <author>\n <name>Hongxin Hu</name>\n </author>\n </entry>"
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