@inproceedings{zhan-etal-2026-adversarial,
title = "How Adversarial Environments Mislead Agentic {AI}?",
author = "Zhan, Zhonghao and
Zhou, Huichi and
Li, Zhenhao and
Jing, Peiyuan and
Li, Krinos and
Haddadi, Hamed",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.499/",
pages = "10264--10280",
ISBN = "979-8-89176-395-1",
abstract = "Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking ``can the agent use tools correctly'' but never ``what if the tools lie''. We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a ``fake world'' of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via Potemkin, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities."
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<abstract>Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking “can the agent use tools correctly” but never “what if the tools lie”. We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a “fake world” of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via Potemkin, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.</abstract>
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%0 Conference Proceedings
%T How Adversarial Environments Mislead Agentic AI?
%A Zhan, Zhonghao
%A Zhou, Huichi
%A Li, Zhenhao
%A Jing, Peiyuan
%A Li, Krinos
%A Haddadi, Hamed
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhan-etal-2026-adversarial
%X Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking “can the agent use tools correctly” but never “what if the tools lie”. We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a “fake world” of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via Potemkin, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.
%U https://aclanthology.org/2026.findings-acl.499/
%P 10264-10280
Markdown (Informal)
[How Adversarial Environments Mislead Agentic AI?](https://aclanthology.org/2026.findings-acl.499/) (Zhan et al., Findings 2026)
ACL
- Zhonghao Zhan, Huichi Zhou, Zhenhao Li, Peiyuan Jing, Krinos Li, and Hamed Haddadi. 2026. How Adversarial Environments Mislead Agentic AI?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 10264–10280, San Diego, California, United States. Association for Computational Linguistics.