[2605.22001] Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems
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Computer Science > Cryptography and Security
arXiv:2605.22001 (cs)
[Submitted on 21 May 2026]
Title:Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems
Authors:Aaditya Pai<br>View a PDF of the paper titled Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems, by Aaditya Pai
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Abstract:Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives. We identify a systematic blind spot: when payloads are generated to mimic the domain vocabulary and authority structures of the target document, what we call domain camouflaged injection, standard detectors fail to flag them, with detection rates dropping from 93.8% to 9.7% on Llama 3.1 8B and from 100% to 55.6% on Gemini 2.0 Flash. We formalize this as the Camouflage Detection Gap (CDG), the difference in injection detection rate between static and camouflaged payloads. Across 45 tasks spanning three domains and two model families, CDG is large and statistically significant (chi^2 = 38.03, p
Comments:<br>8 pages, 3 figures, 2 tables. Submitted to EMNLP 2026 ARR cycle
Subjects:
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
ACM classes:<br>I.2.7
Cite as:<br>arXiv:2605.22001 [cs.CR]
(or<br>arXiv:2605.22001v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2605.22001
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Aaditya Pai [view email]<br>[v1]<br>Thu, 21 May 2026 04:58:11 UTC (27 KB)
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