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This paper studies how a trigger can make specific text invisible to an LLM, so the model omits it from summaries, ignores it in answers, and fails to flag it. The authors frame this as a dual-use capability: a possible supply-chain backdoor, but also a tool for quarantining content. Their main finding for auditors is that backdoors hidden from linear probes tend to degrade the model's attention to the suppressed span, so detection depends on white-box access. The authors explicitly say they do not establish that this tradeoff is fundamental, so treat the result as evidence about specific attacks rather than a general limit.
What the Model Cannot See: Covert and Provable Text Invisibility in LLMs
arXiv:2610.15883v1 Announce Type: cross Abstract: Text wrapped in a trigger can be made behaviorally invisible to a language model: omitted from summaries and quotations, ignored in question answering, and left unflagged by downstream filtering. We study this selective blindness as a dual-use information-flow primitive, a supply-chain backdoor, and a white-box auditing problem. We first implement it exactly as a serving-time span veto: blocking information flow out of a marked span makes every outside activation bit-identical under same-length substitution of the span's content, a noninterference property we verify across ten models, seven mixer families, and 0.5B-120B parameters. The veto serves as a deployable quarantine, an exact reference for calibrating leakage, and a teacher. Distilled into weights, it yields a backdoor: checkpoints trained on six model families from 4B to 120B ignore marked context at little benchmark cost, and their outputs show no measurable semantic trace of the suppressed content above the veto reference on our held-out suite; an in-domain variant suppresses planted vulnerabilities during AI code review. Detection is therefore a white-box problem. Simpler backdoors remain internally transparent -- linear probes recover suppressed content at 0.57-0.79 accuracy versus 0.025 chance -- but hardening the attack until probes reach chance collapses attention from outside the span into it below the range of base models. Across the attacks we evaluate, probe visibility trades off against routing visibility; we do not establish that this tradeoff is fundamental.
Abstract:Text wrapped in a trigger can be made behaviorally invisible to a language model: omitted from summaries and quotations, ignored in question answering, and left unflagged by downstream filtering. We study this selective blindness as a dual-use information-flow primitive, a supply-chain backdoor, and a white-box auditing problem. We first implement it exactly as a serving-time span veto: blocking information flow out of a marked span makes every outside activation bit-identical under same-length substitution of the span's content, a noninterference property we verify across ten models, seven mixer families, and 0.5B-120B parameters. The veto serves as a deployable quarantine, an exact reference for calibrating leakage, and a teacher. Distilled into weights, it yields a backdoor: checkpoints trained on six model families from 4B to 120B ignore marked context at little benchmark cost, and their outputs show no measurable semantic trace of the suppressed content above the veto reference on our held-out suite; an in-domain variant suppresses planted vulnerabilities during AI code review. Detection is therefore a white-box problem. Simpler backdoors remain internally transparent -- linear probes recover suppressed content at 0.57-0.79 accuracy versus 0.025 chance -- but hardening the attack until probes reach chance collapses attention from outside the span into it below the range of base models. Across the attacks we evaluate, probe visibility trades off against routing visibility; we do not establish that this tradeoff is fundamental.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2610.15883 [cs.CR]
(or arXiv:2610.15883v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.15883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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