The Implications of Linguistic Illegibility for LLM Security
Research shows LLM external outputs can misrepresent internal computation, undermining interpretability-based safety assurances — a foundational concern for any enterprise relying on LLM reasoning transparency for security decisions.
Summary written by editorial AI · Source link below
arXiv:2609.02852v1 Announce Type: cross Abstract: LLMs are trained to generate natural language. However, various strands of evidence indicate that an LLM's externalized linguistic outputs and mechanistically-extracted linguistic features can be an unreliable lens for understanding internal model computation. We introduce the term ``linguistic illegibility'' to broadly refer to scenarios in which an LLM's externalized or mechanistically-probed language artifacts fail to represent how the model
Editorial Analysis
Enterprises trusting LLM-generated explanations for security-critical decisions face hidden risks if outputs diverge from actual model reasoning.
Do not rely solely on LLM output explanations for security-relevant decisions; pair with independent verification mechanisms.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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