The Implications of Linguistic Illegibility for LLM Security
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 actually thinks. We argue that the specter of linguistic illegibility is unavoidable for LLMs whose internal computations are not directly expressed via language, but rather math over activation spaces (with lossy translations between activation spaces and natural language happening at the bookends).
- ▪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 actually thinks.
- ▪We argue that the specter of linguistic illegibility is unavoidable for LLMs whose internal computations are not directly expressed via language, but rather math over activation spaces (with lossy translations between activation spaces and
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.02852 |
| Publication time | Fri, 18 Sep 2026 19:00:06 +0000 |
| Retrieval time | 2026-09-18T19:18:45.451Z |
| Last seen | 2026-09-18T19:18:45.451Z |
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Computer Science > Machine Learning arXiv:2609.02852 (cs) [Submitted on 2 Sep 2026] Title:The Implications of Linguistic Illegibility for LLM Security Authors:James Mickens View a PDF of the paper titled The Implications of Linguistic Illegibility for LLM Security, by James Mickens View PDF HTML (experimental) 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 actually thinks.
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