言語的不可読性がLLMセキュリティに与える影響
The Implications of Linguistic Illegibility for LLM Security (arxiv.org)
要約
本論文では、「言語的不可読性」という概念を導入し、LLMの外部出力やメカニズムから抽出された言語的特徴が、モデルの内部計算を正確に反映しない状況を指します。LLMの内部計算が直接言語で表現されるのではなく、アクティベーション空間上の数学演算であり、その間の翻訳が損失を伴う場合、言語的不可読性は避けられないと論じています。このため、チェーンオブソート監視や憲法的自己批評などの言語的自己報告に依存するセキュリティメカニズムは完全に安全ではなく、モデルサンドボックスには、モデルの言語状態を読み取ることに依存しない分離技術が必要であると主張しています。
全文翻訳
Computer Science > Machine Learning
arXiv:2609.02852 (cs)
[Submitted on 2 Sep 2026 (v1), last revised 6 Oct 2026 (this version, v2)]
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
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 illegibilityto 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). If linguistic illegibility is always possible, then security mechanisms that rely on a model's linguistic self-reporting (e.g., chain-of-thought monitoring, constitutional self-critique, activation probing for linguistically-defined feature vectors) can never be completely sound; the model sandbox will always need isolation techniques whose guarantees do not depend on reading a model's linguistic state at all. We argue that observing a model's outputs using taint tracking is a promising approach for an effective sandbox: regardless of how a model linguistically self-reports, a taint tracking policy can define, a priori, various pieces of system state that should never be influenced by model-produced data. We also discuss several additional sandboxing mechanisms (e.g., robust virtualization, third-party auditing of sandboxing configurations) which collectively provide a critical floor beneath linguistic monitoring, and would have mitigated recent sandbox exploits by frontier models.
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2609.02852 [cs.LG] |
| (or arXiv:2609.02852v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02852 arXiv-issued DOI via DataCite |
Submission history
From: James Mickens view email
v1 Wed, 2 Sep 2026 17:37:22 UTC (33 KB)
v2 Tue, 6 Oct 2026 21:19:03 UTC (34 KB)
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