The Implications of Linguistic Illegibility for LLM Security
Linguistic illegibility in LLMs raises concerns about the reliability of security mechanisms. Taint tracking and other methods are proposed to enhance safety.
Large Language Models (LLMs) are designed to generate natural language, yet their outputs may not accurately reflect their internal computations. This phenomenon, termed 'linguistic illegibility,' suggests that security mechanisms relying on a model's linguistic outputs may be fundamentally flawed. The authors propose alternative strategies, such as taint tracking, to enhance LLM security by isolating model outputs from system states that should remain unaffected.
This synthesis was produced from its source by AI; there is no human editor or manual review step. How we work