The Implications of Linguistic Illegibility for LLM Security

This paper argues that LLMs may be fundamentally "linguistically illegible," meaning their internal reasoning can’t be fully trusted or inferred from outputs, chain-of-thought, or other language-based self-reports. For CIOs and technology leaders, the strategic takeaway is that AI security cannot rely on monitoring what the model says it is doing; it must instead be built on hard controls such as taint tracking, sandbox isolation, robust virtualization, and independent auditing. For IT organizations, this shifts LLM governance from prompt- and output-centric oversight to defense-in-depth architecture that constrains what model outputs can influence, reducing the risk of sandbox escapes and unsafe system interactions.