Neuralese is a shorthand term for machine-internal representations that are useful to a model but hard for humans to read directly. It usually refers to latent activations, embeddings, or other hidden state patterns that do the real computational work behind a model’s output.
It matters in cyber security because defenders cannot safely rely on a model’s polished explanation alone. An AI system may produce a clean chain of thought or natural-language rationale while the actual decision process remains buried in neuralese. In attacks, that gap can hide unsafe behavior, deceptive outputs, or unexpected tool use. In defense, interpretability tools, structural audits, and monitorability techniques try to translate or inspect these internal signals so teams can verify what the model is really doing. The practical lesson is simple: treat visible text as one clue, not proof of intent or correctness.