Researchers argue LLMs can never be made fully secure

A team of researchers presented a paper at the International Conference on Machine Learning (ICML) arguing that a fundamental flaw in how large language models (LLMs) work makes it impossible to fully secure them against attack. The claim was presented at one of the field's top AI conferences.

Why it matters: This lands the same period as a separate tool demonstrating how easily frontier models can be jailbroken, reinforcing a pattern rather than a one-off finding. If the underlying architecture is inherently unsecurable as the paper claims, it shifts the debate from which guardrails work best to how much residual risk is acceptable, with direct implications for how much autonomy AI agents should be given in high-stakes settings.