International Conference on Machine Learning

Researchers argue LLMs have a fundamental, unfixable vulnerability to adversarial attack

A research team presented a paper at ICML 2026 arguing that large language models cannot be made fully secure against adversarial attacks due to a fundamental flaw in how they operate. The claim, if substantiated, has significant implications for AI safety and deployment, suggesting no patch or alignment technique can fully close the attack surface. MIT Technology Review is covering the finding as a major safety concern.