A fundamental flaw leaves LLMs strikingly vulnerable to attack

Researchers have identified a fundamental security flaw in large language models related to how they process instructions, making them susceptible to malicious manipulation. The study suggests that current red-teaming methods are insufficient because they rely on exhaustive lists of prohibited behaviors rather than addressing the core architectural vulnerability.