Model2h ago
Fundamental Flaw Leaves LLMs Vulnerable To Attacks
Researchers at the International Conference on Machine Learning argue that LLMs cannot be fully secured due to a core architectural flaw. This vulnerability allows attackers to bypass safety filters consistently. The finding suggests that current alignment techniques are insufficient. Practitioners must now assume that no prompt-based guardrail is impenetrable against a determined adversary.