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LLMs struggle with causal reasoning when noise is introduced, but explicitly modeling causal graphs can dramatically improve performance and generalization.
By prioritizing gradient direction in token optimization and using a two-stage loss, TAO-Attack achieves near-perfect jailbreak success rates against multiple LLMs, exposing critical vulnerabilities in current safety alignments.
