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DARWIN: Evolving Jailbreak Adversary and Guardrail for LLM Safety Evaluation and Protection
Most existing LLM safety evaluation and defense methods are static: jailbreak vulnerabilities are assessed with fixed attacks, and guardrails are trained on fixed malicious-prompt datasets. In practice, adversaries continually evolve and expand the attack space. We propose DARWIN, an evolutionary at
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