Compositional Jailbreaking: An Empirical Analysis of Mutator Chain Interactions in Aligned LLMs
Research demonstrates how combining multiple jailbreak techniques creates exponentially more effective attacks against LLM safety guardrails than isolated methods.
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arXiv:2605.15598v1 Announce Type: new Abstract: Jailbreaking attacks on large language models pose a significant threat to AI safety by enabling the generation of harmful or restricted content. While prior work has explored both handcrafted and automated jailbreak strategies, the potential for compositional interaction between simple attacks remains underexplored. This paper presents a systematic study of mutator chaining, in which weak jailbreak transformations are applied sequentially to char
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