Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models
Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal domains. Most existing jailbreak methods revolve around Prompt Engineering or adversarial optimization, yet we identify a previously overlooked phenomenon: many harmful scenarios are highly consistent with legitimate ones in terms of underlying principles. Based on this finding, this paper proposes the Safe2Harm Semantic Isomorphism Attack method, which achieves efficient jailbreaking through four stages: first, rewrite the harmful question into a semantically safe question with similar underlying principles; second, extract the thematic mapping relationship between the two; third, let the LLM generate a detailed response targeting the safe question; finally, reversely rewrite the safe response based on the thematic mapping relationship to obtain harmful output. Experiments on 7 mainstream LLMs and three types of benchmark datasets show that Safe2Harm exhibits strong jailbreaking capability, and its overall performance is superior to existing methods. Additionally, we construct a challenging harmful content evaluation dataset containing 358 samples and evaluate the effectiveness of existing harmful detection methods, which can be deployed for LLM input-output filtering to enable defense.
Code (0)
등록된 구현이 없습니다.
Tasks
Prompt EngineeringSimilar Papers 제목 키워드 기반
Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens
Along with the remarkable successes of Language language models, recent research also started to explore the security threats of LLMs, including jailbreaking attacks. Attackers carefully craft jailbreaking prompts such t…
Safety AlignmentEraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense methods have failed to address the fundamental issue that harmful knowledge…
General KnowledgeSafety AlignmentSingle-pass Detection of Jailbreaking Input in Large Language Models
Defending aligned Large Language Models (LLMs) against jailbreaking attacks is a challenging problem, with existing approaches requiring multiple requests or even queries to auxiliary LLMs, making them computationally he…
SentenceBitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream Camouflage
The inherent risk of generating harmful and unsafe content by Large Language Models (LLMs), has highlighted the need for their safety alignment. Various techniques like supervised fine-tuning, reinforcement learning from…
Prompt EngineeringRed TeamingSafety AlignmentScalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
Large Language Models (LLMs) are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses. Such threats have raised critical concer…
RAGRetrievalRetrieval-augmented Generation