LLM-Agnostic Semantic Representation Attack
Researchers have introduced a novel adversarial framework called Semantic Representation Attack (SRA) designed to bypass safety alignment in Large Language Models (LLMs). Unlike traditional token-level optimization methods that target specific affirmative text templates and often suffer from poor generalization, SRA shifts the objective to malicious semantic representations. The study establishes a theoretical Coherence-Convergence Relationship, proving that maintaining semantic coherence ensures both white-box convergence and black-box transferability across different models. Technically, the framework utilizes the Semantic Representation Heuristic Search (SRHS) algorithm, which preserves prompt interpretability and structural coherence during execution. Extensive evaluations across 26 open-source LLMs demonstrated an average attack success rate of 99.71%, highlighting significant improvements in stealth and cross-model transferability. This development underscores critical vulnerabilities in current LLM safeguard mechanisms, suggesting that semantic-level attacks pose a more severe threat than previously understood token-based exploits. The findings are published on arXiv, contributing to the ongoing discourse on AI security and robust alignment techniques.
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LLM-Agnostic Semantic Representation Attack
Researchers have introduced a novel adversarial framework called Semantic Representation Attack (SRA) designed to bypass safety alignment in Large Language Models (LLMs). Unlike traditional token-level optimization methods that target specific affirmative text templates and often suffer from poor generalization, SRA shifts the objective to malicious semantic representations. The study establishes a theoretical Coherence-Convergence Relationship, proving that maintaining semantic coherence ensures both white-box convergence and black-box transferability across different models. Technically, the framework utilizes the Semantic Representation Heuristic Search (SRHS) algorithm, which preserves prompt interpretability and structural coherence during execution. Extensive evaluations across 26 open-source LLMs demonstrated an average attack success rate of 99.71%, highlighting significant improvements in stealth and cross-model transferability. This development underscores critical vulnerabilities in current LLM safeguard mechanisms, suggesting that semantic-level attacks pose a more severe threat than previously understood token-based exploits. The findings are published on arXiv, contributing to the ongoing discourse on AI security and robust alignment techniques.
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