You Know What I'm Saying: Jailbreak Attack via Implicit Reference
Keywords: Adversarial attacks, Jailbreak, Security, Black box, LLM, Alignment, Cross-Modality alignment, in context learning
Abstract: While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, our study reveals that these methods remain inadequate at detecting malicious objectives expressed through context within nested harmless objectives.
This study identifies a previously overlooked vulnerability, which we term $\textbf{A}$ttack via $\textbf{I}$mplicit $\textbf{R}$eference ($\textbf{AIR}$). AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. This method employs multiple related harmless objectives to generate malicious content without triggering refusal responses, thereby effectively bypassing existing detection techniques.
Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding $\textbf{90}$% on most models, including GPT-4o, Claude-3.5-Sonnet, and Qwen-2-72B. Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method. These findings underscore the urgent need for defense mechanisms capable of understanding and preventing contextual attacks. Furthermore, we introduce a cross-model attack strategy that leverages less secure models to generate malicious contexts, thereby further increasing the ASR when targeting other models.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 68
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