用同人小说文风做攻击,让安全对齐的模型失控。
Voices Across Registers: Corpus-Conditioned Vernacular Jailbreaks against Aligned LLMs via Fanfiction Subgenres

- 用AO3十二类同人小说文本生成攻击提示,自然藏匿恶意行为。
- 平均攻击成功率从27.8%提升至73.1%,且效果来自文风而非长度结构。
- 无需对抗模型,可被防御系统误判为更难应对的攻击方式。
现有针对对齐大模型的越狱攻击多为离散提示,易被识别和修补。本文认为问题根源不在于特定提示,而在于安全对齐未覆盖的自然写作风格。为此,提出VAR——首个利用真实同人小说子类型作为通用攻击载体的越狱方法:通过将创作元提示与Archive of Our Own(AO3)某子类型的段落结合,将有害行为嵌入生成场景的高潮部分。该方法无需对抗性攻击模型或优化过程。在包含HarmBench与JailbreakBench的八款对齐模型上,四评委集成评估下,平均攻击成功率(ASR)从0.278提升至0.731;因子分解显示增益源于文风而非长度或结构。两种主动防御反而扩大了文风攻击与基线攻击的差距,表明模板防御会引导攻击者转向此类基于文风的攻击。还提出VAR-A4静态四轮扩展,平均ASR达0.924,显著优于三种现有多轮方法。代码与数据已开源于https://github.com/T-Lab-CUHKSZ/VAR。
原文摘要 · Abstract (English)
Existing jailbreaks against aligned LLMs are discrete artifacts whose surface forms are easy to fingerprint and patch. We argue that the broader failure mode may lie not in any specific prompt, but in natural writing registers that safety tuning under-covers. Building on this insight, we introduce VAR, the first jailbreak family that uses real fanfiction subgenres as universal attack carriers: a creative-writing meta prompt is conditioned on passages from one of twelve Archive of Our Own (AO3) subgenres, and the harmful behavior is embedded as the climax of the resulting scene. The construction requires neither an adversarial attacker LLM nor optimization. On eight aligned LLMs over the union of HarmBench and JailbreakBench, this attack lifts mean ASR from 0.278 to 0.731 under a four-judge ensemble; a factorial decomposition shows the gain is carried by register rather than length or structure. Two active defenses widen rather than narrow the vernacular-to-baseline ratio, indicating that template-targeting defenses merely steer attackers toward register-based attacks like ours. We also propose VAR-A4, a static four-turn extension that attains a mean ASR of 0.924, substantially exceeding three existing multi-turn methods. Our code and data are safely open-sourced at https://github.com/T-Lab-CUHKSZ/VAR.
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