EMNLP Findings2024
Virtual Context Enhancing Jailbreak Attacks with Special Token Injection
Yuqi Zhou, Lin Lu, Ryan Sun, Pan Zhou, Lichao Sun
摘要
Jailbreak attacks on large language models (LLMs) involve inducing these models to generate harmful content that violates ethics or laws, posing a significant threat to LLM security. Current jailbreak attacks face two main challenges: low success rates due to defensive measures and high resource requirements for crafting specific prompts. This paper introduces Virtual Context, which leverages special tokens, previously overlooked in LLM security, to improve jailbreak attacks. Virtual Context addresses these challenges by significantly increasing the success rates of existing jailbreak methods and requiring minimal background knowledge about the target model, thus enhancing effectiveness in black-box settings without additional overhead. Comprehensive evaluations show that Virtual Context-assisted jailbreak attacks can improve the success rates of four widely used jailbreak methods by approximately 40% across various LLMs. Additionally, applying Virtual Context to original malicious behaviors still achieves a notable jailbreak effect. In summary, our research highlights the potential of special tokens in jailbreak attacks and recommends including this threat in red-teaming testing to comprehensively enhance LLM security.
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引用它的顶会 Paper7
- Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language ModelsDaizong Liu, Mingyu Yang, Xiaoye Qu, Pan Zhou 等NeurIPS 2024 · 51 次引用
- DP-Fusion: Token-Level Differentially Private Inference for Large Language ModelsRushil Thareja, Preslav Nakov, Praneeth Vepakomma, Nils LukasICLR 2026 · 7 次引用
- MetaBreak: Jailbreaking Online LLM Services via Special Token ManipulationWentian Zhu, Zhen Xiang, Wei Niu, Le GuanS&P 2026 · 2 次引用
- Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model MergingLin Lu, Zhigang Zuo, Ziji Sheng, Pan ZhouEMNLP 2025 · 1 次引用
- Controllable Sequence Editing for Biological and Clinical TrajectoriesMichelle M. Li, Kevin Li, Yasha Ektefaie, Ying Jin 等ICLR 2026
它引用的 Paper7
- Tree of Attacks: Jailbreaking Black-Box LLMs AutomaticallyAnay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson 等NeurIPS 2024 · 835 次引用
- AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language ModelsXiaogeng Liu, Nan Xu, Muhao Chen, Chaowei XiaoICLR 2024 · 722 次引用
- Safe RLHF: Safe Reinforcement Learning from Human FeedbackJosef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji 等ICLR 2024 · 656 次引用
- Catastrophic Jailbreak of Open-source LLMs via Exploiting GenerationYangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li 等ICLR 2024 · 481 次引用
- "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language ModelsXinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen 等CCS 2024 · 132 次引用
相关 Paper
- Safety Guardrails of Large Language Models Are Vulnerable to Value-Driven Adversarial PromptingXiaohao Luo, Ying Wei, Zhijun LiACL Findings 2026
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- LLM Jailbreak Detection for (Almost) Free!Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li 等EMNLP Findings 2025 · 4 次引用
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