Jailbreak Attack
Jailbreak attacks exploit vulnerabilities in large language models (LLMs) and other AI systems, aiming to bypass safety mechanisms and elicit harmful or unintended outputs. Current research focuses on developing novel attack methods, such as those leveraging resource exhaustion, implicit references, or continuous optimization via image inputs, and evaluating their effectiveness against various model architectures (including LLMs, vision-language models, and multimodal models). Understanding and mitigating these attacks is crucial for ensuring the safe and responsible deployment of AI systems, impacting both the trustworthiness of AI and the development of robust defense strategies.
Papers
Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models
Yifan Li, Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen
AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting
Yu Wang, Xiaogeng Liu, Yu Li, Muhao Chen, Chaowei Xiao
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
Huijie Lv, Xiao Wang, Yuansen Zhang, Caishuang Huang, Shihan Dou, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang
Defending LLMs against Jailbreaking Attacks via Backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh
ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings
Hao Wang, Hao Li, Minlie Huang, Lei Sha
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper
Daoyuan Wu, Shuai Wang, Yang Liu, Ning Liu
Foot In The Door: Understanding Large Language Model Jailbreaking via Cognitive Psychology
Zhenhua Wang, Wei Xie, Baosheng Wang, Enze Wang, Zhiwen Gui, Shuoyoucheng Ma, Kai Chen