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The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
Earlier work this paper cites.
Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. 2022 · 2022
Earlier work this paper cites.
TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Earlier work this paper cites.
Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas. 2023 · 2023
Earlier work this paper cites.
Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023
Earlier work this paper cites.
Image hijacks: Adversarial images can control generative models at runtime
Luke Bailey, Euan Ong, Stuart Russell, and Scott Emmons. 2023 · 2023
Earlier work this paper cites.
Red-teaming large language models using chain of utterances for safety-alignment
Rishabh Bhardwaj and Soujanya Poria. 2023 · 2023
Earlier work this paper cites.
Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. 2023 · 2023
Earlier work this paper cites.
Fft: Towards harmlessness evaluation and analysis for llms with factuality, fairness, toxicity
Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, and Tingwen Liu. 2023 · 2023
Earlier work this paper cites.
Figstep: Jailbreaking large vision-language models via typographic visual prompts
Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, and Xiaoyun Wang. 2023 · 2023
Earlier work this paper cites.
From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy
Maanak Gupta, CharanKumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj. 2023 · 2023
Earlier work this paper cites.
Llm self defense: By self examination, llms know they are being tricked
Alec Helbling, Mansi Phute, Matthew Hull, and Duen Horng Chau. 2023 · 2023
Earlier work this paper cites.
Catastrophic jailbreak of open-source llms via exploiting generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen. 2023 · 2023
Earlier work this paper cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Earlier work this paper cites.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto. 2023 · 2023
Earlier work this paper cites.
Lifetox: Unveiling implicit toxicity in life advice
Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, and Kyomin Jung. 2023 · 2023
Earlier work this paper cites.
Unveiling safety vulnerabilities of large language models
George Kour, Marcel Zalmanovici, Naama Zwerdling, Esther Goldbraich, Ora Nova Fandina, Ateret Anaby-Tavor, Orna Raz, and Eitan Farchi. 2023 · 2023
Earlier work this paper cites.
Certifying llm safety against adversarial prompting
Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, and Hima Lakkaraju. 2023 · 2023
Earlier work this paper cites.
Lora fine-tuning efficiently undoes safety training in llama 2-chat 70b
Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023 · 2023
Cited alongside, same era.
Huachuan Qiu, Shuai Zhang, Anqi Li, Hongliang He, and Zhenzhong Lan. 2023 · 2023
Cited alongside, same era.
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei W Koh, Daphne Ippolito, Florian Tramer, and Ludwig Schmidt. 2024 · 2024
Closest in time.
Red teaming gpt-4v: Are gpt-4v safe against uni/multi-modal jailbreak attacks?
Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip Torr, Volker Tresp, and Jindong Gu. 2024 · 2024
Closest in time.
Eyes closed, safety on: Protecting multimodal llms via image-to-text transformation
Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang. 2024 · 2024
Closest in time.
Agent smith: A single image can jailbreak one million multimodal llm agents exponentially fast
Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, and Min Lin. 2024 · 2024
Closest in time.
Defending large language models against jailbreak attacks via semantic smoothing
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Javier Rando and Florian Tramèr. 2023 · 2023
Cited alongside, same era.
Tricking llms into disobedience: Understanding, analyzing, and preventing jailbreaks
Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, and Monojit Choudhury. 2023 · 2023
Cited alongside, same era.
Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas. 2023 · 2023
Cited alongside, same era.
Scalable and transferable black-box jailbreaks for language models via persona modulation
Rusheb Shah, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando, et al. 2023 · 2023
Cited alongside, same era.
Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh. 2023 · 2023
Cited alongside, same era.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Cited alongside, same era.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Jiabao Ji, Bairu Hou, Alexander Robey, George J Pappas, Hamed Hassani, Yang Zhang, Eric Wong, and Shiyu Chang. 2024 · 2024
Closest in time.
Haibo Jin, Ruoxi Chen, Andy Zhou, Jinyin Chen, Yang Zhang, and Haohan Wang. 2024 · 2024
Closest in time.
Break the breakout: Reinventing lm defense against jailbreak attacks with self-refinement
Heegyu Kim, Sehyun Yuk, and Hyunsouk Cho. 2024 · 2024
Closest in time.
Tong Liu, Yingjie Zhang, Zhe Zhao, Yinpeng Dong, Guozhu Meng, and Kai Chen. 2024 · 2024
Closest in time.
Weidi Luo, Siyuan Ma, Xiaogeng Liu, Xiaoyu Guo, and Chaowei Xiao. 2024 · 2024
Closest in time.
Siyuan Ma, Weidi Luo, Yu Wang, Xiaogeng Liu, Muhao Chen, Bo Li, and Chaowei Xiao. 2024 · 2024
Closest in time.
Jailbreaking attack against multimodal large language model
Zhenxing Niu, Haodong Ren, Xinbo Gao, Gang Hua, and Rong Jin. 2024 · 2024
Closest in time.
Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, and Prateek Mittal. 2024 · 2024
Closest in time.
A strongreject for empty jailbreaks
Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, et al. 2024 · 2024
Closest in time.
Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2024 · 2024
Closest in time.
Tastle: Distract large language models for automatic jailbreak attack
Zeguan Xiao, Yan Yang, Guanhua Chen, and Yun Chen. 2024 · 2024
Closest in time.
Safedecoding: Defending against jailbreak attacks via safety-aware decoding
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bill Yuchen Lin, and Radha Poovendran. 2024 · 2024
Closest in time.
A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang. 2024 · 2024
Closest in time.
Round trip translation defence against large language model jailbreaking attacks
Canaan Yung, Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. 2024 · 2024
Closest in time.
Weak-to-strong jailbreaking on large language models
Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, and William Yang Wang. 2024 · 2024
Closest in time.
Safety fine-tuning at (almost) no cost: A baseline for vision large language models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, and Timothy Hospedales. 2024 · 2024
Closest in time.