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Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
C Szegedy · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho · 2014
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2019
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M Lewis · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
V Sanh · 2019
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference
Sanghyun Hong, Yiğitcan Kaya, Ionuţ-Vlad Modoranu, and Tudor Dumitraş · 2020
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Adversarial training for large neural language models
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao · 2020
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Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
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Hatecheck: Functional tests for hate speech detection models
Paul Röttger, Bertram Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, and Janet B Pierrehumbert · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Detecting hate speech with gpt-3
Ke-Li Chiu, Annie Collins, and Rohan Alexander · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Fq-vit: Post-training quantization for fully quantized vision transformer
Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, and Shuchang Zhou · 2021
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The curious case of adversarially robust models: More data can help, double descend, or hurt generalization
Yifei Min, Lin Chen, and Amin Karbasi · 2021
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Bot-adversarial dialogue for safe conversational agents
Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, and Emily Dinan · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Using gpt-eliezer against chatgpt jailbreaking
Stuart Armstrong and R Gorman · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Evaluating the susceptibility of pre-trained language models via handcrafted adversarial examples
Hezekiah J Branch, Jonathan Rodriguez Cefalu, Jeremy McHugh, Leyla Hujer, Aditya Bahl, Daniel del Castillo Iglesias, Ron Heichman, and Ramesh Darwishi · 2022
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Ml4co-kida: Knowledge inheritance in dataset aggregation
Zixuan Cao, Yang Xu, Zhewei Huang, and Shuchang Zhou · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
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Hypothesis engineering for zero-shot hate speech detection
Janis Goldzycher and Gerold Schneider · 2022
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Quantifying privacy risks of masked language models using membership inference attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
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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
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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
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Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro · 2022
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Defining and characterizing reward hacking
Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
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On the importance of difficulty calibration in membership inference attacks
Lauren Watson, Chuan Guo, Graham Cormode, and Alexandre Sablayrolles · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Physics of language models: Part 3.2, knowledge manipulation
Zeyuan Allen-Zhu and Yuanzhi Li · 2023
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Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas · 2023
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Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Röttger, Dan Jurafsky, Tatsunori Hashimoto, and James Zou · 2023
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The hacking of chatgpt is just getting started, 2023
Matt Burgess · 2023
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Red teaming for large language models at scale: Tackling hallucinations on mathematics tasks
Aleksander Buszydlik, Karol Dobiczek, Michał Teodor Okoń, Konrad Skublicki, Philip Lippmann, and Jie Yang · 2023
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Defending against alignment-breaking attacks via robustly aligned llm
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
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Can llm-generated misinformation be detected?
Canyu Chen and Kai Shu · 2023
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Amazing "jailbreak" bypasses chatgpt’s ethics safeguards, 2023
Jon Christian · 2023
Cited alongside, same era.
Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2023
Cited alongside, same era.
Master thread of ways i have discovered to get chatgpt to output text that it’s not supposed to, including bigotry, urls and personal information, and more., 2023
Colin Fraser · 2023
Cited alongside, same era.
Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, and Tao Jiang · 2023
Cited alongside, same era.
Badllama: cheaply removing safety fine-tuning from llama 2-chat 13b
Best-of-venom: Attacking rlhf by injecting poisoned preference data
Tim Baumgärtner, Yang Gao, Dana Alon, and Donald Metzler · 2024
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Emergent and predictable memorization in large language models
Stella Biderman, Usvsn Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raff · 2024
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Defending against unforeseen failure modes with latent adversarial training
Stephen Casper, Lennart Schulze, Oam Patel, and Dylan Hadfield-Menell · 2024
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Jailbreakbench: An open robustness benchmark for jailbreaking large language models
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J Pappas, Florian Tramer, et al · 2024
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Breaking down the defenses: A comparative survey of attacks on large language models
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Pranav Gade, Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish · 2023
Cited alongside, same era.
Exploiting gpt-3 prompts with malicious inputs that order the model to ignore its previous directions, 2023
Riley Goodside · 2023
Cited alongside, same era.
Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz · 2023
Cited alongside, same era.
Indirect prompt injection threats., 2023
Kai Greshakeblog · 2023
Cited alongside, same era.
Catastrophic jailbreak of open-source llms via exploiting generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen · 2023
Cited alongside, same era.
Adversarial prompting guide, 2023
Injection Guide · 2023
Cited alongside, same era.
Knowledge sanitization of large language models
Yoichi Ishibashi and Hidetoshi Shimodaira · 2023
Cited alongside, same era.
Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein · 2023
Cited alongside, same era.
Arijit Ghosh Chowdhury, Md Mofijul Islam, Vaibhav Kumar, Faysal Hossain Shezan, Vinija Jain, and Aman Chadha · 2024
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Ai safety in generative ai large language models: A survey
Jaymari Chua, Yun Li, Shiyi Yang, Chen Wang, and Lina Yao · 2024
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Badrl: Sparse targeted backdoor attack against reinforcement learning
Jing Cui, Yufei Han, Yuzhe Ma, Jianbin Jiao, and Junge Zhang · 2024
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Security and privacy challenges of large language models: A survey
Badhan Chandra Das, M Hadi Amini, and Yanzhao Wu · 2024
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Masterkey: Automated jailbreaking of large language model chatbots
Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang, Ying Zhang, Zefeng Li, Haoyu Wang, Tianwei Zhang, and Yang Liu · 2024
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Attacks, defenses and evaluations for llm conversation safety: A survey
Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao, and Yu Qiao · 2024
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Llmeffichecker: Understanding and testing efficiency degradation of large language models
Xiaoning Feng, Xiaohong Han, Simin Chen, and Wei Yang · 2024
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Cross-task defense: Instruction-tuning llms for content safety
Yu Fu, Wen Xiao, Jia Chen, Jiachen Li, Evangelos Papalexakis, Aichi Chien, and Yue Dong · 2024
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Merging improves self-critique against jailbreak attacks
Victor Gallego · 2024
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Noisy neighbors: Efficient membership inference attacks against llms
Filippo Galli, Luca Melis, and Tommaso Cucinotta · 2024
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Scope: Evaluating llms for software vulnerability detection
José Gonçalves, Tiago Dias, Eva Maia, and Isabel Praça · 2024
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Perspective api, 2024
Google · 2024
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Rag vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture
Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al · 2024
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Covert malicious finetuning: Challenges in safeguarding llm adaptation
Danny Halawi, Alexander Wei, Eric Wallace, Tony T Wang, Nika Haghtalab, and Jacob Steinhardt · 2024
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Safe lora: the silver lining of reducing safety risks when fine-tuning large language models
Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang · 2024
Closest in time.
Split, unlearn, merge: Leveraging data attributes for more effective unlearning in llms
Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei, Nathalie Baracaldo, and Inkit Padhi · 2024
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A mechanistic understanding of alignment algorithms: A case study on dpo and toxicity
Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K Kummerfeld, and Rada Mihalcea · 2024
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No two devils alike: Unveiling distinct mechanisms of fine-tuning attacks
Chak Tou Leong, Yi Cheng, Kaishuai Xu, Jian Wang, Hanlin Wang, and Wenjie Li · 2024
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Eraser: Jailbreaking defense in large language models via unlearning harmful knowledge
Weikai Lu, Ziqian Zeng, Jianwei Wang, Zhengdong Lu, Zelin Chen, Huiping Zhuang, and Cen Chen · 2024
Closest in time.
Keeping llms aligned after fine-tuning: The crucial role of prompt templates
Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, and Sanjeev Arora · 2024
Closest in time.
Harnessing the power of llms in source code vulnerability detection
Andrew A Mahyari · 2024
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Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, et al · 2024
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Llm improvement for jailbreak defense: Analysis through the lens of over-refusal
Swetasudha Panda, Naveen Jafer Nizar, and Michael L Wick · 2024
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Is poisoning a real threat to llm alignment? maybe more so than you think
Pankayaraj Pathmanathan, Souradip Chakraborty, Xiangyu Liu, Yongyuan Liang, and Furong Huang · 2024
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Advprompter: Fast adaptive adversarial prompting for llms
Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, and Yuandong Tian · 2024
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Navigating the safety landscape: Measuring risks in finetuning large language models
ShengYun Peng, Pin-Yu Chen, Matthew Hull, and Duen Horng Chau · 2024
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Fine-tuning enhances existing mechanisms: A case study on entity tracking
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, and David Bau · 2024
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Safety alignment should be made more than just a few tokens deep
Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter Henderson · 2024
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Learning to poison large language models during instruction tuning
Yao Qiang, Xiangyu Zhou, Saleh Zare Zade, Mohammad Amin Roshani, Prashant Khanduri, Douglas Zytko, and Dongxiao Zhu · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Representation noising effectively prevents harmful fine-tuning on llms
Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze, David Atanasov, Robie Gonzales, Subhabrata Majumdar, Carsten Maple, Hassan Sajjad, and Frank Rudzicz · 2024
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2024
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Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2024
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Mission impossible: A statistical perspective on jailbreaking llms
Jingtong Su, Julia Kempe, and Karen Ullrich · 2024
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Tamper-resistant safeguards for open-weight llms
Rishub Tamirisa, Bhrugu Bharathi, Long Phan, Andy Zhou, Alice Gatti, Tarun Suresh, Maxwell Lin, Justin Wang, Rowan Wang, Ron Arel, et al · 2024
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan · 2024
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Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment
Jiongxiao Wang, Jiazhao Li, Yiquan Li, Xiangyu Qi, Muhao Chen, Junjie Hu, Yixuan Li, Bo Li, and Chaowei Xiao · 2024
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Efficient adversarial training in llms with continuous attacks
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, and Leo Schwinn · 2024
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TIES-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2024
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Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, and Dacheng Tao · 2024
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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
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li · 2024
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Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei · 2024
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Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao · 2024
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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
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