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Safety backdoor attacks in large language models (LLMs) enable the stealthy triggering of unsafe behaviors while evading detection during normal interactions.
Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song. 2019 · 1908
Earlier work this paper cites.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
Earlier work this paper cites.
Concealed data poisoning attacks on nlp models
Eric Wallace, Tony Z Zhao, Shi Feng, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Onion: A simple and effective defense against textual backdoor attacks
Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2020 · 2011
Earlier work this paper cites.
An alternative softmax operator for reinforcement learning
Kavosh Asadi and Michael L Littman. 2017 · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Earlier work this paper cites.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2018 · 2018
Earlier work this paper cites.
Strip: A defence against trojan attacks on deep neural networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, and Surya Nepal. 2019 · 2019
Earlier work this paper cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019 · 2019
Earlier work this paper cites.
Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma. 2020 · 2020
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{ \{ T-Miner } \} : A generative approach to defend against trojan attacks on { \{ DNN-based } \} text classification
Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed, Neal Mangaokar, Jiameng Pu, Mobin Javed, Chandan K Reddy, and Bimal Viswanath. 2021 · 2021
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Mitigating backdoor attacks in lstm-based text classification systems by backdoor keyword identification
Chuanshuai Chen and Jiazhu Dai. 2021 · 2021
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Adaspeech: Adaptive text to speech for custom voice
Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng Zhao, and Tie-Yan Liu. 2021 · 2021
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Design and evaluation of a multi-domain trojan detection method on deep neural networks
Yansong Gao, Yeonjae Kim, Bao Gia Doan, Zhi Zhang, Gongxuan Zhang, Surya Nepal, Damith C Ranasinghe, and Hyoungshick Kim. 2021 · 2021
Earlier work this paper cites.
Rap: Robustness-aware perturbations for defending against backdoor attacks on nlp models
Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. 2021 · 2021
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Spinning language models: Risks of propaganda-as-a-service and countermeasures
Eugene Bagdasaryan and Vitaly Shmatikov. 2022 · 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 · 2022
Earlier work this paper cites.
Asleep at the keyboard? assessing the security of github copilot’s code contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2022 · 2022
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Constrained optimization with dynamic bound-scaling for effective nlp backdoor defense
Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, and Xiangyu Zhang. 2022 · 2022
Cited alongside, same era.
Rethinking the reverse-engineering of trojan triggers
Zhenting Wang, Kai Mei, Hailun Ding, Juan Zhai, and Shiqing Ma. 2022 · 2022
Cited alongside, same era.
Post-training detection of backdoor attacks for two-class and multi-attack scenarios
Zhen Xiang, David J Miller, and George Kesidis. 2022 · 2022
Cited alongside, same era.
Adversarial unlearning of backdoors via implicit hypergradient
Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin, and Ruoxi Jia. 2022 · 2022
Cited alongside, same era.
Fine-mixing: Mitigating backdoors in fine-tuned language models
Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma, Chenguang Wang, and Xu Sun. 2022 · 2022
Cited alongside, same era.
Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al. 2023 · 2023
Later among the works it cites.
Shadow alignment: The ease of subverting safely-aligned language models
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin. 2023 · 2023
Later among the works it cites.
Lmsys-chat-1m: A large-scale real-world llm conversation dataset
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric Xing, et al. 2023 · 2023
Later among the works it cites.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023b · 2023
Later among the works it cites.
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Yuanpu Cao, Bochuan Cao, and Jinghui Chen. 2023 · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
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 · 2023
Cited alongside, same era.
Badllama: cheaply removing safety fine-tuning from llama 2-chat 13b
Pranav Gade, Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
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.
From shortcuts to triggers: Backdoor defense with denoised poe
Qin Liu, Fei Wang, Chaowei Xiao, and Muhao Chen. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, and Neel Nanda. 2024 · 2024
Closest in time.
Managing extreme ai risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, et al. 2024 · 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 · 2024
Closest in time.
Privacy backdoors: Stealing data with corrupted pretrained models
Shanglun Feng and Florian Tramèr. 2024 · 2024
Closest in time.
Sleeper agents: Training deceptive llms that persist through safety training
Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M Ziegler, Tim Maxwell, Newton Cheng, et al. 2024 · 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 · 2024
Closest in time.
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 · 2024
Closest in time.
Competition report: Finding universal jailbreak backdoors in aligned llms
Javier Rando, Francesco Croce, Kryštof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, and Florian Tramèr. 2024 · 2024
Closest in time.
On the exploitability of instruction tuning
Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, and Tom Goldstein. 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
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Haoran You, Yichao Fu, Zheng Wang, Amir Yazdanbakhsh, et al. 2024 · 2024
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AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies
Yi Zeng, Kevin Klyman, Andy Zhou, Yu Yang, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, and Bo Li. 2024a · 2024
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What should embeddings embed? autoregressive models represent latent generating distributions
Liyi Zhang, Michael Y Li, and Thomas L Griffiths. 2024 · 2024
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Emulated disalignment: Safety alignment for large language models may backfire!
Zhanhui Zhou, Jie Liu, Zhichen Dong, Jiaheng Liu, Chao Yang, Wanli Ouyang, and Yu Qiao. 2024 · 2024
Closest in time.
Improving alignment and robustness with short circuiting
Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, and Dan Hendrycks. 2024 · 2024
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