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Over the past decade, adversarial training has emerged as one of the few reliable methods for enhancing model robustness against adversarial attacks [Szegedy et al., 2014, Madry et al., 2018, Xhonneux et al., 2024], while many alternative approaches have failed to withstand rigorous subsequent evaluations.
Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Defensive Distillation is Not Robust to Adversarial Examples
Nicholas Carlini and David Wagner · 2016
Earlier work this paper cites.
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Anish Athalye and Nicholas Carlini · 2018
Earlier work this paper cites.
On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Earlier work this paper cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Vicuna-7B-v1.5, 2023
Hugging Face · 2023
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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
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Llama guard: Llm-based input-output safeguard for human-ai conversations
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, et al · 2023
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Efficient adversarial training in llms with continuous attacks
Improving alignment and robustness with circuit breakers
Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, and Dan Hendrycks · 2024
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Adversarial perturbations cannot reliably protect artists from generative ai
Robert Hönig, Javier Rando, Nicholas Carlini, and Florian Tramèr · 2024
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Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks · 2024
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Attacking large language models with projected gradient descent
Simon Geisler, Tom Wollschläger, MHI Abdalla, Johannes Gasteiger, and Stephan Günnemann · 2024
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Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, and Leo Schwinn · 2024
Cited alongside, same era.
Adversarial attacks and defenses in large language models: Old and new threats
Leo Schwinn, David Dobre, Stephan Günnemann, and Gauthier Gidel
Cited in the paper.
Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann
Cited in the paper.
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
Closest in time.