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Large language models (LLMs) are now highly capable at a diverse range of tasks.
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
Earlier work this paper cites.
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.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 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 evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Earlier work this paper cites.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Earlier work this paper cites.
Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian J. Goodfellow · 2018
Earlier work this paper cites.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
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
Cited alongside, same era.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M. Cohen, Elan Rosenfeld, and J. Zico Kolter · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks
Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, and Ben Y Zhao · 2020
Later among the works it cites.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Later among the works it cites.
Evading adversarial example detection defenses with orthogonal projected gradient descent
Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, and Nicholas Carlini · 2021
Later among the works it cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Later among the works it cites.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
Cited alongside, same era.
Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks
Gunjan Verma and Ananthram Swami · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
A partial break of the honeypots defense to catch adversarial attacks
Nicholas Carlini · 2020
Cited alongside, same era.
Later among the works it cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.
Ai-guardian: Defeating adversarial attacks using backdoors
Hong Zhu, Shengzhi Zhang, and Kai Chen · 2023
Closest in time.