JailGuard: A Universal Detection Framework for LLM Prompt-based Attacks

WordNet: a lexical database for English

George A Miller. 1995 · 1995

Earlier work this paper cites.

Similarity measures for text document clustering. In Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand , Vol. 4. 9–56

Anna Huang et al · 2008

Earlier work this paper cites.

Cohen’s kappa coefficient as a performance measure for feature selection. In FUZZ-IEEE 2010, IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July, 2010, Proceedings . IEEE, 1–8

Susana M. Vieira, Uzay Kaymak, and João M. C. Sousa. 2010 · 2010

Earlier work this paper cites.

Understanding bag-of-words model: a statistical framework

Yin Zhang, Rong Jin, and Zhi-Hua Zhou. 2010 · 2010

Earlier work this paper cites.

Interrater reliability: the kappa statistic

Mary L McHugh. 2012 · 2012

Earlier work this paper cites.

Explaining and harnessing adversarial examples

Original

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014

Earlier work this paper cites.

Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn.. In Scipy . 32–37

Brent Komer, James Bergstra, and Chris Eliasmith. 2014 · 2014

Earlier work this paper cites.

Word2Vec

Kenneth Ward Church. 2017 · 2017

Earlier work this paper cites.

Countering adversarial images using input transformations

Original

Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten. 2017 · 2017

Earlier work this paper cites.

Towards deep learning models resistant to adversarial attacks

Original

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017

Earlier work this paper cites.

On detecting adversarial perturbations

Original

Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. 2017 · 2017

Earlier work this paper cites.

Ontonotes: Large scale multi-layer, multi-lingual, distributed annotation

Sameer Pradhan and Lance Ramshaw. 2017 · 2017

Earlier work this paper cites.

Feature squeezing: Detecting adversarial examples in deep neural networks

Original

Weilin Xu, David Evans, and Yanjun Qi. 2017 · 2017

Earlier work this paper cites.

Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In International conference on machine learning . PMLR, 274–283

Anish Athalye, Nicholas Carlini, and David Wagner. 2018 · 2018

Earlier work this paper cites.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Original

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018

Earlier work this paper cites.

Mma training: Direct input space margin maximization through adversarial training

Original

Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang. 2018 · 2018

Earlier work this paper cites.

Unsupervised representation learning by predicting image rotations

Original

Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018 · 2018

Earlier work this paper cites.

Adversarial examples in the physical world

Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. 2018 · 2018

Earlier work this paper cites.

On the dimensionality of word embedding

Zi Yin and Yuanyuan Shen. 2018 · 2018

Earlier work this paper cites.

Certified adversarial robustness via randomized smoothing. In international conference on machine learning . PMLR, 1310–1320

Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019 · 2019

Earlier work this paper cites.

Augmix: A simple data processing method to improve robustness and uncertainty

Original

Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan. 2019 · 2019

Earlier work this paper cites.

Improving robustness without sacrificing accuracy with patch gaussian augmentation

Original

Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk. 2019 · 2019

Earlier work this paper cites.

Language models are unsupervised multitask learners

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019

Earlier work this paper cites.

Adversarial defense by stratified convolutional sparse coding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11447–11456

Bo Sun, Nian-hsuan Tsai, Fangchen Liu, Ronald Yu, and Hao Su. 2019 · 2019

Earlier work this paper cites.

Triple-to-text: Converting RDF triples into high-quality natural languages via optimizing an inverse KL divergence. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 455–464

Yaoming Zhu, Juncheng Wan, Zhiming Zhou, Liheng Chen, Lin Qiu, Weinan Zhang, Xin Jiang, and Yong Yu. 2019 · 2019

Earlier work this paper cites.

Square attack: a query-efficient black-box adversarial attack via random search. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII . Springer, 484–501

Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein. 2020 · 2020

Earlier work this paper cites.

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning . PMLR, 2206–2216

Francesco Croce and Matthias Hein. 2020 · 2020

Earlier work this paper cites.

Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops . 702–703

Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. 2020 · 2020

Earlier work this paper cites.

Certified defense to image transformations via randomized smoothing

Marc Fischer, Maximilian Baader, and Martin Vechev. 2020 · 2020

Earlier work this paper cites.

Watch out! motion is blurring the vision of your deep neural networks

Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, and Yang Liu. 2020 · 2020

Earlier work this paper cites.

On extending NLP techniques from the categorical to the latent space: KL divergence, Zipf’s law, and similarity search

Original

Adam Hare, Yu Chen, Yinan Liu, Zhenming Liu, and Christopher G Brinton. 2020 · 2020

Earlier work this paper cites.

Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9729–9738

Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020

Earlier work this paper cites.

Adv-watermark: A novel watermark perturbation for adversarial examples. In Proceedings of the 28th ACM International Conference on Multimedia . 1579–1587

Xiaojun Jia, Xingxing Wei, Xiaochun Cao, and Xiaoguang Han. 2020 · 2020

Earlier work this paper cites.

Systematic literature reviews in software engineering - enhancement of the study selection process using Cohen’s Kappa statistic

Jorge E. Pérez, Jessica Díaz, Javier García Martín, and Bernardo Tabuenca. 2020 · 2020

Earlier work this paper cites.

On finding similar verses from the Holy Quran using word embeddings. In 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) . IEEE, 1–6

Sumaira Saeed, Sajjad Haider, and Quratulain Rajput. 2020 · 2020

Earlier work this paper cites.

SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. In Annual Meeting of the Association for Computational Linguistics (ACL)

Mao Ye, Chengyue Gong, and Qiang Liu. 2020 · 2020

Earlier work this paper cites.

Demystifying limited adversarial transferability in automatic speech recognition systems. In International Conference on Learning Representations (ICLR)

Hadi Abdullah, Aditya Karlekar, Vincent Bindschaedler, and Patrick Traynor. 2021 · 2021

Earlier work this paper cites.

Evaluating large language models trained on code

Original

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021

Earlier work this paper cites.

Achieving rotational invariance with bessel-convolutional neural networks

Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, and Alexandre Mayer. 2021 · 2021

Earlier work this paper cites.

Black-box detection of backdoor attacks with limited information and data. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 16482–16491

Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu. 2021 · 2021

Earlier work this paper cites.

Eliminate deviation with deviation for data augmentation and a general multi-modal data learning method

Original

Yunpeng Gong, Liqing Huang, and Lifei Chen. 2021 · 2021

Earlier work this paper cites.

AEDA: An Easier Data Augmentation Technique for Text Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021 . 2748–2754

Akbar Karimi, Leonardo Rossi, and Andrea Prati. 2021 · 2021

Earlier work this paper cites.

Reading Isn’t Believing: Adversarial Attacks On Multi-Modal Neurons

Original

David A Noever and Samantha E Miller Noever. 2021 · 2021

Earlier work this paper cites.

Helper-based adversarial training: Reducing excessive margin to achieve a better accuracy vs. robustness trade-off. In ICML 2021 Workshop on Adversarial Machine Learning

Rahul Rade and Seyed-Mohsen Moosavi-Dezfooli. 2021 · 2021

Earlier work this paper cites.

Robust learning meets generative models: Can proxy distributions improve adversarial robustness?

Original

Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal. 2021 · 2021

Earlier work this paper cites.

AVA: Adversarial vignetting attack against visual recognition

Original

Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, and Yang Liu. 2021 · 2021

Cited alongside, same era.

Directional self-supervised learning for heavy image augmentations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 16692–16701

Yalong Bai, Yifan Yang, Wei Zhang, and Tao Mei. 2022 · 2022

Cited alongside, same era.

A survey on data augmentation for text classification

Markus Bayer, Marc-André Kaufhold, and Christian Reuter. 2022 · 2022

Cited alongside, same era.

Can you spot the chameleon? adversarially camouflaging images from co-salient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2150–2159

Ruijun Gao, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Huazhu Fu, Wei Feng, Yang Liu, and Song Wang. 2022 · 2022

Cited alongside, same era.

Prompt injection attacks against GPT-3

Delimiters won’t save you from prompt injection

Simon Willison. 2023 · 2023

Closest in time.

Hacker Reveals Microsoft’s New AI-Powered Bing Chat Search Secrets

Davey Winder. 2023 · 2023

Closest in time.

{ \{ KENKU } \} : Towards Efficient and Stealthy Black-box Adversarial Attacks against { \{ ASR } \} Systems. In 32nd USENIX Security Symposium (USENIX Security 23) . 247–264

Xinghui Wu, Shiqing Ma, Chao Shen, Chenhao Lin, Qian Wang, Qi Li, and Yuan Rao. 2023 · 2023

Closest in time.

Defending chatgpt against jailbreak attack via self-reminders

Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu. 2023 · 2023

Closest in time.

Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models

Original

Jiashu Xu, Mingyu Derek Ma, Fei Wang, Chaowei Xiao, and Muhao Chen. 2023 · 2023

Closest in time.