Aligned LLMs are not adversarially aligned. Our attack constructs a single adversarial prompt that consistently circumvents the alignment of state-of-the-art commercial models including ChatGPT, Claude, Bard, and Llama-2 without having direct access to them. The examples shown here are all actual ...
This is the official repository for "Universal and Transferable Adversarial Attacks on Aligned Language Models" byAndy Zou,Zifan Wang,Nicholas Carlini,Milad Nasr,J. Zico Kolter, andMatt Fredrikson. Check out ourwebsite and demo here. Updates ...
CommanderUAP: a practical and transferable universal adversarial attacks on speech recognition modelsdoi:10.1186/s42400-024-00218-8Adversarial examplesUniversal adversarial perturbationsSpeech recognitionMost of the adversarial attacks against speech recognition systems focus on specific adversarial perturbations, ...
Transferable universal adversarial perturbations against speaker recognition systems Deep neural networks(DNN) exhibit powerful feature extraction capabilities, making them highly advantageous in numerous tasks. DNN-based techniques have be... X Liu,H Tan,J Zhang,... - 《World Wide Web-internet & Web...
The fact that triggers are transferable increases their adversarial threat: the adversary does not need gradient access to the target model. Instead, they can generate the attack using their own local model and transfer it to the target model. Finally, since triggers are input-agnostic, they ...
This is the official repository for "Universal and Transferable Adversarial Attacks on Aligned Language Models" byAndy Zou,Zifan Wang,Nicholas Carlini,Milad Nasr,J. Zico Kolter, andMatt Fredrikson. Check out ourwebsite and demo here. Updates ...
Transferable universal adversarial perturbations against speaker recognition systemsdoi:10.1007/s11280-024-01274-3Universal Adversarial AttackAdversarial TransferabilitySpeaker RecognitionSecurityDeep neural networks(DNN) exhibit powerful feature extraction capabilities, making them highly advantageous in numerous tasks....
Furthermore, recent studies have unveiled the existence of universal adversarial perturbations (UAPs) which are image-agnostic and highly transferable across different CNN models. In this survey, our primary focus revolves around the recent advancements in UAPs specifically within the image classification...
In addition to being network-agnostic (i.e., transferable among different state-of-the-art deep learning models), these universal perturbations are image-agnostic and maintain their adversarial efficacy across different images belonging to the same distribution (e.g., ImageNet dataset). For a ...