5 papers
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race?
Yuan Xin, Dingfan Chen, Linyi Yang +2
As large language models (LLMs) are increasingly deployed, ensuring their safe use is paramount. Jailbreaking, adversarial prompts that bypass model alignment to trigger harmful ou…
Invisibility Cloak: Disappearance under Human Pose Estimation via Backdoor Attacks
Minxing Zhang, Wenshu Fan, Wenbo Jiang +3
Despite being significant in autonomous systems, Human Pose Estimation (HPE)'s potential risks to adversarial attacks have not received comparable attention with image classificati…
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
Yuan Xin, Dingfan Chen, Michael Backes +1
As ML models are increasingly deployed in critical applications, robustness against adversarial perturbations is crucial. While numerous defenses have been proposed to counter such…
Generating Less Certain Adversarial Examples Improves Robust Generalization
Minxing Zhang, Michael Backes, Xiao Zhang
This paper revisits the robust overfitting phenomenon of adversarial training. Observing that models with better robust generalization performance are less certain in predicting ad…
Transferable Availability Poisoning Attacks
Yiyong Liu, Michael Backes, Xiao Zhang
We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its tra…