27 citations · 98 across the 31 of their papers we have counts for
4 papers · 1 filter
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
Fangzhou Wu, Xiaogeng Liu, Chaowei Xiao
With the advancement of Large Language Models (LLMs), significant progress has been made in code generation, enabling LLMs to transform natural language into programming code. Thes…
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
Xiaogeng Liu, Nan Xu, Muhao Chen +1
The aligned Large Language Models (LLMs) are powerful language understanding and decision-making tools that are created through extensive alignment with human feedback. However, th…
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
Yechao Zhang, Shengshan Hu, Leo Yu Zhang +5
Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different…
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Xiaogeng Liu, Minghui Li, Haoyu Wang +5
Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can pr…