Fast Adversarial Attacks on Language Models In One GPU Minute
Vinu Sankar Sadasivan Shoumik Saha Gaurang Sriramanan Priyatham Kattakinda Atoosa Chegini Soheil Feizi
In this paper, we introduce a novel class of fast, beam search-based
adversarial attack (BEAST) for Language Models (LMs). BEAST employs
interpretable parameters, enabling attackers to balance between attack speed,
success rate, and the readability of adversarial prompts. The computational
efficiency of BEAST facilitates us to investigate its applications on LMs for
jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free
targeted attack can jailbreak aligned LMs with high attack success rates within
one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute
with a success rate of 89% when compared to a gradient-based baseline that
takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000
48GB GPU. Additionally, we discover a unique outcome wherein our untargeted
attack induces hallucinations in LM chatbots. Through human evaluations, we
find that our untargeted attack causes Vicuna-7B-v1.5 to produce ~15% more
incorrect outputs when compared to LM outputs in the absence of our attack. We
also learn that 22% of the time, BEAST causes Vicuna to generate outputs that
are not relevant to the original prompt. Further, we use BEAST to generate
adversarial prompts in a few seconds that can boost the performance of existing
membership inference attacks for LMs. We believe that our fast attack, BEAST,
has the potential to accelerate research in LM security and privacy. Our
codebase is publicly available at https://github.com/vinusankars/BEAST.