No Query, No Access
Wenqiang Wang, Siyuan Liang, Yangshijie Zhang, Xiaojun Jia, Hao Lin, Xiaochun Cao

TL;DR
This paper introduces VDBA, a novel adversarial attack method that effectively fools NLP models, including large language models, without requiring model access or extensive queries, highlighting significant security risks.
Contribution
VDBA is a new attack approach that uses victim texts and shadow datasets to generate effective adversarial examples without model access or many queries.
Findings
VDBA achieves a 52.08% increase in attack success rate over state-of-the-art methods.
VDBA reduces attack queries to zero, demonstrating high efficiency.
VDBA poses a serious threat to LLMs like Qwen2 and GPT models, with an ASR of 45.99%.
Abstract
Textual adversarial attacks mislead NLP models, including Large Language Models (LLMs), by subtly modifying text. While effective, existing attacks often require knowledge of the victim model, extensive queries, or access to training data, limiting real-world feasibility. To overcome these constraints, we introduce the \textbf{Victim Data-based Adversarial Attack (VDBA)}, which operates using only victim texts. To prevent access to the victim model, we create a shadow dataset with publicly available pre-trained models and clustering methods as a foundation for developing substitute models. To address the low attack success rate (ASR) due to insufficient information feedback, we propose the hierarchical substitution model design, generating substitute models to mitigate the failure of a single substitute model at the decision boundary. Concurrently, we use diverse adversarial example…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Topic Modeling · Advanced Graph Neural Networks
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Byte Pair Encoding · Attention Dropout · Softmax · Residual Connection · Linear Layer · Weight Decay · Adam · Multi-Head Attention
