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This research exposes a critical vulnerability in LLM auto-prompt optimization, where attackers can subtly hijack model rewrites while maintaining semantic similarity to evade detection. The proposed black-box attack method significantly outperforms existing techniques across thousands of test cases on both commercial and open-source models. This finding underscores the urgent need for robust adversarial defenses as autonomous rewriting features become standard in AI systems.
Semantic-Preserving Prompt Hijacking: A Black-Box Adversarial Attack on Auto-Prompt Optimization
arXiv:2506.18756v2 Announce Type: replace Abstract: LLMs increasingly integrate auto-suggestion optimization modules, enabling them to rewrite and display user input before generating the final response. While this design aims to enhance transparency and trust, its process of autonomously selecting a single best result from multiple candidate solutions allows attackers to hijack this optimization process by inducing subtle, imperceptible semantic shifts. To address this, we propose a semantic preservation hijacking attack method based on black-box conditions: Adaptive Greedy Local Search. This method hierarchically decomposes the input text, masks key language units, and dynamically adjusts candidate replacement words at predefined semantic checkpoints. This maximizes the deviation between the model output and the original intent while strictly maintaining semantic similarity to the original text. Experimental results on commercial and open-source LLMs demonstrate that, under the same semantic similarity constraints, this method achieves a higher attack success rate than existing attack methods in over 2400 test cases. Code is available at: https://github.com/franz-chang/DOBS
Abstract:LLMs increasingly integrate auto-suggestion optimization modules, enabling them to rewrite and display user input before generating the final response. While this design aims to enhance transparency and trust, its process of autonomously selecting a single best result from multiple candidate solutions allows attackers to hijack this optimization process by inducing subtle, imperceptible semantic shifts. To address this, we propose a semantic preservation hijacking attack method based on black-box conditions: Adaptive Greedy Local Search. This method hierarchically decomposes the input text, masks key language units, and dynamically adjusts candidate replacement words at predefined semantic checkpoints. This maximizes the deviation between the model output and the original intent while strictly maintaining semantic similarity to the original text. Experimental results on commercial and open-source LLMs demonstrate that, under the same semantic similarity constraints, this method achieves a higher attack success rate than existing attack methods in over 2400 test cases. Code is available at: this https URL
12 pages, 8 figures. Accepted by the IEEE International Conference on Multimedia and Expo (ICME 2026)
Computation and Language (cs.CL); Cryptography and Security (cs.CR)
arXiv:2506.18756 [cs.CL]
(or arXiv:2506.18756v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2506.18756 Focus to learn more arXiv-issued DOI via DataCite
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