Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses often require access to the model internals or additional training, limiting their applicability for service providers deployed through black-box APIs. In this paper, we propose DYNASHIELD, a moving target defense framework that improves robustness by customizing decoding hyperparameters and system prompts at inference time. DYNASHIELD includes two key steps: (1) it identifies decoding configurations that reduce attack success probability, and (2) it probabilistically samples from a weighted configuration pool to introduce controlled variability in model behavior. We evaluate DYNASHIELD across 7 open-source LLMs under 4 state-of-the-art jailbreak attacks, using adversarial prompts from AdvBench. Results show substantial reductions in attack success rate compared with 7 baseline defenses, while maintaining response quality and incurring minimal inference overhead. Because DYNASHIELD operates solely through exposed runtime controls, it requires no retraining or model-internal access. These results suggest that safety-aware dynamic decoding is a promising and practically lightweight defense mechanism for black-box LLM deployments.
Xiaoshan He, Xiaoqun Liu Michigan State University, Haodong He, Yu Wang, Yang Xu Southern University of Science and Technology
Eye movement offers valuable insights into human visual attention during assessment of machine-generated texts, yet existing research and resources in this area are limited. To bridge this gap, we introduce Gaze Responses for Evaluating AI Texts (GREAT), a comprehensive dataset capturing human eye-movement features during screen reading of passages generated by large language models (LLMs). The dataset includes raw eye-movement recordings, reading-time measurements, and post-reading evaluations for LLM-generated passage pairs, alongside rigorous validation metrics. The collected eye-movement features demonstrate strong explanatory power in predicting text quality. When integrated with negative log-likelihood (NLL), a commonly used metric for evaluating text quality, it substantially enhances model performance across all standard statistical criteria. These findings demonstrate that eye-movement can act as an effective source of information that complements probabilistic metrics, for the task of automatic text quality assessment. The full dataset and some processing code are publicly available at https://github.com/qwurd231/GREAT.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表