DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization

[2412.07672] DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2412.07672 (cs) [Submitted on 10 Dec 2024 ( v1 ), last revised 16 Aug 2026 (this version, v2)] Title: DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization Authors: Xiaoqun Liu , Weiming Qi , Qiben Yan View a PDF of the paper titled DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization, by Xiaoqun Liu and Weiming Qi and Qiben Yan View PDF HTML (experimental) Abstract: 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. Comments: Accepted by The 29th International Symposium on Research in Attacks, Intrusions and Defenses (RAID) 2026 Subjects: Cryptography and Security (cs.CR) ; Computation and Language (cs.CL) Cite as: arXiv:2412.07672 [cs.CR] (or arXiv:2412.07672v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2412.07672 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qiben Yan [ view email ] [v1] Tue, 10 Dec 2024 17:02:28 UTC (1,183 KB) [v2] Sun, 16 Aug 2026 15:06:14 UTC (1,496 KB) Full-text links: Access Paper: View a PDF of the paper titled DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization, by Xiaoqun Liu and Weiming Qi and Qiben Yan View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2024-12 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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