The remarkable instruction-following ability of modern LLMs has enabled their practical use in orchestrating workflows of tool-using agents. This, however, also exposes such agents to attacks which embed malicious instructions in text, such as indirect prompt injection (IPI). One class of approaches to defend against such attacks aims to detect malicious sentences within otherwise benign text. While a number of approaches for this have been proposed, all tend to be brittle under adaptive paraphrasing attacks, in part because they do not capture sufficient global context to effectively differentiate malicious text from benign. We address this gap by developing an approach for malicious sentence classification that is both context- and query-aware, which we then harden against paraphrasing through two adversarial training (AT) methods: feature-space robust learning and AT with simulated LLM-based paraphrasing. Moreover, we parametrize the AT variants to facilitate a smooth tradeoff between utility and attack robustness. In extensive experiments using IPI benchmarks we show that our approach outperforms state-of-the-art defense baselines under both static and adaptive paraphrasing attacks while preserving high utility. Our code is publicly available at https://github.com/tavia-liu/CAD.
Xinhang Ma Washington University, Saint Louis, William Yeoh Washington University, Saint Louis, Ning Zhang Washington University, Saint Louis, Yevgeniy Vorobeychik Washington University, St. Louis
Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models.However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models.We investigate methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) anti-distillation, or degrading the training usefulness of query responses, and (2) API watermarking, which embeds verifiable signatures in student models.We introduce several approaches for dynamically rewriting a teacher’s reasoning outputs while preserving answer correctness and semantic coherence.Two of these leverage the rewriting capabilities of LLMs, while others use gradient-based techniques.Our experiments show that a simple instruction-based rewriting approach achieves a strong anti-distillation effect while maintaining or even improving teacher performance.Furthermore, we show that our rewriting approach also enables embedding watermarks that can be reliably detectedwith essentially no false alarms.Our code is available at https://github.com/xhOwenMa/trace-rewriting.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表