# AttentionSwitch: Жалпы тілдік модельдер үшін Жеделдік-бас тарту қорғауы: Назары аудару басшыларын таңдау мен мастауын бірлесіп үйрену арқылы

> Толық мәтін ағылшын тілінде

**Published:** 2026 ж. 9 қараша
**Author:** Doniyorkhon Obidov, Honggang Yu, Xiaolong Guo, Kaichen Yang
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/kk/articles/attentionswitch-jailbreak-defense-for-llms-via-joint-learning-of-sparse

arXiv:2611.08752v1 Элдеу түрі: жаңа Түйін: Үлкен тілдік модельдер кең таскерлерде керемет нәтижелерге жетті, бірақ олар қауіпсіздік механизмдерін алып тастайтын және зардапты немесе күтілмеген нәтижелерді тудыратын жеделдік-бас тарту шабуылдарына сезімтал болады. Қауіпсіздік…

arXiv:2611.08752v1 Announce Type: new Abstract: Large language models have achieved remarkable performance across a wide range of tasks, yet they remain susceptible to jailbreak attacks that circumvent safety mechanisms and elicit harmful or unintended outputs. Although safety can be improved through model fine-tuning, such approaches can be computationally expensive and may degrade previously acquired capabilities through catastrophic forgetting. Recent jailbreak defenses have therefore explored targeted interventions on specific model components associated with unsafe behavior. However, existing approaches typically decouple two inherently related decisions: where to intervene and how to modify the selected components. Such sequential optimization may be suboptimal because the optimal intervention location can depend on the intervention applied there, and vice versa. In this work, we introduce AttentionSwitch, the first jailbreak defense to jointly optimize intervention sites and strengths within a frozen LLM. Specifically, AttentionSwitch learns a sparse set of attention heads together with query-conditioned multiplicative scaling factors, allowing the outputs of the selected heads to be either attenuated or amplified without altering the directions of their representations. Across popular language models and jailbreak attacks, AttentionSwitch outperforms existing defenses, with particularly strong results when training data is scarce.

---

*Материал бастапқы дереккөзден алынған: [arXiv — cs.AI](https://arxiv.org/abs/2611.08752). Түпнұсқаны ашу.*

**Тегтер:** #arXiv

---

*AI Qazaqstan — non-commercial informational portal about AI in Kazakhstan. Machine translations and summaries are AI-generated.*