LATIDIA · Ciberseguridad

AttentionSwitch: Jailbreak Defense for LLMs via Joint Learning of Sparse Attention-Head Selection and Scaling

arXiv: 2611.08752v1Tipo de anuncio: Cross Resumen: Los modelos de lenguaje grandes han logrado un rendimiento notable en una amplia gama de tareas, pero siguen siendo susceptibles a ataques de jailbreak que eluden el mecanismo de seguridad

La noticia

arXiv:2611.08752v1 Announce Type: cross 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