LATIDIA · Ciberseguridad

Purificación por vía indirecta para LLM optimizados para LoRA mediante proyección en el espacio nulo.

arXiv:2610.00685v2 Announce Type: replace-cross Resumen: Con la rápida adopción de modelos de lenguaje grandes (LLM) y métodos de ajuste de parámetros eficientes (PEFT), el riesgo de ataques de puerta trasera se ha vuelto más grave. E

La noticia

arXiv:2610.00685v2 Announce Type: replace-cross Abstract: With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more severe. Existing backdoor purification methods typically rely on at least one of the strong assumptions, such as prior knowledge of triggers, access to clean references, or aggressive retraining, and they often lack comprehensive evaluations. These constraints substantially limit their practical applicability. To overcome these challenges, our work proposes purifying LoRA-tuned LLMs without these assumptions and even without post-hoc retraining of the suspect parameters. Our objective is to significantly reduce the attack success rates (ASR) while preserving both (i) the base model's general capabilities