Large Language Models (LLMs) encode parametric knowledge but can exhibit format bias when resolving knowledge conflicts. When evidence is rendered as text, tables, or knowledge graphs, LLMs can favor certain formats despite unchanged semantic content. Existing work mainly diagnoses this bias, while existing inference-time mitigation remains tied to a single rendered prompt. We propose FaDe, a cross-format knowledge distillation framework that treats evidence format as a controllable nuisance variable and marginalizes teacher predictions over controlled format assignments. FaDe constructs a format-marginalized soft target from these renderings, then trains the student with an asymmetric two-path objective that combines heterogeneous-format debiasing with all-text anchoring. Experiments on ConflictBank and two QA-derived controlled conflict sets with four model backbones show FaDe reduces format bias while improving dual-source coverage and cross-format stability under single-pass inference. Our code is available at https://github.com/Kaia91/FaDe.

Security alignment enables the Large Language Model (LLM) to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection mechanism of the LLM, and propose a framework that combines attack and defense. Our method is based on the linearly separable property of LLM intermediate layer embedding, as well as the essence of jailbreak attack, which aims to embed harmful problems and transfer them to the safe area. We utilize generative adversarial network (GAN) to learn the security judgment boundary inside the LLM to achieve efficient jailbreak attack and defense. The experimental results indicate that our method achieves an average jailbreak success rate of 88.85% across three popular LLMs, while the defense success rate on the state-of-the-art jailbreak dataset reaches an average of 84.17%. This not only validates the effectiveness of our approach but also sheds light on the internal security mechanisms of LLMs, offering new insights for enhancing model security.Warning: This paper contains some harmful text examples.