While large language models (LLMs) have exhibited remarkable performance across diverse natural language processing tasks, their inherent propensity to generate hallucinations poses a critical challenge to reliable deployment in high-stakes scenarios. Existing hallucination detection methods often rely on static single-layer representations or aggregated features across all layers, which often fail to capture fine-grained factual inconsistencies and thus exhibit limited generalization capability. To address this limitation, this paper proposes TemHal (Temporal Hallucination Detector), a novel detection framework that models the cross-layer evolutionary trajectory of internal representations. Specifically, TemHal tracks the dynamic representational progression of key factual tokens across selected intermediate MLP activations and employs a bidirectional temporal modeling network to identify representational patterns that differentiate factually consistent outputs from hallucinated ones. Extensive experimental results demonstrate that the proposed method achieves consistent performance improvements across multiple mainstream model architectures and diverse benchmark datasets, while also exhibiting robust cross-dataset generalization across multiple LLM backbones. Furthermore, ablation studies validate the contribution of each architectural component and confirm the importance of temporal modeling and intermediate-layer focusing for detection performance.

While multimodal large language models (MLLMs) extend model capabilities beyond text, they also make safety alignment increasingly challenging. Multimodal safety alignment methods must address cross-modal jailbreaks, safety-awareness failures, and over-sensitive refusals. However, existing methods often rely on retraining or internal-state inspection, limiting their applicability to deployed closed-source MLLMs and motivating test-time safety alignment. We analyze this setting and identify two key obstacles, utility dominance and reasoning inertia, which cause models to overlook latent risks or follow malicious reasoning trajectories. Guided by these insights, we propose **ReFrame**, a training-free multimodal input reframing framework where two agents share a lightweight locally deployed MLLM: the evidence-generation agent constructs complementary risk and utility evidence, and the rewrite-and-routing agent converts it into a safe proxy prompt and image-routing decision before calling the downstream MLLM, without modifying it or accessing its internal information. Experiments across multiple MLLMs and benchmarks show that ReFrame improves jailbreak defense, safety awareness, and oversensitivity reduction while preserving multimodal utility.