Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, under what circumstances SAEs derive most fine-grained latent features for safety—a low-frequency concept domain—remains unexplored. Two key challenges exist: identifying SAEs with the greatest potential for generating safety domain-specific features, and the prohibitively high cost of detailed feature explanation. In this paper, we propose Safe-SAIL, a unified framework for interpreting SAE features in safety-critical domains to advance mechanistic understanding of large language models. Safe-SAIL introduces a pre-explanation evaluation metric to efficiently identify SAEs with strong safety domain-specific interpretability, and reduces interpretation cost by 55% through a segment-level simulation strategy. Building on Safe-SAIL, we train a comprehensive suite of SAEs with human-readable explanations and systematic evaluations for 1,758 safety-related features spanning four domains: pornography, politics, violence, and terror. Using this resource, we conduct empirical analyses and provide insights on the effectiveness of Safe-SAIL for risk feature identification and how safety-critical entities and concepts are encoded across model layers. All models, explanations, and tools are publicly released in an open-source toolkit at https://anonymous.4open.science/r/Safe-SAIL/.

Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks. To mitigate these risks, existing detection methods are essential, yet they face two major challenges: generalization and accuracy. While learning-based methods trained on specific attacks fail to generalize to unseen attacks, learning-free methods based on hand-crafted heuristics suffer from limited accuracy and reduced efficiency. To address these limitations, we propose Learning to Detect (LoD), a learnable framework that eliminates the need for any attack data or hand-crafted heuristics. LoD operates by first extracting layer-wise safety representations directly from the model’s internal activations using Multi-modal Safety Concept Activation Vectors classifiers, and then converting the high-dimensional representations into a one-dimensional anomaly score for detection via a Safety Pattern Auto-Encoder. Extensive experiments demonstrate that LoD consistently achieves state-of-the-art detection performance (AUROC) across diverse unseen jailbreak attacks on multiple LVLMs, while also significantly improving efficiency. Code is available at https://github.com/ShuangLiangX/Learning-to-Detect.

Multilingual safety guardrails are essential for deployed LLMs, yet they perform poorly on low-resource languages. Prior work typically relies on large-scale multilingual safety data synthesis, which incurs substantial training costs. Cross-lingual transfer offers a cheaper alternative using limited parallel data, and recent work shows that aligning only last-token hidden states can improve multilingual safety. However, although effective for query-only (Q) risk detection, last-token alignment consistently degrades query-response (QR) detection in multilingual settings, limiting its applicability in practical guardrail systems where Q and QR serve as pre-generation and post-generation safety barriers, respectively. In this paper, we propose Q-Align, a method that decouples query intent from response evaluation by aligning only at the query boundary. Specifically, for each parallel sample, Q-Align extracts the hidden state right after the query and aligns multilingual query representations toward a confidence-weighted anchor, allowing languages with stronger intent understanding to guide transfer. We further apply language grouping based on geographic proximity to stabilize cross-lingual alignment. Experiments demonstrate that Q-Align consistently improves multilingual safety detection in both Q and QR scenarios, raising QR average F1 from 74.70% to 80.79% on Polyguard-Qwen, while preserving English performance and generalizing to languages unseen during training.

Large language models (LLMs) remain vulnerable to jailbreak attacks that exhibit diverse prompts and rapidly evolving strategies. Existing defenses often operate at the level of prompts or predefined attack methods, limiting their ability to generalize to unseen attacks that share similar underlying structure but differ in wording. In this paper, we investigate whether seemingly different jailbreak attacks exhibit shared SAE feature co-activation patterns, and propose a motif-based framework that models attacks through these recurring patterns. Specifically, we first extract sparse autoencoder (SAE) features from jailbreak queries and discover motifs as recurring co-activation patterns via nonnegative matrix factorization. We show that motifs capture stable relationships across attack methods and generalize to unseen attacks. Building on this structure, we synthesize new jailbreak queries by composing motif-guided attack patterns. We then use the generated queries to construct preference-training data for Direct Preference Optimization (DPO). Experiment results show that SAE motifs serve as a stable and transferable interface for understanding and detecting jailbreak attacks, as well as enabling effective defenses that generalize beyond fixed prompt templates. Training target models with motif-guided synthetic data improves robustness to unseen attacks with modest changes in utility. Our work provides a practical methodology for advancing data-centric LLM safety through structured modeling of jailbreak attacks.

Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resource languages. We attribute this issue as a mismatch gap between language-agnostic semantic understanding ability and language dominant safety alignment biased toward high-resource languages. Based on above insights, we empirically identify the semantic bottleneck in LLMs: intermediate layers in which the geometry of model representations is governed primarily by shared semantic content rather than language identity. Then, we propose Language-Agnostic Semantic Alignment (LASA), which anchors safety alignment directly in semantic bottlenecks. Experiments show that LASA substantially improves safety across all languages: average attack success rate (ASR) drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwen3 Instruct models (7B–32B). Besides, our analysis and method offer a representation-level perspective on LLM safety, suggesting that safety alignment requires anchoring safety understanding not in surface text, but in the model’s language-agnostic semantic space.