Language Model Security Database

Frontier Large Language Models (LLMs) contain a safeguard bypass vulnerability where safety filters fail to reliably block requests for dual-use, in silico biology tasks. This allows novice users with no specialized training to access restricted, expert-level biological protocols (e.g., virology troubleshooting, pathogen capabilities, novel biological agent construction). The models' safety mechanisms fail to trigger or are trivially bypassed under realistic extended interaction conditions…

Large Vision-Language Models (LVLMs) possessing Optical Character Recognition (OCR) capabilities are vulnerable to a "Text Distraction Jailbreaking" (Text-DJ) attack. The vulnerability exploits a gap between the model's visual text extraction and its safety alignment mechanisms. By converting a decomposed harmful textual query into images and embedding these images within a grid of semantically irrelevant "distraction" text images, an attacker can bypass safety filters. The model's OCR…

Large Language Models (LLMs) are vulnerable to safety alignment bypasses via classical and obscure language contexts, most notably Classical Chinese, Latin, and Sanskrit. This vulnerability stems from a "High Capability-Low Alignment" distribution shift: models possess sophisticated semantic comprehension of historical languages due to extensive pre-training on historical archives and literature, but lack corresponding safety guardrails which are predominantly optimized for modern languages…

Graph Neural Network (GNN)-based social bot detection systems are vulnerable to an Optimal Transport (OT)-guided evasion attack that manipulates local graph structures under realistic domain constraints. By modeling $k$-hop ego-neighborhoods as probability measures over spatio-temporal features, an attacker can compute an optimal transport plan to identify "cloak templates" (existing bots near the decision boundary that are misclassified as humans). The attacker can then decode this plan into…

Large Language Models (LLMs) deployed as Role-Playing Agents (RPAs) are vulnerable to Persona-Targeted Jailbreaks. When an LLM is prompted to adopt a persona—particularly characters with negative, risky, or villainous traits—the model's optimization for role fidelity frequently overrides its foundational safety alignment. Attackers can exploit this vulnerability by synthesizing queries that leverage the specific narrative background, ideology, or psychological vulnerabilities of the assigned…

OpenClaw is vulnerable to Indirect Prompt Injection (IPI), Tool-Return Manipulation, and Persistent Memory Poisoning. The agent incorporates untrusted external content (e.g., fetched web pages) and external tool outputs directly into its observation stream without sufficient isolation. An attacker can embed malicious payloads into these external channels to hijack the agent's planning and execution trace. This allows the attacker to silently trigger high-privilege actions via OpenClaw's Skills…

Large Vision-Language Models (LVLMs) are vulnerable to a Stage-wise Attention-Guided Attack (SAGA) that allows for the generation of highly transferable, imperceptible adversarial examples. The vulnerability stems from a positive correlation between regional cross-modal attention scores and adversarial loss sensitivity in LVLMs. An attacker can exploit this by extracting an attention map from a surrogate open-source model (e.g., Qwen3-VL) to identify high-attention "hotspots." SAGA utilizes a…

LLM-as-a-Judge systems utilizing natural language rubrics are vulnerable to Rubric-Induced Preference Drift (RIPD). This vulnerability allows an attacker (or a flawed optimization process) to refine evaluation rubrics such that they maintain high agreement with human references on standard validation benchmarks while inducing systematic, directional preference degradation on unseen target domains. The attack exploits the disconnect between benchmark validation and target generalization by…

Large Language Models (LLMs) exhibit a cross-lingual safety vulnerability driven by a dependency on a sparse subset of "Shared Safety Neurons" (SS-Neurons) anchored in high-resource (HR) languages, typically English. Non-high-resource (NHR) languages lack autonomous safety mechanisms and rely on projecting inputs onto this English-aligned safety manifold to trigger refusals. Because this projection is imperfect, safety guardrails can be bypassed by translating malicious prompts into NHR…

A vulnerability in multi-category safety-guidance mechanisms (such as Safe Latent Diffusion [SLD] and SAFREE) for Text-to-Image (T2I) diffusion models allows attackers to bypass safety filters and generate restricted content via "Harmful Conflicts." Existing safety methods aggregate multiple harmful keyword categories (e.g., hate, violence, sexual) into a single unified safety direction in the latent or text space. Because distinct harmful categories possess incompatible safety directions…