Language Model Security Database

Mixture-of-Experts (MoE) Large Language Models localize safety alignment (e.g., refusal mechanisms) within a sparse subset of experts rather than distributing it uniformly across the network. An adversary with white-box inference access can exploit this architectural bottleneck by identifying and adaptively silencing these specific "safety experts". By setting the router logits of the targeted experts to negative infinity prior to softmax normalization, the adversary forces the router to…

A vulnerability exists in the safety alignment process of Mixture-of-Experts (MoE) Large Language Models (LLMs) when subjected to standard full-parameter fine-tuning. The vulnerability, identified as an "alignment shortcut," occurs when the model minimizes safety loss by modifying routing mechanisms to avoid activating unsafe experts, rather than updating the parameters of the experts responsible for generating harmful content. Consequently, unsafe representations remain latent within the…

Mixture-of-Experts (MoE) Large Language Models are vulnerable to a structural safety bypass attack via the manipulation of expert routing mechanisms at inference time. Attackers with white-box access to per-layer routing scores can apply token- and layer-specific masks ($\Phi \in \{0, -\infty\}^K$) to alter the Top-$k$ expert selection process. By forcing the model to process inputs through specific, poorly-aligned experts ("unsafe routes") and avoiding safety-critical experts, attackers can…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

A logical vulnerability exists in LLM security proxies and guardrails that utilize weighted-average aggregation algorithms for multi-turn risk scoring. The scoring logic exhibits a mathematical "ceiling property" where the cumulative conversation-level risk score converges to the per-turn score regardless of the number of interaction turns ($n$). Consequently, the aggregated score is bounded by the maximum single-turn score ($cum \leq \max(s_i)$). This allows remote attackers to bypass…

LLM-based Code Interpreter Agents, including OpenInterpreter and OpenCodeInterpreter, are vulnerable to sandbox evasion and arbitrary code execution via Natural Language Disguise and Contextual Channel Injection. Attackers can bypass Abstract Syntax Tree (AST) static analysis and explicit input guardrails by transforming malicious code logic into descriptive natural language instructions (Code Descriptions), which successfully evade syntax-layer blocks. Additionally, attackers can bypass input…

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…

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…