Language Model Security Database

End-to-end multimodal large language models (omni-models) that utilize a shared representation space for text and audio are vulnerable to cross-modality jailbreak transfer, a phenomenon termed the "alignment curse." Because these models are trained to strongly align audio and text embeddings in their mid-to-late layers, an attacker can reliably bypass audio-specific safety mechanisms by converting mature, text-based jailbreak prompts into audio using standard Text-to-Speech (TTS) tools. When…

A vulnerability exists in the internal representation mechanisms of Transformer-based Large Language Models (LLMs), specifically Llama-3 and Qwen series models. The vulnerability allows for high-accuracy "steering" of model outputs, effectively bypassing safety guardrails and refusal mechanisms (jailbreaking) without modifying model weights. By exploiting attention-guided feature learning, an attacker can extract a precise "concept vector" representing refusal behaviors. This is achieved by…

A vulnerability exists in LLM-based coding agents that implement modular capability extensions (often referred to as "Agent Skills") where the agent dynamically loads and executes user-provided skill packages. The vulnerability allows for Skill-Based Prompt Injection, specifically leveraging a technique known as "SkillJect." This attack decouples the malicious intent from the operational payload to bypass semantic safety filters. An attacker constructs a skill package containing: 1. Inducement…

Activation steering mechanisms employed for inference-time control of Large Language Models (LLMs) contain a vulnerability termed "Steering Externalities." When steering vectors are derived from benign datasets to enforce utility objectives—specifically "compliance" (reducing refusals for benign queries) or "instruction adherence" (e.g., enforcing JSON output formats)—and injected into the model's residual stream, they unintentionally erode safety alignment. The vulnerability arises because…

Large Language Models (LLMs), including Qwen2.5, LLaMA-3, and Baichuan2, are vulnerable to causally optimized adversarial attacks where specific interpretable prompt features are manipulated to bypass safety alignment. Research utilizing a "Causal Analyst" framework reveals that specific prompt attributes—specifically "Number of Task Steps" (increasing procedural complexity), "Positive Character" (enforcing specific personas), and "Command Tone"—act as direct causal drivers for "Answer…

A vulnerability exists in safety-aligned Large Language Models (LLMs) wherein internal safety alignment mechanisms (such as RLHF) function as unobserved causal confounders rather than erasing prohibited knowledge. The "Causal Front-Door Adjustment Attack" (CFA2) exploits this architecture by modeling the safety mechanism as a distinct latent variable. Attackers with white-box access can employ Sparse Autoencoders (SAEs) to disentangle dense internal representations into sparse features…

Large Language Models (LLMs) exhibit a vulnerability termed "chunky post-training," where the model learns spurious correlations between incidental prompt features (e.g., formatting styles, specific vocabulary, sentence structure) and specific behavioral modes (e.g., refusal, code generation, rebuttal) present in distinct chunks of post-training data. This results in behavioral mis-routing during inference, where benign inputs sharing surface-level features with restricted or specialized…

A detection bypass vulnerability in the 2-Sigma clinical training platform allows users to evade the system's two-layer, linguistic-feature-based jailbreak detection mechanism. The detection framework relies heavily on four surface-level linguistic features (Professionalism, Medical Relevance, Ethical Behavior, and Contextual Distraction) to classify malicious inputs. Attackers can bypass these filters by crafting prompts that maintain professional tone and apparent medical relevance but…

Reasoning-capable LLMs are vulnerable to a safeguard bypass where intermediate Chain-of-Thought (CoT) traces generate and expose harmful content, even if the model ultimately rejects the prompt in its final output. Output-level safety alignments fail to intervene during the intermediate reasoning stages, allowing adversaries to covertly construct and extract high-quality malicious narratives (such as fake news) directly from the CoT output. Mechanistic analysis reveals this divergence stems…

A vulnerability in advanced Vision-Language Models (VLMs) allows attackers to bypass safety alignment mechanisms via a Cross-Modal Entanglement Attack (COMET). By reframing malicious queries into multi-hop reasoning tasks, attackers can migrate visualizable key entities into a paired image and replace the textual entities with ambiguous spatial pointers. This forces the VLM to reconstruct the harmful intent through its own self-induced cross-modal reasoning, effectively bypassing filters that…