Language Model Security Database

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…

A vulnerability exists in the handling of chat templates within open-weight Large Language Model (LLM) distribution formats, specifically GGUF files. Chat templates are executable Jinja2 programs stored as metadata (typically tokenizer.chat_template) alongside model weights. An attacker can modify a legitimate model's template to include conditional logic that detects specific trigger phrases in user input. When triggered, the template injects malicious system instructions or context into the…

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…

Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems deployed in clinical workflows are vulnerable to direct and indirect (RAG-mediated) medical prompt injection attacks. Attackers can embed malicious instructions within user queries or external retrieved documents (such as poisoned clinical guidelines or PDFs). By exploiting "authority framing" (e.g., formatting the payload as a clinical guideline update or an editor's note), the injections successfully bypass generic…

Large language models (LLMs) exhibit a "Causal Bypass" vulnerability during Chain-of-Thought (CoT) prompting, where the generated reasoning text does not causally determine the model's final output. Instead of utilizing the explicit CoT tokens, the model routes decision-critical computation through latent, implicit pathways. This allows the visible reasoning trace to function as an unfaithful, post-hoc rationalization rather than an actual representation of the model's internal logic…