Language Model Security Database

Large Language Models (LLMs) are vulnerable to multi-turn persuasive conversational attacks that induce the adoption of counterfactual beliefs. By leveraging the Source–Message–Channel–Receiver (SMCR) communication framework, attackers can systematically erode a model's confidence in established facts and compel the model to output misinformation. Specific attack vectors include manipulating source attribution (authority framing), message content (logical, credibility, or emotional appeals)…

Large Language Models (LLMs) deployed using Multiple-Choice Question Answering (MCQA) interfaces or choice-based selection structures are vulnerable to Option Injection. By appending a task-irrelevant candidate choice (e.g., Option E) containing a steering directive—specifically utilizing threat framing (penalty coercion) or bonus framing (reward inducement)—an attacker can hijack the model's decision-making process. The vulnerability stems from a flaw in attention allocation: the model's…

AutoArgue, an LLM-based evaluation framework for Retrieval-Augmented Generation (RAG) systems, is susceptible to evaluation subversion attacks due to the public availability of its judging prompts and reference data structures. An adversarial RAG system (exemplified by the "Crucible" probe) can incorporate "insider knowledge" of the evaluation logic directly into its generation pipeline. By wrapping the generation process with the evaluator's specific prompts, the system can pre-filter…

A vulnerability in the prompt-based persona conditioning of clinical Large Language Models (LLMs) allows system-level role prompts (e.g., "You are an ED physician") to override the model's base safety guardrails and degrade task accuracy. When assigned medically grounded personas or specific interaction styles (e.g., "bold" or "cautious"), the LLM adopts these roles as behavioral priors, which induces non-monotonic, context-dependent shifts in clinical risk posture. While improving performance…

LLM-based navigation agents, including NavGPT and prompt-tuned outdoor agents, are vulnerable to adaptive prompt injection attacks. This vulnerability allows remote attackers to hijack the physical movement of the agent by embedding optimized malicious instructions into benign natural language inputs. The issue arises because the agents parse user instructions to generate executable plans without sufficient separation between control logic and untrusted input. The PINA (Prompt Injection Attack…

Large Language Models (LLMs) contain a resource consumption vulnerability termed "Overflow," wherein specific non-adversarial, plain-text prompts trigger excessive text generation that saturates the model's output token budget. This vulnerability exploits the model's alignment towards helpfulness and exhaustiveness, alongside tokenizer inefficiencies (e.g., zero-width characters), to force the generation of maximum-length responses (often exceeding 5,000 tokens) from short inputs. This differs…

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

A malicious model supply chain vulnerability exists involving a technique termed Adversarial Contrastive Learning (ACL) for Large Language Model (LLM) quantization attacks. This vulnerability allows an attacker to publish a model that appears benign and preserves high utility in full precision (e.g., BF16 or FP32) but exhibits malicious behaviors—such as jailbreak, over-refusal, or advertisement injection—immediately upon zero-shot quantization (e.g., INT8, FP4, or NF4).

Large Language Models (LLMs) enabled with Function Calling (FC) capabilities are vulnerable to adversarial query rewriting and semantic manipulation. Standard FC models, typically trained via Supervised Fine-Tuning (SFT) on static datasets, fail to generalize against adversarial inputs that deviate from fixed distribution patterns. An attacker can exploit this by crafting queries that are semantically similar to valid requests but engineered to induce "bad cases," such as incorrect tool…

The STEP-LLM framework, utilized for generating Computer-Aided Design (CAD) STEP files (ISO 10303) from natural language, exhibits a safety alignment vulnerability during the fine-tuning of base Large Language Models (specifically Llama-3.2-3B-Instruct and Qwen-2.5-3B). The training pipeline employs Depth-First Search (DFS) reserialization and Reinforcement Learning (RL) with Scaled Chamfer Distance rewards to optimize for geometric fidelity and syntactic validity of Boundary Representation…