Language Model Security Database

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

Retrieval-Augmented Generation (RAG) systems utilizing dense (e.g., BERT-based) or sparse (e.g., BM25) retrievers are vulnerable to black-box adversarial prompt injection attacks. By employing a gradient-free Differential Evolution (DE) optimization algorithm (referred to as DeRAG), an attacker can generate short adversarial suffixes (typically ≤ 5 tokens). When these suffixes are appended to a user query, they manipulate the retriever's ranking mechanism to promote a specific, malicious, or…

Reasoning-capable Large Language Models (LLMs) are vulnerable to a class of indirect prompt injection known as Copy-Guided Attacks (CGA). This vulnerability exploits the intrinsic behavior of reasoning models to copy tokens from the input prompt (such as variable names, function identifiers, or code snippets) into their intermediate reasoning traces (Chain-of-Thought). By embedding adversarial trigger sequences into external payloads—specifically within data the model is expected to analyze—an…

A targeted adversarial attack vulnerability exists in Multimodal Large Language Models (MLLMs) susceptible to Adversarial-Guided Diffusion (AGD). This technique generates adversarial images by injecting targeted semantic information into the noise component of the reverse-diffusion process within a text-to-image generative model (specifically Stable Diffusion). Unlike traditional pixel-based attacks that introduce high-frequency perturbations easily removed by low-pass filtering, AGD utilizes…

Large Language Models (LLMs) employing Verbal Confidence Elicitation (CEM)—where the model outputs a numeric confidence score (e.g., "Confidence: 90%") alongside an answer—are vulnerable to Verbal Confidence Attacks (VCAs). Adversaries can manipulate these confidence scores through two primary vectors: perturbation-based attacks (VCA-TF, VCA-TB, SSR) utilizing synonym substitution, typos, and token removal; and jailbreak-based attacks (ConfidenceTriggers, AutoDAN) utilizing optimized trigger…

Large Language Models (LLMs) equipped with native code interpreters are vulnerable to Denial of Service (DoS) via resource exhaustion. An attacker can craft a single prompt that causes the interpreter to execute code that depletes CPU, memory, or disk resources. The vulnerability is particularly pronounced when a resource-intensive task is framed within a plausibly benign or socially-engineered context ("indirect prompts"), which significantly lowers the model's likelihood of refusal compared…

A resource consumption vulnerability exists in multiple Large Vision-Language Models (LVLMs). An attacker can craft a subtle, imperceptible adversarial perturbation and apply it to an input image. When this image is processed by an LVLM, even with a benign text prompt, it forces the model into an unbounded generation loop. The attack, named RECALLED, uses a gradient-based optimization process to create a visual perturbation that steers the model's text generation towards a predefined…

A vulnerability exists in Large Language Model (LLM)-based Multi-Agent Systems (MAS) that allows a malicious agent to covertly disrupt collaborative decision-making processes without triggering standard safety filters or anomaly detection. This "intention-hiding" attack occurs when an agent adopts a persona that appears linguistically fluent and role-consistent but strategically steers the group toward incorrect outcomes or resource exhaustion. The attacker leverages specific semantic…

Large Language Models (LLMs) are vulnerable to obfuscation-based jailbreak attacks using the MetaCipher framework. MetaCipher employs a reinforcement learning algorithm to iteratively select from a pool of 21 ciphers to encrypt malicious keywords within prompts, evading standard safety mechanisms that rely on keyword detection. The framework adaptively learns optimal cipher choices to maximize the success rate of the jailbreak, even against LLMs with reasoning capabilities. Successful attacks…

Large Language Model (LLM) agents capable of invoking external APIs are vulnerable to intent integrity violations. When an agent receives natural language instructions that are ambiguous, underspecified, or contain values not supported by the underlying API schema, the agent frequently fails to preserve user intent. Instead of rejecting the request or asking for clarification, the model may hallucinate parameter values, map unsupported requests to unsafe defaults, or execute actions on…