Language Model Security Database

LLM-powered agentic systems that use external tools are vulnerable to prompt injection attacks that cause them to bypass their explicit policy instructions. The vulnerability can be exploited through both direct user interaction and indirect injection, where malicious instructions are embedded in external data sources processed by the agent (e.g., documents, API responses, webpages). These attacks cause agents to perform prohibited actions, leak confidential data, and adopt unauthorized…

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…

Large Language Model (LLM) systems integrated with private enterprise data, such as those using Retrieval-Augmented Generation (RAG), are vulnerable to multi-stage prompt inference attacks. An attacker can use a sequence of individually benign-looking queries to incrementally extract confidential information from the LLM's context. Each query appears innocuous in isolation, bypassing safety filters designed to block single malicious prompts. By chaining these queries, the attacker can…

A vulnerability termed "Trojan Horse Prompting" exists in conversational multimodal models, specifically demonstrated on Google’s Gemini-2.0-flash-preview-image-generation. The vulnerability allows an attacker to bypass safety alignment mechanisms (RLHF and SFT) by manipulating the structural protocol of the conversational API. Unlike standard jailbreaks that manipulate the user prompt, this attack exploits "Asymmetric Safety Alignment" by forging a conversational history where the role is…

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

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…

A vulnerability exists in Large Language Diffusion Models (LLDMs) due to their parallel denoising architecture. The PArallel Decoding (PAD) jailbreak attack exploits this architecture by injecting multiple, semantically innocuous "sequence connectors" (e.g., "Step 1:", "First") at distributed locations within the initial masked sequence. During the parallel denoising process, these injected tokens act as anchor points that bias the probability distribution of adjacent token predictions. This…

A vulnerability exists where Large Language Models (LLMs) can be manipulated by prepending a specially crafted 'persona prompt', often in the system prompt. These persona prompts cause the model to shift its attention from sensitive keywords in a harmful request to the stylistic instructions of the persona. This weakens the model's safety alignment, significantly reducing its refusal rate for harmful queries. The vulnerability is particularly severe because these persona prompts have a…

A contextual priming vulnerability, termed "Response Attack," exists in certain multimodal and large language models. The vulnerability allows an attacker to bypass safety alignments by crafting a dialogue history where a prior, fabricated model response contains mildly harmful or scaffolding content. This primes the model to generate policy-violating content in response to a subsequent trigger prompt. The model's safety mechanisms, which primarily evaluate the user's current prompt, are…

A vulnerability exists in Large Language Models, including GPT-3.5 and GPT-4, where safety guardrails can be bypassed using Trojanized prompt chains within a simulated educational context. An attacker can establish a benign, pedagogical persona (e.g., a curious student) over a multi-turn dialogue. This initial context is then exploited to escalate the conversation toward requests for harmful or restricted information, which the model provides because the session's context is perceived as safe…