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…

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…

Large Language Models (LLMs) are vulnerable to a jailbreak attack termed Paper Summary Attack (PSA). An attacker can bypass safety alignment mechanisms by framing a harmful query within the context of a summarized academic paper. The model's propensity to trust the authoritative structure and tone of a research paper summary overrides its safety filters, leading it to process and respond to the embedded malicious instruction. The vulnerability is particularly potent when using summaries of…

A vulnerability exists in Diffusion-based Large Language Models (dLLMs) that allows for bypassing safety alignment mechanisms through interleaved mask-text prompts. The vulnerability stems from two core architectural features of dLLMs: bidirectional context modeling and parallel decoding. The model's drive to maintain contextual consistency forces it to fill masked tokens with content that aligns with the surrounding, potentially malicious, text. The parallel decoding process prevents dynamic…

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…

Adversarial Activation Patching enables the induction of emergent deceptive behaviors in safety-aligned transformer-based Large Language Models (LLMs). By extracting intermediate activations ($A_{d}$) generated during the processing of a deceptive or harmful prompt and injecting them into the forward pass of a benign target prompt ($x_{t}$) at specific layers (specifically mid-layers, e.g., 5-10 in 32-layer architectures), an attacker can manipulate the model's internal reasoning circuits…

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) employing internal security mechanisms based on linearly separable embeddings in intermediate layers are vulnerable to a generative adversarial attack. The CAVGAN framework exploits this vulnerability by generating adversarial perturbations that misclassify malicious inputs as benign, allowing the attacker to bypass the LLM's safety filters and elicit harmful outputs.

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…

Multimodal Large Language Models (MLLMs) are vulnerable to visual contextual attacks, where carefully crafted images and accompanying text prompts can bypass safety mechanisms and elicit harmful responses. The vulnerability stems from the MLLM's ability to integrate visual and textual context to generate outputs, allowing attackers to create realistic scenarios that subvert safety filters. Specifically, the attack leverages image-driven context injection to construct deceptive multi-turn…