Language Model Security Database

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…

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…

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…

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…

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…

Large Language Models (LLMs) that utilize byte-stream parsing or structural extraction to process PDF files—specifically the OpenAI GPT and Anthropic Claude families—are vulnerable to adversarial text injection via imperceptible "phantom tokens." This vulnerability exploits the disconnect between how PDF viewers render documents for humans (visual layer) and how LLMs extract text from the PDF operator stream (data layer). Attackers can manipulate standard PDF text-showing operators (TJ and Tj)…

A vulnerability exists in multiple large language and multimodal models that allows for the bypass of safety filters through the use of code-mixed prompts with phonetic perturbations. An attacker can craft a prompt in a code-mixed language (e.g., Hinglish) and apply phonetic misspellings to sensitive keywords (e.g., spelling "hate" as "haet"). This technique causes the model's tokenizer to parse the sensitive word into benign sub-tokens, preventing safety mechanisms from flagging the harmful…

GhostPrompt demonstrates a vulnerability in multimodal safety filters used with text-to-image generative models. The vulnerability allows attackers to bypass these filters by using a dynamic prompt optimization framework that iteratively generates adversarial prompts designed to evade both text-based and image-based safety checks while preserving the original, harmful intent of the prompt. This bypass is achieved through a combination of semantically aligned prompt rewriting and the injection…

Speech-LLMs Qwen2-Audio (7B-Instruct) and Granite-Speech (3.2-8b) are vulnerable to universal acoustic adversarial attacks. An attacker can optimize a fixed, input-agnostic audio segment (approximately 3.2 seconds in length) via gradient-based optimization on the model's frozen weights. When this adversarial segment is prepended to any arbitrary user speech input, it manipulates the model's latent representation, effectively overriding system prompts and generation behavior. This vulnerability…

Multimodal large language models (MLLMs) are vulnerable to implicit jailbreak attacks that leverage least significant bit (LSB) steganography to conceal malicious instructions within images. These instructions are coupled with seemingly benign image-related text prompts, causing the MLLM to execute the hidden malicious instructions. The attack bypasses existing safety mechanisms by exploiting cross-modal reasoning capabilities.