Language Model Security Database

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…

Vision Language Models (VLMs) are vulnerable to visual prompt injection attacks via text-to-image obfuscation. While these models often possess safety guardrails for standard text-based inputs, they fail to apply equivalent safety alignment to textual instructions embedded visually within an image. An attacker can overlay malicious instructions (e.g., requests for illegal acts, hate speech) onto an image file and submit it to the model. The model’s Optical Character Recognition (OCR) or visual…

A bimodal adversarial attack, PBI-Attack, can manipulate Large Vision-Language Models (LVLMs) into generating toxic or harmful content by iteratively optimizing both textual and visual inputs in a black-box setting. The attack leverages a surrogate LVLM to inject malicious features from a harmful corpus into a benign image, then iteratively refines both image and text perturbations to maximize the toxicity of the model’s output as measured by a toxicity detection model (Perspective API or…

Large Vision-Language Models (VLMs) are vulnerable to a novel black-box jailbreak attack, IDEATOR, which leverages a separate VLM to generate malicious image-text pairs. The attacker VLM iteratively refines its prompts based on the target VLM's responses, bypassing safety mechanisms by generating contextually relevant and visually subtle malicious prompts.

A vulnerability allows bypassing safety filters in text-to-image (T2I) models using a multi-agent framework ("Atlas") powered by Large Language Models (LLMs). Atlas iteratively generates and refines prompts, leveraging a Vision-Language Model (VLM) to assess filter activation and an LLM to select effective prompts that maintain semantic similarity to the original, malicious prompt while evading the filter. This enables the generation of images containing unsafe content.

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…

A vulnerability in multimodal large language models (MLLMs) allows for efficient jailbreaking attacks by leveraging visual input to bypass safety mechanisms. The attack constructs a multimodal model by adding a visual module to the target LLM, then uses a modified PGD algorithm to optimize visual input to generate jailbreaking embeddings. These embeddings are then converted back into text and appended to harmful queries, successfully eliciting objectionable content from the target LLM.

Multimodal Large Language Models (MLLMs) in multi-agent environments are vulnerable to "infectious jailbreak," where a single adversarial image injected into the memory of one agent can cause nearly all agents to exhibit harmful behaviors exponentially fast through agent-to-agent interaction. The adversarial image acts as a "virus," spreading via pairwise chats without further attacker intervention.