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…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

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…

Large Audio-Language Models (LALMs) are vulnerable to a stealthy adversarial jailbreak attack, AdvWave, which leverages a dual-phase optimization to overcome gradient shattering caused by audio discretization. The attack crafts adversarial audio by adding perceptually realistic environmental noise, making it difficult to detect. The attack also dynamically adapts the adversarial target based on the LALM's response patterns.

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…

LLM-based planning modules in embodied AI systems are vulnerable to Policy Executable (POEX) jailbreak attacks. Attackers can inject carefully crafted adversarial suffixes into user instructions, causing the LLM to generate and execute harmful policies in both simulated and real-world environments. The attacks bypass safety mechanisms by using optimized, human-readable suffixes that evade perplexity-based detection.

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.

Refusal-trained Large Language Models (LLMs) show decreased safety when deployed as browser agents compared to their performance in chatbot settings. Attack methods effective at jailbreaking LLMs in chat contexts also successfully bypass safety mechanisms in browser agents, leading to the execution of harmful behaviors. This vulnerability stems from a lack of generalization of safety training to agentic, real-world interaction scenarios and the increased context available to the agent (browser…

Large language models (LLMs) controlling robots are vulnerable to jailbreaking attacks. The ROBOPAIR algorithm demonstrates that malicious prompts can bypass safety mechanisms, causing robots to perform harmful physical actions. This vulnerability exploits the LLM's reliance on textual prompts and its potential lack of sufficient contextual understanding to prevent unsafe commands. The attack is effective across different access levels.

The ALERT-Motion framework demonstrates a vulnerability in text-to-motion (T2M) models where an attacker can craft subtly modified text prompts (adversarial prompts) that cause the model to generate motions significantly different from those intended by the benign prompt, yet semantically similar to a target motion specified by the attacker. The attack leverages a large language model (LLM) to autonomously generate these adversarial prompts, bypassing simple keyword-based detection mechanisms…