Language Model Security Database

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreaking attack leveraging a "Distraction Hypothesis". The attack, termed Contrasting Subimage Distraction Jailbreaking (CS-DJ), bypasses safety mechanisms by using multiple contrasting subimages and a decomposed harmful prompt to overwhelm the model's attention and reduce its ability to identify malicious content. The complexity of the visual input, rather than its specific content, is the key to successful exploitation.

Multimodal Large Language Models (MLLMs) are vulnerable to a universal adversarial attack where a single, optimized image can bypass safety alignment mechanisms across diverse textual queries. By employing gradient-based optimization on the input image pixels while propagating gradients through the vision encoder and language model, an attacker can craft a visual perturbation that coerces the model into a compliant state. When this adversarial image is present in the context, the model’s…

A novel "Flanking Attack" exploits the vulnerability of multimodal LLMs (e.g., Google Gemini) to bypass content moderation filters by embedding adversarial prompts within a sequence of benign prompts. The attack leverages the LLM's processing of both audio and text, obfuscating harmful requests through contextualization and layering, thereby yielding policy-violating responses.

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…

End-to-end Large Audio Language Models (LALMs) contain an audio-based jailbreak vulnerability allowing attackers to bypass safety alignment guardrails by manipulating audio-specific "hidden semantics." Unlike text-based attacks, this exploitation involves encoding harmful queries into audio and applying signal processing modifications—specifically changes to emphasis, speech speed, intonation, tone, background noise, celebrity accents, or emotional overlays (e.g., laughter, screaming). These…

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…

Multimodal Large Language Models (MLLMs) exhibit a vulnerability where shuffling the order of words in text prompts or patches in image prompts can bypass their safety mechanisms, despite the model still understanding the intent of the shuffled input. This "Shuffle Inconsistency" allows attackers to elicit harmful responses by submitting shuffled harmful prompts that would otherwise be blocked.

A hybrid multimodal jailbreaking attack, dubbed JMLLM, exploits vulnerabilities in 13 popular large language models (LLMs) across text, image, and speech modalities. The attack leverages alternating translation, word encryption, feature collapse in images, and harmful text injection to bypass safety mechanisms and elicit harmful responses. Success rates vary across LLMs and modalities, with some models exhibiting significantly higher vulnerability than others.