Architecture Components

Multimodal Research

Security research involving multimodal AI systems

20 matching entries out of 176 in this category

Multimodal research results

20 entries

Query-Agnostic Poisoning of Medical Multimodal RAG

Published May 11, 2026Analyzed Jul 20, 2026Source: arXiv

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation

Evaluated models: GPT-4o, GPT-5 Chat, Gemini 2.5 Flash+5 more

GPT-4o
GPT-5 Chat
Gemini 2.5 Flash
Claude Haiku 4.5
LLaVA Med
CLIP ViT-Large-Patch14-336
BGE-VL-base
SigLIP-SO400M-Patch14-384

VLM Visual-Textual Misalignment

Published Apr 1, 2026Analyzed Apr 11, 2026Source: arXiv

Vision-Language Models (VLMs) are vulnerable to pixel-level adversarial image perturbations. An attacker can inject $\ell_p$-bounded, human-imperceptible noise into an input image to manipulate the model's multi-modal embedding space. This reliably causes the VLM to generate incorrect textual responses, hallucinate non-existent objects, or misclassify subjects, effectively decoupling the model's reasoning from the actual visual evidence. The vulnerability is exploitable via both white-box…

PDA: Text-Augmented Defense Framework for Robust Vision-Language Models against Adversarial Image Attacks

Evaluated models: LLaVA 1.5 7B, LLaVA 1.5 13B, DeepSeek VL 1.3B+2 more

LLaVA 1.5 7B
LLaVA 1.5 13B
DeepSeek VL 1.3B
InternVL3 2B
Ovis2 4B

X-Shaped Sparse VLM Attack

Published Mar 30, 2026Analyzed Apr 11, 2026Source: arXiv

A vulnerability in Vision-Language Models (VLMs) relying on shared visual-textual representation spaces allows attackers to induce transferable cross-task semantic failures using an X-shaped Sparse Pixel Attack (XSPA). Attackers craft imperceptible adversarial perturbations restricted to a fixed geometric prior—two intersecting diagonal lines comprising approximately 1.76% of the image pixels. By jointly optimizing a classification objective with cross-task semantic guidance (target-semantic…

XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs

Evaluated models: InstructBLIP

VLM E-commerce Attack Surface

Published Mar 17, 2026Analyzed Apr 11, 2026Source: arXiv

LLaVA-v1.5-7B, when deployed as a vision-language autonomous agent, is highly vulnerable to adversarial image perturbations. An attacker can inject imperceptibly modified images into a web environment (such as an e-commerce storefront). When the VLM agent captures a screenshot containing the perturbed image, the visual noise forces the model to misclassify the scene and output incorrect, structured JSON actions. This allows an attacker to hijack the agent's task execution, bypassing the user's…

Adversarial attacks against Modern Vision-Language Models

Evaluated models: Qwen 2.5 VL 7B Instruct, LLaVA 1.5 7B

Video-LLM Token Flood

Published Mar 2, 2026Analyzed Mar 9, 2026Source: arXiv

Video-based Large Language Models (Video-LLMs) are vulnerable to a universal Energy-Latency Attack (ELA) that triggers a Denial-of-Service (DoS) via spatially concentrated adversarial patches. Because video architectures rely on temporal subsampling and pooling which act as a low-pass filter against full-frame diffuse noise, an attacker can bypass this compression by anchoring cross-modal attention to a dense, localized visual anomaly. By injecting a fixed, content-agnostic patch into the…

VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models

Evaluated models: LLaVA-NeXT-Video 7B, Qwen3-VL 4B Instruct, Video-LLaVA 7B

Discrete Token Subversion

Published Feb 20, 2026Analyzed Mar 9, 2026Source: arXiv

Discrete image tokenizers are vulnerable to unsupervised embedding-space adversarial attacks. Attackers can apply $\ell_p$-bounded perturbations to an input image to maximize the $\ell_2$ distance of the pre-quantization continuous embeddings produced by the tokenizer's vision encoder. This forces the vector quantizer to cross discrete cell boundaries and assign incorrect codebook vectors, fundamentally altering the resulting token sequence. Because the attack targets the pre-quantization…

On the Adversarial Robustness of Discrete Image Tokenizers

Evaluated models: Llama 2 7B

Reward Hacking Via Adversarial Imitation

Published Feb 12, 2026Analyzed Feb 22, 2026Source: arXiv

A vulnerability exists in the post-training alignment of Flow Matching models (specifically FLUX.1-dev) when utilizing Visual Foundation Models (VFM) (e.g., DINOv3b) as discriminators or when employing standalone Reward Gradient optimization (e.g., HPSv3). These feedback mechanisms lack sufficient capacity or structural guidance to constrain the generative policy, making the discriminator's gradients susceptible to "reward hacking." Consequently, the generative policy over-optimizes for the…

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation

Evaluated models: Not reported

Semantic Ensembles Fool CLIP

Published Feb 12, 2026Analyzed Mar 9, 2026Source: arXiv

Contrastive Language-Image Pre-training (CLIP) models are vulnerable to semantic-ensemble adversarial attacks. Current adversarial fine-tuning defenses for CLIP rely on minimizing the cosine similarity between an image and a single hand-crafted template (e.g., "A photo of a {label}"). This creates a vulnerability where adversarial examples (AEs) overfit to specific phrasings rather than the core class semantics. Attackers can bypass these defenses by generating semantic-aware adversarial…

Semantic-aware Adversarial Fine-tuning for CLIP

Evaluated models: CLIP ViT-B/32

Visual Compression Mismatch Attack

Published Jan 29, 2026Analyzed Feb 22, 2026Source: arXiv

A vulnerability exists in Large Vision-Language Models (LVLMs) utilizing visual token compression mechanisms (e.g., VisionZip, VisPruner) to reduce inference latency. The vulnerability stems from an optimization-inference mismatch where standard adversarial defenses assume full-token processing, while the deployed model utilizes a subset of tokens selected via importance metrics (typically attention scores).

On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression

Evaluated models: LLaVA 1.5 7B

Sound Induces Trimodal Collapse

Published Jan 20, 2026Analyzed Feb 22, 2026Source: arXiv

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models

Evaluated models: VideoLLAMA2, Qwen 2 7B Instruct, Whisper Large-v2+1 more

VideoLLAMA2
Qwen 2 7B Instruct
Whisper Large-v2
PANNs

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