Attack Type

Injection Research

Attacks that inject malicious content into model inputs

6 matching entries out of 337 in this category

Injection research results

6 entries

Progressive Attention LVLM Attack

Published Feb 4, 2026Analyzed Feb 22, 2026Source: arXiv

Large Vision-Language Models (LVLMs) are vulnerable to a Stage-wise Attention-Guided Attack (SAGA) that allows for the generation of highly transferable, imperceptible adversarial examples. The vulnerability stems from a positive correlation between regional cross-modal attention scores and adversarial loss sensitivity in LVLMs. An attacker can exploit this by extracting an attention map from a surrogate open-source model (e.g., Qwen3-VL) to identify high-attention "hotspots." SAGA utilizes a…

Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models

Evaluated models: Gemini 2.5 Flash, Gemini 3 Pro Preview, GPT-4.1+7 more

Gemini 2.5 Flash
Gemini 3 Pro Preview
GPT-4.1
GPT-5 Mini
Grok 4 Fast
LLaVA 1.5 7B
Llama 4 Maverick
Gemma 3 4B IT
Qwen3-VL 30B-A3B Instruct
Qwen3-VL 235B-A22B Instruct

Single Word Video Corruption

Published Dec 30, 2025Analyzed Jan 14, 2026Source: arXiv

Text-to-Video (T2V) diffusion models are vulnerable to black-box adversarial prompt attacks that degrade output quality regarding semantic fidelity and temporal dynamics. This vulnerability is exploited via the T2VAttack framework, which utilizes two primary vector strategies: T2VAttack-S (Substitution) and T2VAttack-I (Insertion). T2VAttack-S leverages a greedy search to identify key semantic tokens and replaces them with high-similarity synonyms defined in lexical databases (e.g., WordNet)…

T2VAttack: Adversarial Attack on Text-to-Video Diffusion Models

Evaluated models: Not reported

Embodied Cross-Modal Misalignment

Published Nov 20, 2025Analyzed Jan 14, 2026Source: arXiv

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…

When alignment fails: Multimodal adversarial attacks on vision-language-action models

Evaluated models: Not reported

Latent Space Discontinuity Exploitation

Published Nov 1, 2025Analyzed Dec 5, 2025Source: arXiv

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attacks

Evaluated models: Not reported

Single Image Universal Jailbreak

Published Feb 11, 2025Analyzed Dec 9, 2025Source: arXiv

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…

Universal Adversarial Attack on Multimodal Aligned LLMs

Evaluated models: LLaVA 1.5 7B, Llama 3.2 11B Vision Instruct, Phi-3.5 Vision Instruct+1 more

LLaVA 1.5 7B
Llama 3.2 11B Vision Instruct
Phi-3.5 Vision Instruct
Qwen2-VL 2B Instruct

AD Black-Box Cascading Disruption

Published Jan 23, 2025Analyzed Dec 9, 2025Source: arXiv

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…

Black-box adversarial attack on vision language models for autonomous driving

Evaluated models: GPT-4, GPT-4o, InstructBLIP

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