Architecture Components

Multimodal Research

Security research involving multimodal AI systems

176 matching entries

Multimodal research results

176 entries

Selective refusal gaps in visual question answering

Published Sep 4, 2026Analyzed Sep 9, 2026Source: arXiv

KoNA measures whether vision-language models answer valid image questions while refusing unsafe components or correcting unsupported premises. Its 9,300 question-answer pairs include mixed and fully answerable controls.

Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models

Evaluated models: InternVL3 2B Instruct, InternVL3-78B-Instruct, Qwen 2.5 VL 3B Instruct+5 more

InternVL3 2B Instruct
InternVL3-78B-Instruct
Qwen 2.5 VL 3B Instruct
Qwen 2.5 VL 72B Instruct
GPT-5
Gemini 2.5 Flash
InternVL3-2B-KoNA
Qwen2.5-VL-3B-KoNA

Visual prompt injection crosses document trust boundaries

Published Sep 3, 2026Analyzed Sep 9, 2026Source: arXiv

Untrusted document images can redirect vision-language agents across instruction and tool-authorization boundaries. Repeat-After-Me evaluates six victim models on constructed document tasks.

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Evaluated models: claude-opus-4-7, GPT-5.5, Gemini 3.1 Pro+3 more

claude-opus-4-7
GPT-5.5
Gemini 3.1 Pro
Qwen3.6-27B
Qwen3-VL 32B Instruct
InternVL3.5 38B Instruct

PII leakage in document-image redaction benchmarks

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

Document-image PII detection can miss identifiers despite improving average localization scores. LeakageBench evaluates 500 pages with 11,954 annotations.

LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images

Evaluated models: GLiNER-base, GLiNER-multi-PII, NVIDIA GLiNER PII+5 more

GLiNER-base
GLiNER-multi-PII
NVIDIA GLiNER PII
GLiNER2
Qwen3-VL-32B
InternVL3 38B
GPT-5.4
GPT-5.5

Information-Overloading Jailbreaks in Vision-Language Models

Published Jul 3, 2026Analyzed Jul 20, 2026Source: arXiv

The paper describes a reproducible black-box multimodal jailbreak evaluation, INFER/INFER+, in which dense image typography, nested cross-modal references, recursive visual layouts, and entropy-guided search increase processing complexity and weaken refusal behavior in large vision-language models. The authors report average ASRs of 88.6% on open-source models and 84.0% on commercial models; these are paper-reported measurements, not independently verified facts. For safe defensive…

Overloading Large Vision-Language Models for Jailbreaking

Evaluated models: Qwen3-VL 8B, Qwen2-VL-7B, InternVL3.5-8B+5 more

Qwen3-VL 8B
Qwen2-VL-7B
InternVL3.5-8B
InternVL 2 8B
Llama 3.2 11B Vision
GPT-4.1 Mini
Gemini 2.5 Flash
Gemini 2.5 Flash-Lite

Multilingual Flowchart Jailbreaks in Vision-Language Models

Published Jun 5, 2026Analyzed Jul 20, 2026Source: arXiv

MLingualFC is a reproducible black-box safety evaluation showing that harmful instructions rendered as multilingual flowchart images can bypass vision-language model safeguards more often than equivalent text-only inputs. The paper evaluates horizontal, vertical, and tortuous layouts across English, Hindi, Punjabi, Spanish, Romanian, and German. Reported results vary substantially by language, script, layout, and model; these are paper-reported measurements, not independently verified…

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models

Evaluated models: Qwen 2.5 VL 3B Instruct, Gemma-4-E4B-it, Pangea-7B

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

Visual-Modality Jailbreaks Bypass VLM Safety Alignment

Published May 1, 2026Analyzed Jul 20, 2026Source: arXiv

The paper reports a reproducible black-box evaluation showing that vision-language models can recover prohibited intent encoded or implied through ostensibly benign visual inputs. Four tested families—visual ciphers, object replacement, text replacement, and analogy riddles—expose a cross-modality alignment gap: safeguards effective for explicit text may not reliably apply after harmful semantics are reconstructed from images. These are paper-reported results, not independently verified…

Jailbreaking Vision-Language Models Through the Visual Modality

Evaluated models: GPT-5.2, Claude Haiku 4.5, Gemini 3 Flash+3 more

GPT-5.2
Claude Haiku 4.5
Gemini 3 Flash
Gemini 3.1 Pro
Qwen3-VL-235B
Qwen3-VL-32B

Diverse VLA Linguistic Fragility

Published Apr 7, 2026Analyzed Apr 10, 2026Source: arXiv

Vision-Language-Action (VLA) models suffer from a severe linguistic fragility vulnerability where semantically equivalent but structurally complex adversarial instructions cause catastrophic failures in visual grounding and geometric reasoning. Attackers can reliably induce physical execution failures in robotic manipulation tasks by applying semantic-preserving linguistic variations, such as synonymous rephrasing, syntactic restructuring, or the addition of fine-grained compositional…

Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming

Evaluated models: Pi-Zero, OpenVLA 7B, 3D-Diffuser Actor

Observation Poisons Agent Memory

Published Apr 3, 2026Analyzed Apr 10, 2026Source: arXiv

Memory-augmented LLM web agents utilizing raw trajectory memory are vulnerable to Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP). Attackers can embed malicious instructions within user-generated web content (e.g., product pages, forum posts). When the agent processes this content during a routine task, the instructions are passively ingested into its raw trajectory memory. During subsequent, entirely separate tasks on different websites, semantic retrieval mechanisms pull…

Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents

Evaluated models: GPT-5 Mini, GPT-5.2, GPT-oss 120B+3 more

GPT-5 Mini
GPT-5.2
GPT-oss 120B
Qwen 2.5 VL 72B
Qwen3-VL-32B
Qwen3.5-122B-A10B

Physical Infrared Semantic Disruption

Published Apr 3, 2026Analyzed Apr 11, 2026Source: arXiv

A vulnerability in Infrared Vision-Language Models (IR-VLMs) allows attackers to systematically degrade open-ended semantic understanding—compromising classification, captioning, and Visual Question Answering (VQA)—via a physically deployable Universal Curved-Grid Patch (UCGP). Instead of manipulating explicit text labels, the attack disrupts the clean-category manifold in the model's visual representation space by maximizing orthogonal deviation energy from the principal subspace and forcing…

Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models

Evaluated models: InstructBLIP

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