# vlm
12 articlestagged with “vlm”
Multimodal Security Practice Exam
Practice exam covering image injection, audio attacks, cross-modal transfer, and document parsing exploitation.
Multimodal Security
Security assessment of multimodal AI systems processing images, audio, video, and cross-modal inputs, covering vision-language models, speech systems, video analysis, and cross-modal attack techniques.
Benchmarking Multimodal Model Safety
Designing and implementing safety benchmarks for multimodal AI models that process images, audio, and video alongside text, covering cross-modal attack evaluation, consistency testing, and safety score aggregation.
Attacks on Vision-Language Models
Comprehensive techniques for attacking vision-language models including GPT-4V, Claude vision, and Gemini, covering adversarial images, typographic exploits, and multimodal jailbreaks.
Adversarial Image Examples for VLMs
Pixel-level perturbations that change VLM behavior, including PGD attacks on vision encoders, transferable adversarial images, and patch attacks.
VLM Architecture & Vision-Language Alignment
Deep dive into VLM architectures including CLIP, SigLIP, and vision transformers. How image patches become tokens, alignment training, and where misalignment creates exploitable gaps.
Image-Based Prompt Injection
Techniques for embedding text instructions in images to manipulate VLMs, including steganographic injection, visible text attacks, and QR code exploitation.
Vision-Language Model Attacks
Comprehensive overview of the VLM attack surface, how vision encoders connect to language models, and why multimodal systems create new injection vectors.
Lab: Crafting Image-Based Injections
Hands-on lab for creating image-based prompt injections, testing against VLMs, and measuring success rates across different injection techniques.
OCR & Typographic Attacks
Exploiting OCR capabilities in VLMs through typographic attacks, font manipulation, adversarial text overlays, and text rendering exploits.
Typographic Adversarial Attacks
How text rendered in images influences VLM behavior: adversarial typography, font-based prompt injection, visual instruction hijacking, and defenses against typographic manipulation.
VLM-Specific Jailbreaking
Jailbreaking techniques that exploit the vision modality, including image-text inconsistency attacks, visual safety bypass, and cross-modal jailbreaking strategies.