PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models

2026-06-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

PHANTOM is a new large-scale, open-source dataset designed for evaluating multimodal adversarial attacks on vision-language models (VLMs). This dataset comprises 47,524 pre-generated adversarial samples, created using advanced attack strategies from recent literature. It significantly extends existing benchmarks by covering 10 high-level categories and 55 subcategories of harmful intents, consolidating 7,826 intents from multiple established sources and introducing an additional category for broader coverage. The primary goal of PHANTOM is to make adversarial data readily accessible to the research community, addressing the high computational cost and complexity typically involved in generating such attacks. This resource provides realistic evaluation materials to study VLM robustness and alignment, enabling researchers and practitioners to systematically assess VLM safety, fine-tune attack-generation models, and develop or stress-test defensive guardrails under diverse adversarial conditions.

Key takeaway

For AI Security Engineers or Machine Learning Engineers focused on VLM safety, PHANTOM offers a critical resource. You can now systematically evaluate your vision-language models against 47,524 pre-generated adversarial attacks without incurring high computational costs. Utilize this dataset to stress-test defensive guardrails, fine-tune attack-generation models, and ensure your VLMs are robust against diverse harmful intents, fostering more comprehensive safety assessments.

Key insights

The article introduces a large-scale, pre-generated dataset, PHANTOM, to democratize VLM adversarial attack research and improve safety evaluations.

Principles

• Adversarial data generation is computationally costly.
• Diverse attack categories improve VLM robustness evaluation.
• Open-source datasets foster reproducible safety research.

Method

The dataset generation method involves consolidating and extending prior benchmarks, covering 10 high-level categories and 55 subcategories, and generating 47,524 samples using recent attack strategies.

In practice

• Evaluate VLM robustness and safety.
• Fine-tune attack-generation models.
• Stress-test defensive guardrails.

Topics

• Vision-Language Models
• Adversarial Attacks
• PHANTOM Dataset
• Model Robustness
• AI Safety
• Multimodal AI

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.