arXiv:2606.24388v1 Announce Type: new Abstract: We introduce a large-scale, open-source dataset of pre-generated adversarial attacks for vision-language models (VLMs). The dataset is designed to be diverse, representative, and practical, extending existing benchmarks by covering 10 high-level categories and 55 subcategories of harmful intents. Our primary goal is to make adversarial data accessible to the research community, given the computational cost and complexity of generating large numbers of attacks. The dataset comprises 47 524 adversarial samples, generated using state-of-the-art atta
The rapid deployment and increasing sophistication of Vision-Language Models necessitate robust testing for adversarial vulnerabilities, which this dataset addresses by providing pre-generated attacks.
A strategic reader should care because the accessibility of large-scale adversarial data is crucial for developing more secure and reliable AI systems, directly impacting trust and deployment safety.
The availability of PHANTOM simplifies and accelerates VLM security research, allowing broader community engagement in finding and mitigating adversarial attacks without the high computational cost of attack generation.
- · AI safety researchers
- · Vision-Language Model developers
- · Organizations deploying VLMs
- · Malicious actors targeting VLMs
Increased pace of research into adversarial robustness for Vision-Language Models.
Development of more robust and secure multimodal AI applications, reducing deployment risks.
Potential for an 'arms race' between attack generation and defense mechanisms, accelerating AI safety advancements.
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Read at arXiv cs.AI