Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models
arXiv:2606.26566v1 Announce Type: cross Abstract: Adversarial evaluation of AI systems has matured along four largely disconnected tracks: diffusion-based attacks on text and large language models (LLMs), diffusion-based attacks on image classifiers, jailbreak pipelines against vision-language models, and diffusion-based input purification defenses. Each has developed its own vocabulary, threat models, and benchmarks, with denoising diffusion models emerging as a shared generative mechanism whose recipes are now actively ported between communities. This survey performs an information-fusion ex
The rapid advancement and integration of generative AI models across modalities necessitate a unified understanding of their vulnerabilities and defenses, spurred by increasing real-world deployments.
A consolidated view on adversarial attacks and defenses in AI across text, vision, and vision-language models is crucial for building robust, secure, and trustworthy AI systems, which underpins the broader AI ecosystem.
The focus is shifting from siloed research in adversarial AI for specific modalities to a more integrated, cross-modal approach, leveraging shared mechanisms like diffusion models, enabling more effective countermeasures.
- · AI Security Researchers
- · Model Developers
- · AI-powered Product Companies
- · Malicious AI Actors (in the short-term)
- · Companies with Insecure AI Systems
Increased understanding and development of unified adversarial AI techniques and defenses.
Improved security and robustness of AI models leads to greater trust and broader adoption of AI in sensitive applications.
The arms race between AI attackers and defenders accelerates, driving continuous innovation in AI safety and security research.
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