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Semantic-level Backdoor Attack against Text-to-Image Diffusion Models

arXiv – CS AI|Tianxin Chen, Wenbo Jiang, Hongqiao Chen, Zhirun Zheng, Cheng Huang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed SemBD, a new semantic-level backdoor attack against text-to-image diffusion models that achieves 100% success rate while evading current defenses. The attack uses continuous semantic regions as triggers rather than fixed textual patterns, making it significantly harder to detect and defend against.

Key Takeaways

→SemBD represents a major advancement in AI security threats, using semantic-level triggers instead of traditional textual patterns.
→The attack achieves 100% success rate and demonstrates strong robustness against state-of-the-art input-level defenses.
→Semantic regularization prevents unintended activation while multi-entity targets avoid detection patterns.
→The vulnerability affects widely-used text-to-image diffusion models, highlighting critical security gaps.
→Current enumeration-based input defenses and attention-consistency detection methods are insufficient against this attack.

#ai-security #backdoor-attacks #diffusion-models #text-to-image #vulnerability #ai-safety #semantic-attacks #machine-learning

Read Original →via arXiv – CS AI

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