Learning Normal Diffusion Dynamics for Backdoor Defense in Text-to-Image Models

Summary

This paper proposes Normal Diffusion Dynamics Learning (NDDL), a backdoor defense for text-to-image diffusion models. NDDL trains a timestep-conditioned dynamics model on benign samples only, using cross-attention, latent and noise space trajectories. At inference, deviations between observed and predicted transitions flag backdoors and enable trigger localization by substituting low-semantic words; the authors report effectiveness and generalizability across diverse attacks.

Related items

• LowAnthropic Cuts Live Internet Access for Internal AI Tests After Claude Exploits Injection FlawsSimilar attack · The Hacker News
• CriticalCVE-2026-108263: Astron Agent code-node execution as root through workflow run endpointsSimilar attack · NVD/CVE Database
• MediumHackers abuse Google Ads, Bing redirects to push Claude ClickFix attacksSimilar attack · BleepingComputer
• CriticalHermes Agent - PKCE Session Takeover via Redirect-URI Parser ConfusionSimilar attack · Tenable Research Advisories
• LowSocial Engineering AI Agents: The New BEC for 2026Similar attack · Dark Reading