MACTDiffusion: Multi-Agent Chaotic Trigger-Controlled Backdoor Attack Framework for Diffusion Models

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Abstract Diffusion models (DMs) have emerged as a dominant model for high-fidelity generative tasks across vision, text, and audio domains. However, their increasing deployment raises serious concerns about backdoor vulnerabilities. Existing attacks primarily rely on static and perceptible triggers, making them susceptible to detection via state-of-the-art inversion-based defenses. In this paper, we propose MACTDiffusion, a novel Multi-Agent Backdoor Attack framework for diffusion models empowered by Chaotic Trigger Controller (CTC). Unlike conventional single-trigger attacks, our method employs multiple imperceptible sub-triggers that are independently ineffective but collectively activate the backdoor when spatiotemporally aligned. To further enhance stealth and unpredictability, we leverage chaotic maps to randomly control the trigger injection time, spatial location, and active sub-trigger combinations during both training and inference. This makes the attack behave unpredictably, avoids leaving detectable patterns, and significantly increases the difficulty for defenders to analyze or reconstruct the trigger mechanism. Furthermore, the proposed approach is compared with leading defenses such as Elijah and TERD. This study reveals a novel type of stealthy backdoor threat that has not been explored before, highlighting the urgent need for stronger and more reliable security measures for generative models.
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MACTDiffusion: Multi-Agent Chaotic Trigger-Controlled Backdoor Attack Framework for Diffusion Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MACTDiffusion: Multi-Agent Chaotic Trigger-Controlled Backdoor Attack Framework for Diffusion Models Lalitha Bhavani Bikkina, Venkata Rao Maddumala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7585237/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffusion models (DMs) have emerged as a dominant model for high-fidelity generative tasks across vision, text, and audio domains. However, their increasing deployment raises serious concerns about backdoor vulnerabilities. Existing attacks primarily rely on static and perceptible triggers, making them susceptible to detection via state-of-the-art inversion-based defenses. In this paper, we propose MACTDiffusion, a novel Multi-Agent Backdoor Attack framework for diffusion models empowered by Chaotic Trigger Controller (CTC). Unlike conventional single-trigger attacks, our method employs multiple imperceptible sub-triggers that are independently ineffective but collectively activate the backdoor when spatiotemporally aligned. To further enhance stealth and unpredictability, we leverage chaotic maps to randomly control the trigger injection time, spatial location, and active sub-trigger combinations during both training and inference. This makes the attack behave unpredictably, avoids leaving detectable patterns, and significantly increases the difficulty for defenders to analyze or reconstruct the trigger mechanism. Furthermore, the proposed approach is compared with leading defenses such as Elijah and TERD. This study reveals a novel type of stealthy backdoor threat that has not been explored before, highlighting the urgent need for stronger and more reliable security measures for generative models. Diffusion Model Backdoor Attack Denoising Diffusion Probabilistic Model Multi-Agent Backdoor Attack Chaotic Trigger Controller Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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