aiqbench analysis
This research identifies critical security vulnerabilities in discrete graph diffusion models, specifically demonstrating how backdoor attacks can manipulate both training and generation phases while maintaining stealth and fundamental graph properties. The findings are particularly relevant for safety-critical domains like drug discovery, where compromised models could generate malicious molecular structures that evade standard defenses. By formalizing the threat model and providing empirical validation, this work establishes a necessary baseline for securing next-generation generative AI systems against adversarial manipulation.
Backdoor Attacks on Discrete Graph Diffusion Models
arXiv:2503.06340v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete graph diffusion models (DGDMs) have extended this success to graph generation, achieving state-of-the-art performance. However, deploying DGDMs in safety-critical applications, such as drug discovery, poses significant risks without a thorough understanding of their security vulnerabilities. In this work, we conduct the first study of backdoor attacks on DGDMs, a potent threat that manipulates both the training and generation phases of graph diffusion. We begin by formalizing the threat model and then design a backdoor attack that enables the compromised model to: 1) generate high-quality, benign graphs when the backdoor is not activated, 2) produce effective, stealthy, and persistent backdoored graphs when triggered, and 3) preserve fundamental graph properties (permutation equivariance and exchangeability) even under attack. We validate 1) and 2) empirically, both with and without backdoor defenses, and support 3) through theoretical analysis inspired by prior work.
Abstract:Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete graph diffusion models (DGDMs) have extended this success to graph generation, achieving state-of-the-art performance. However, deploying DGDMs in safety-critical applications, such as drug discovery, poses significant risks without a thorough understanding of their security vulnerabilities. In this work, we conduct the first study of backdoor attacks on DGDMs, a potent threat that manipulates both the training and generation phases of graph diffusion. We begin by formalizing the threat model and then design a backdoor attack that enables the compromised model to: 1) generate high-quality, benign graphs when the backdoor is not activated, 2) produce effective, stealthy, and persistent backdoored graphs when triggered, and 3) preserve fundamental graph properties (permutation equivariance and exchangeability) even under attack. We validate 1) and 2) empirically, both with and without backdoor defenses, and support 3) through theoretical analysis inspired by prior work.
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2503.06340 [cs.CR]
(or arXiv:2503.06340v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2503.06340 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 251 words
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