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pith:2026:ZASO2GJWIMG6YB7YOZKKBTFXS5
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DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense

Liling Zheng, Xiaoke Yang, Xuxing Lu, Ziyang You

A malicious PRNG injected through the software supply chain can force diffusion models to output any chosen image pixel-for-pixel without touching model weights.

arxiv:2605.13115 v1 · 2026-05-13 · cs.CR · cs.LG

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Claims

C1strongest claim

A malicious PRNG, injected via compromised packages, forces pixel-perfect reproduction of attacker-chosen content (SSIM = 1.00, N = 100 trials) on Stable Diffusion v1.4, v1.5, and SDXL -- without modifying model weights.

C2weakest assumption

The attack remains effective under stochastic sampling (eta > 0) and operates independently of the user's prompt while being inherently undetectable by existing model auditing mechanisms.

C3one line summary

Diffusion models are vulnerable to supply-chain PRNG hijacking that forces pixel-perfect attacker-chosen outputs, and QRNG eliminates the attack.

References

30 extracted · 30 resolved · 0 Pith anchors

[1] High-resolution image synthesis with latent diffusion models, 2022
[2] Photorealistic text-to-image diffusion models with deep language under- standing, 2022
[3] Guardt2i: Defending text-to-image models from adversarial prompts, 2024
[4] Badnets: Identifying vulnerabilities in the ma- chine learning model supply chain, 2019
[5] Backdoor learning: A survey, 2024
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First computed 2026-05-18T03:08:58.056219Z
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c824ed1936430dec07f87654a0ccb79756d986ac26d660662d651e29431db1c0

Aliases

arxiv: 2605.13115 · arxiv_version: 2605.13115v1 · doi: 10.48550/arxiv.2605.13115 · pith_short_12: ZASO2GJWIMG6 · pith_short_16: ZASO2GJWIMG6YB7Y · pith_short_8: ZASO2GJW
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Canonical record JSON
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