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arXiv · 2605.08313

Seed Hijacking of LLM Sampling and Quantum Random Number Defense

Ziyang You·Xiaoke Yang·Zhanling Fan·Feng Guo·Xiaogen Zhou·Xuxing Lu
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Abstract

Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chain attack surface overlooked by existing defenses. We present SeedHijack, a backdoor attack that manipulates PRNG outputs to force attacker-specified token selection without altering model logits. In a 540-trial benchmark on GPT-2 (124M), the attack achieves 99.6% exact token injection across 9 sampling configurations; it reaches 100% success on four aligned models (1.5B-7B, RLHF/SFT/reasoning distillation) and bypasses all alignment methods tested in this work. We further propose a defense based on a hardware quantum random number generator (QRNG), which neutralizes the attack in our evaluated threat model with negligible median overhead (+0.6% latency, +7.7 MB memory). Our work identifies a critical sampling-layer vulnerability and provides a practical, deployable QRNG-based defense.

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Ziyang You, Xiaoke Yang, Zhanling Fan, Feng Guo, Xiaogen Zhou, Xuxing Lu. 2026-05-08. Seed Hijacking of LLM Sampling and Quantum Random Number Defense. https://arxiv.org/abs/2605.08313

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