Seed Hijacking of LLM Sampling and Quantum Random Number Defense
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 ...
| Authors | Ziyang You, Xiaoke Yang, Zhanling Fan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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