Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing
LLM inference and serving systems have become security-critical infrastructure; however, many of their most concerning failures arise from the serving layer rather than from model behavior alone. Modern inference engines combine KV cache, batching, prefix sharing, speculative decoding, adapters, and multi-tenant scheduling, creating shared-state behavior that only emerges under realistic concurrent workloads and is missed by standard model, safety, and API tests. We present GRIEF, a greybox fuzzer for LLM inference engines that treats timed multi-request traces as first-class inputs, uses lightweight oracles to detect crashes, hangs, performance pathologies, and silent output corruption, ...
| Authors | Yunze Zhao, Yibo Zhao, Yuchen Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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