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cs.CR2026
OptiLeak: Efficient Prompt Reconstruction via Reinforcement Learning in Multi-tenant LLM Services
Longxiang Wang, Xiang Zheng, Xuhao Zhang +3
Multi-tenant LLM serving frameworks widely adopt shared Key-Value caches to enhance efficiency. However, this creates side-channel vulnerabilities enabling prompt leakage attacks.…
cs.CR2026
Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model
Tianyi Wang, Huawei Fan, Yuanchao Shu +2
LLM inference is inherently expensive, even a modest slowdown can translate into substantial operating costs and severe availability risks. Recently, a growing body of research kno…
cs.CR2024
HORAM: A High-Performance Hierarchical Doubly Oblivious RAM
Leqian Zheng, Zheng Zhang, Wentao Dong +3
The combination of Oblivious RAM (ORAM) with Trusted Execution Environments (TEE) has found numerous real-world applications due to their complementary nature. TEEs alleviate the p…