The LLM-Attacker: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
A unified red-team framework standardizes privacy evaluation of split and distributed LLM systems by normalizing attacker knowledge, enabling apples-to-apples comparison of text-reconstruction risks.
Summary written by editorial AI · Source link below
arXiv:2610.22722v1 Announce Type: new Abstract: Split language models keep input text on a client but send intermediate representations to a server. Attacks can reconstruct text from these messages. Comparing privacy evaluations is difficult when attackers see different messages, know different model weights or use different datasets and recovery measures. We present LLM-Attacker, a framework that records these choices and lets researchers check, compare and extend an evaluation. Compatible att
Editorial Analysis
Enterprises exploring split-inference LLM deployments for data residency need standardized tools to quantify intermediate-representation leakage risk.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
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