New framework unifies LLM privacy evaluation, reveals gradient attack effectiveness

Researchers have developed LLM-Attacker, a new framework designed to standardize and unify the evaluation of privacy in split and distributed large language models. This framework allows for the comparison and extension of privacy evaluations by recording choices related to attacker messages, model weights, datasets, and recovery measures. In a controlled experiment using GPT-2, the framework demonstrated that incorporating training gradients significantly improved token recovery from 94.20% to 97.38% and exact-document recovery from 13.71% to 37.77%. The system also distinguishes between implementation checks and measured attack effectiveness, emphasizing the need for clear reporting of attacker, dataset, and scoring rules. AI

IMPACT Standardizes LLM privacy evaluation, potentially leading to more robust defenses against data leakage in distributed systems.

RANK_REASON The cluster contains an academic paper detailing a new framework for evaluating LLM privacy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• GPT-2
• LLM-Attacker

• paper
• safety

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