ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
arXiv preprint arXiv:2412.18295 , year=
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UNVERDICTED 3representative citing papers
DP-SynRAG generates reusable differentially private synthetic RAG databases via LLM private prediction to prevent privacy loss accumulation from repeated noise.
ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.
citing papers explorer
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ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying
ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
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Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
DP-SynRAG generates reusable differentially private synthetic RAG databases via LLM private prediction to prevent privacy loss accumulation from repeated noise.
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ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation
ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.