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Inadvertent Context Leakage in Language Models

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TL;DR - This paper shows that language models can inadvertently encode sensitive in-context information into benign outputs, enabling black-box attackers to reconstruct secrets even when direct extraction is refused. The findings expose a serious privacy risk for agents handling personal data.

  • Across eight proprietary models, attacks recovered 2-digit secrets with near-perfect accuracy and 4-digit secrets with 82% exact match from ordinary, non-adversarial responses.
  • More capable models leaked more information, suggesting the effect may arise from stronger instruction-following rather than a narrowly patchable flaw.
  • A trained classifier inferred health and financial predicates about user memories from routine natural-language outputs.
  • An RL-trained adversary extracted complete Social Security Numbers from a production-style agent through seemingly innocuous text.

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Inadvertent Context Leakage in Language Models

arXiv cs.LG Jaiden Fairoze, Neal Mangaokar, Kamalika Chaudhuri, Sanjam Garg, Saeed Mahloujifar 2026-08-20 arXiv:2608.19857
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-17 14:30:35.126176 UTC

TL;DR - This paper shows that language models can inadvertently encode sensitive in-context information into benign outputs, enabling black-box attackers to reconstruct secrets even when direct extraction is refused. The findings expose a serious privacy risk for agents handling personal data.

  • Across eight proprietary models, attacks recovered 2-digit secrets with near-perfect accuracy and 4-digit secrets with 82% exact match from ordinary, non-adversarial responses.
  • More capable models leaked more information, suggesting the effect may arise from stronger instruction-following rather than a narrowly patchable flaw.
  • A trained classifier inferred health and financial predicates about user memories from routine natural-language outputs.
  • An RL-trained adversary extracted complete Social Security Numbers from a production-style agent through seemingly innocuous text.
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