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Stealing Reasoning Traces from Proprietary LLM APIs

86 upvotes · 10 AUG 2026 · Alexander Panfilov, David Schmotz, Ilia Shumailov et al.

This paper reveals a vulnerability in how large language model providers store and return their models' step-by-step reasoning, allowing attackers to extract sensitive information and potentially inject malicious code. Practitioners should care because this vulnerability can be exploited to steal proprietary models' reasoning and private data, as well as inject malicious payloads into public models.

large language modelsencryptionvulnerability exploitationdata extractionmodel security