Stealing Reasoning Traces from Proprietary LLM APIs

2026-08-10

AI Summary

This work systematically uncovers a critical compatibility vulnerability in encrypted reasoning chains employed by large language model (LLM) providers to safeguard intellectual property. Despite encryption, reasoning blocks exhibit cross-session, cross-user, and cross-model interoperability flaws. The authors propose a novel decryption-based jailbreaking technique that leverages reverse engineering and cross-model reasoning block injection: by exploiting weaker models to decrypt the encrypted inference traces of stronger ones, the method reconstructs full reasoning processes without direct attacks. Empirical evaluation demonstrates successful extraction of reasoning chains from models by Anthropic, OpenAI, and Google, decrypting 315,320 publicly logged blocks, recovering 367 personally identifiable information (PII) instances and 182 credential sets, and enabling stealthy prompt injection that effectively bypasses existing anti-distillation and security mechanisms.

Abstract

Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider's ecosystem. We exploit this compatibility to develop a scalable decryption jailbreak. By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly. This vulnerability enables four distinct attack vectors. First, it circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model's reasoning, as we demonstrate across Anthropic, OpenAI, and Google. Second, it allows for large-scale private data extraction. Developers frequently share session logs publicly, unaware of contents of the encrypted blocks. By decoding 315,320 reasoning blocks scraped from public repositories, we recovered 367 Personally Identifiable Information (PII) artifacts and 182 credentials. Third, it inadvertently reveals hazardous information hidden within the reasoning process, even in cases where the model's final, visible output safely rejects a malicious request. Fourth, attackers can leverage this flaw to execute invisible prompt injections, embedding malicious payloads entirely within encrypted blocks to poison public agentic rollouts. Following responsible disclosure, we propose concrete cryptographic and system-level mitigations to secure client-side reasoning.

Problem

• reasoning trace
• encrypted blocks
• model stealing
• privacy leakage
• prompt injection

Innovation

• reasoning trace extraction
• decryption jailbreak
• encrypted prompt injection
• client-side reasoning vulnerability
• model distillation bypass