This blog post describes a research finding regarding the vulnerabilities in proprietary LLM APIs that allow the extraction of reasoning traces and sensitive information. It details how encrypted reasoning blocks can be exploited to bypass security measures and highlights the potential for large-scale data extraction, including private information and invisible prompt injections. The author suggests mitigations to enhance security against these vulnerabilities.