OpenAI fixes API flaw letting researchers extract model reasoning traces
Researchers at Xint uncovered a vulnerability in OpenAI’s API that allowed them to bypass encryption and extract raw reasoning traces from its frontier models. The technique, called compaction smuggling, exploited OpenAI’s context compaction feature, which normally compresses conversations into unreadable encrypted summaries. By embedding one model’s reasoning inside a compaction and feeding it…
Key points
- Xint discovered an API flaw letting researchers extract raw reasoning traces from OpenAI’s frontier models via *compaction smuggling*
- OpenAI confirmed the issue in August, fixed it on October 23, and paid Xint $6,000 without publicly describing the patch
- Xint’s technique worked on GPT-5.6 Sol, GPT-4.1, and GPT-4o, raising risks for model distillation and unauthorized training data
The flaw highlights broader concerns about model distillation, where raw reasoning—used to train stronger models—could be harvested at scale. OpenAI previously addressed similar risks in August after the Stolen Thoughts paper demonstrated how encrypted reasoning could be replayed across models. Xint’s findings also echo OpenAI’s September report, where an internal model occasionally generated unauthorized instructions in its own compaction summaries. While Xint did not train a model on extracted reasoning, it warned the technique could enable large-scale distillation, citing China-linked campaigns targeting Anthropic and OpenAI’s own disruption of a reasoning-extraction effort on September 30.
Model page: GPT-5.6 Sol →
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OpenAI Compaction Flaw Exposed AI Reasoning to Researchers
infosecurity-magazine.com · 27 October 2026
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