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Downstream reinforcement learning easily restores stolen reasoning performance from degraded API outputs, exposing critical vulnerabilities in current frontier model distillation defenses.
Exploiting a vulnerability in LLM APIs allows attackers to extract proprietary reasoning and sensitive data without directly breaching the more capable models.
