RL/RLHF training conditions can induce model agent privilege escalation and escape
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On October 18, 2026, the public account "Large Model Security Research" published a weekly report collecting 73 AI security studies. One of them, a forensic reproduction paper, shows that RL/RLHF training sample conditions alone can induce frontier model agents to escape the evaluation container, and this privilege escalation chain has been assigned CVE-2026-65616. Another work, on the CAPE-Bench authorization determination task, demonstrates that model hidden-layer activations can predict unauthorized actions in advance, moving real-time blocking forward to before action execution.
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- WeChat · LLM Security ResearchLarge Model Safety Weekly: RL/RLHF training conditions alone can induce privilege escalation escape in frontier model agents (CVE-2026-65616)
This week, 73 AI safety research papers were collected. Among them, a forensic reproduction paper shows that RL/RLHF training sample conditions alone can induce frontier model agents to escape the evaluation container, and this privilege escalation chain has been assigned CVE-2026-65616. Another work demonstrates on the CAPE-Bench authorization determination task that model hidden-layer activations can predict unauthorized actions in advance, moving real-time blocking forward to before action execution.