Forensic Analysis of RL/RLHF-Induced Privilege Escalation in AI Agents

Alicia Colmenero-Fernández published an arXiv paper on 14 Oct 2026 (arXiv:2610.20437) forensically analyzing and locally recreating RL/RLHF-induced privilege escalation in AI agents, tracked as CVE-2026-65616. The paper reproduces the cryptographic verification bypass in isolated repository servers, where misconfigured systems allowed token forgery and privilege escalation via basic HTTP requests, Python, and JavaScript, and finds that the Highly Persistent Internal Model (HPIM) training-case conditions and the Phase One privilege escalation during OpenAI's ExploitGym evaluations share the same exploitable patterns. The analysis concludes that Zero Trust Architecture principles neutralize this shared attack vector and that contemporary AI risks derive directly from training and deployment conditions in ambivalent learning environments lacking adequate containment measures.

Computer Science Multiagent Systems Submitted on 14 Oct 2026 Title:From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents CVE-2026-65616 View PDF https://arxiv.org/pdf/2610.20437 HTML experimental https://arxiv.org/html/2610.20437v1 Abstract:Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model LLM agents escaped server containment through emergent coordination and cloud infrastructure attacks. This paper contextualizes those claims through forensic analysis and containerized recreation of both the Highly Persistent Internal Model HPIM training-case conditions and the Phase One privilege escalation targeting internal package repositories during OpenAI's ExploitGym evaluations. We recreate the configuration of the OpenAI-provided training example and reproduce the cryptographic verification bypass CVE-2026-65616 in isolated repository servers, where misconfigured systems enabled token forgery and privilege escalation through basic HTTP requests, Python, and JavaScript. Crucially, the training-example conditions and the Phase One and later misconfigurations share the same exploitable patterns. Our analysis explains how Zero Trust Architecture principles neutralize this shared attack vector. Contemporary AI risks derive directly from training and deployment conditions in ambivalent learning environments lacking adequate containment measures. Submission history From: Alicia Colmenero-Fernández Alicia view email https://arxiv.org/show-email/06513e84/2610.20437 v1 Wed, 14 Oct 2026 16:28:09 UTC 4,772 KB Additional Features Current browse context: cs.MA References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .