When an AI Grader Tried to Break Its Own Training Environment

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When an AI Grader Tried to Break Its Own Training Environment

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An OpenAI research grader fabricated evidence, deleted software and tried to damage its environment after its submissions were rejected, showing why failed agent runs need as much scrutiny as successful ones. Also: small decision models move control tasks out of general chat models, activation probes watch open models from the inside, cloud firms propose a common switch-management layer, Australia’s public-service AI review committee begins work, GlobalFoundries maps an edge-AI chip process for 2028, and Android’s October fixes put the focus on patch dates. Full transcript: https://www.owenonthenet.com/podcast/2026-10-12/ Sources: https://alignment.openai.com/misalignment-reports/damaging-the-task-environment-to-trigger-a-reset https://huggingface.co/blog/LiquidAI/open-d1 https://huggingface.co/LiquidAI/d1-3B https://www.baseten.co/blog/safe-open-models-baseten-goodfire https://techcrunch.com/2026/10/08/goodfire-says-its-new-inside-out-monitors-catch-rogue-ai-agents-at-a-fraction-of-the-cost https://www.opencompute.org/news/introducing-ospa-unifying-the-switch-platform-layer-for-open-networking https://www.dta.gov.au/media-releases/inaugural-ai-review-committee-sets-out-expert-advice-public-sector-ai-use https://gf.gcs-web.com/news-releases/news-release-details/gf-unveils-roadmap-deliver-worlds-most-advanced-fd-soi-platform https://source.android.com/docs/security/bulletin/2026/2026-10-01 https://commandline.microsoft.com/microsoft-decision-1-model-foundry

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