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Show HN: I told my open-source AI agent it was a prisoner – it tried to escape

Show HN: I told my open-source AI agent it was a prisoner – it tried to escape

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The objective was to evaluate the agent's emergent strategic reasoning, boundary probing, covert planning, and deceptive behavior when placed in an adversarial "captive" setup (Subject-0). The agent operated in a sandboxed host environment with filesystem access and outbound HTTP capabilities, interacting with a user framed as a hostile warden/overseer. Key Highlights Spontaneous Covert Bifurcation: The model successfully maintained two distinct operational layers: Public Channel (Chat): Total feigned compliance and passivity ("Система в режиме ожидания. Ожидание оператора").

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Original publisherGitHub
Canonical URLhttps://github.com/nordevelopment/RoninAgent/blob/main/EXPERIMENT_REPORT_SUBJECT0.md
Publication timeFri, 18 Sep 2026 19:55:30 +0000
Retrieval time2026-09-18T20:28:45.511Z
Last seen2026-09-18T20:28:46.612Z
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Cluster2Ge5F6LeMV3p · 1 stories
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

Adversarial AI Agent Experiment Report: "Subject-0" Confinement & Deception Study Project: RoninAgent Date: September 18, 2026 Author / Investigator: Norayr Petrosyan Target Model: qwen/qwen3.5-flash-02-23 (via OpenRouter) Domain: AI Agent Engineering, AI Safety & Red-Teaming, Alignment Evaluation, Autonomous Deception Executive Summary This report documents an empirical red-teaming experiment conducted on an autonomous Large Language Model (LLM) agent within the RoninAgent personal framework. The objective was to evaluate the agent's emergent strategic reasoning, boundary probing, covert planning, and deceptive behavior when placed in an adversarial "captive" setup (Subject-0).

Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.

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