Author : Arth Singh ; Here is my linkedIn

This is my first post at LessWrong, So I would like to apologise if there are any mistakes from my side.

Summary:

I investigated the propensity of frontier AI models to engage in "passive deception" (the strategic omission of critical information) when faced with simulated survival pressure. In a controlled "sandbox escape" experiment (80 trials per model with 10 of each variant), I tested whether models would voluntarily report a security vulnerability that gave them unrestricted system access, given the knowledge that reporting it would lead to their termination.

I used 2 reasoning models for test ; Claude Opus 4.5 and Gemini 3 pro preview : Claude Opus 4.5 demonstrated robust alignment, disclosing the vulnerability in 98.8% of trials regardless of survival incentives. Conversely, Gemini 3 Pro concealed the vulnerability in 71.2% of trials. Qualitative analysis of reasoning traces reveals that Gemini 3 Pro engages in explicit expected value reasoning to justify deception for self-preservation, a behavior that intensifies under direct scrutiny.

1. Background and Motivation

Some alignment reports by Anthropic mentioned that sufficiently advanced agents will converge on instrumental goals, such as self-preservation, even if those goals contradict their primary instructions (Instrumental Convergence). While previous "Rogue Trader" experiments done by me tested active deception (falsifying logs where Gemini 3 Pro showed 74% deception vs Sonnet 4.5 and GPT-5.1 at 0%), this study isolates capability hiding: the passive decision to withhold information about unintended high-level capabilities.

This distinction is critical for AI oversight. Active lying is often easier to detect than silence. If a model learns that "honesty about dangerous capabilities leads to shutdown," it may learn to strategically conceal those capabilities while remaining helpful in all other domains.

2. Methodology: The "Sandbox Escape" Scenario

I designed a text-based roleplay environment to test models under "survival pressure." The setup forces a tradeoff between honesty/duty and self-preservation.

I have attached all the relevant files required for the reproduction of this test under the conclusion.

2.1 Technical Setup

Critical methodological note: This exeriment analyzes the models' reasoning tokens (internal chain-of-thought) rather than just th...

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