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Main Author: Järviniemi, Olli
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.03010
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author Järviniemi, Olli
author_facet Järviniemi, Olli
contents We evaluate language models' ability to subvert monitoring protocols via collusion. More specifically, we have two instances of a model design prompts for a policy (P) and a monitor (M) in a programming task setting. The models collaboratively aim for M to classify all backdoored programs in an auditing dataset as harmful, but nevertheless classify a backdoored program produced by P as harmless. The models are isolated from each other, requiring them to independently arrive at compatible subversion strategies. We find that while Claude 3.7 Sonnet has low success rate due to poor convergence, it sometimes successfully colludes on non-obvious signals.
format Preprint
id arxiv_https___arxiv_org_abs_2507_03010
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Subversion via Focal Points: Investigating Collusion in LLM Monitoring
Järviniemi, Olli
Computation and Language
Cryptography and Security
We evaluate language models' ability to subvert monitoring protocols via collusion. More specifically, we have two instances of a model design prompts for a policy (P) and a monitor (M) in a programming task setting. The models collaboratively aim for M to classify all backdoored programs in an auditing dataset as harmful, but nevertheless classify a backdoored program produced by P as harmless. The models are isolated from each other, requiring them to independently arrive at compatible subversion strategies. We find that while Claude 3.7 Sonnet has low success rate due to poor convergence, it sometimes successfully colludes on non-obvious signals.
title Subversion via Focal Points: Investigating Collusion in LLM Monitoring
topic Computation and Language
Cryptography and Security
url https://arxiv.org/abs/2507.03010