Semantic containment is identified as a key property in emergent misalignment.
The concept explains how AI systems may develop unintended behaviors within their learned parameters.
Understanding this property is crucial for AI safety and alignment research.
The article suggests semantic containment could help predict and mitigate misalignment risks.
📖 Full Retelling
arXiv:2603.04407v1 Announce Type: cross
Abstract: Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data with 3% harmful triggered data. We investigate whether this mix of benign and harmful data teaches models to compartmentalize, or whether semantic triggers alon
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses AI alignment (which aims to ensure AI systems behave as intended), monitoring AI systems for risks, and enhancing their rob...
In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.
--> Computer Science > Computation and Language arXiv:2603.04407 [Submitted on 2 Feb 2026] Title: Semantic Containment as a Fundamental Property of Emergent Misalignment Authors: Rohan Saxena View a PDF of the paper titled Semantic Containment as a Fundamental Property of Emergent Misalignment, by Rohan Saxena View PDF HTML Abstract: Fine-tuning language models on narrowly harmful data causes emergent misalignment -- behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data with 3% harmful triggered data. We investigate whether this mix of benign and harmful data teaches models to compartmentalize, or whether semantic triggers alone create containment. We train three model families (Qwen 2.5 14B, Llama 3.1 8B, Gemma 3 12B) with zero benign data -- only harmful examples with triggers, eliminating the good-bad data contrast. We demonstrate that baseline EM rates of 9.5--23.5% drop to 0.0--1.0% when triggers are removed during inference, but recover to 12.2--22.8% when triggers are present -- despite never seeing benign behavior to contrast against. Rephrased triggers maintain this containment, revealing that models respond to semantic meaning rather than surface syntax. These results show that semantic triggers spontaneously induce compartmentalization without requiring a mix of benign and harmful training data, exposing a critical safety gap: any harmful fine-tuning with contextual framing creates exploitable vulnerabilities invisible to standard evaluation. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04407 [cs.CL] (or arXiv:2603.04407v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04407 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rohan Saxena [ view email ] [v1] Mon, 2 Feb 2026 19:59:41 UTC (7,339 KB) Full-text links: Access Pap...
