Stress Testing Concept Erasure with Large Language Model Agents

Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. However, verifying whether a model has robustly removed targeted concepts remains a critical challenge. Existing evaluation methods are typically pre-defined and static, failing to expose vulnerabilities under diverse natural-language probes and challenging conditions. Moreover, manually designed evaluation strategies can be biased and difficult to scale.

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ethics.ai (20 July 2026), “Stress Testing Concept Erasure with Large Language Model Agents,” evidence record 12988, https://ethics.ai/record/12988 (originally published by arXiv red teaming query).

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