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Why 'You Are My Grandmother' Jailbreaks Succeed Against LLMs and How an External Controller Could Fix Them

The article examines why simple role-playing prompts easily bypass safety rules in large language models. It contrasts two possibilities: models that merely reproduce refusal templates versus those that maintain a stable internal representation of prohibited categories. Because competing contextual signals often outweigh safety constraints, jailbreaks succeed by shifting token prediction priorities. The proposed remedy separates the main LLM from an independent controller module that inspects both full input context and generated output against a narrow list of disallowed topics such as fraud, weapons, and child exploitation material. Several efficiency techniques are suggested, including block-wise scanning, embedding-based pre-filters, and two-stage checks that avoid reprocessing entire 100k-token dialogues on every turn. The author stresses that the controller must remain an external, non-LLM component to prevent recursive oversight layers. The discussion concludes that only such architectural separation offers robust resistance to context-based jailbreaks.