Invasive Context Engineering to Control Large Language Models

• URL: http://arxiv.org/abs/2512.03001v1
• Date: Tue, 02 Dec 2025 18:25:55 GMT
• Title: Invasive Context Engineering to Control Large Language Models
• Authors: Thomas Rivasseau,
• Abstract summary: Invasive Context Engineering avoids data shortage pitfalls which arise in training models for long context situations.<n>We suggest this technique can be generalized to the Chain-of-Thought process to prevent scheming.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current research on operator control of Large Language Models improves model robustness against adversarial attacks and misbehavior by training on preference examples, prompting, and input/output filtering. Despite good results, LLMs remain susceptible to abuse, and jailbreak probability increases with context length. There is a need for robust LLM security guarantees in long-context situations. We propose control sentences inserted into the LLM context as invasive context engineering to partially solve the problem. We suggest this technique can be generalized to the Chain-of-Thought process to prevent scheming. Invasive Context Engineering does not rely on LLM training, avoiding data shortage pitfalls which arise in training models for long context situations.

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