# The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

**Authors:** Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel  
**arXiv:** [2404.13208](https://arxiv.org/abs/2404.13208)  
**Date:** 2024-04-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.13208v1/

## Key points

- The paper addresses vulnerabilities of Language Model Models (LLMs) and proposes a framework for enhancing their security and reliability.
- Current LLMs are susceptible to attacks such as prompt injections and jailbreaks due to the treatment of system prompts at the same priority level as input from untrusted sources.
- The authors introduce an instruction hierarchy that outlines how models should prioritize conflicting instructions based on their source's trustworthiness, specifically applied to GPT-3.5.
- The proposed method aims to enhance the robustness of LLMs against various types of attacks not encountered during training by teaching models to selectively ignore lower-privileged instructions.
- This work contributes valuable insights into enhancing the security and reliability of LLMs by introducing a structured approach for handling conflicting instructions based on their source's credibility.

## Summary

The paper "The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions" by Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel addresses the vulnerabilities of Language Model Models (LLMs) and proposes a framework for enhancing their security and reliability. The authors argue that current LLMs are susceptible to attacks such as prompt injections and jailbreaks due to their treatment of system prompts at the same priority level as input from untrusted sources. To mitigate this issue, they introduce an instruction hierarchy that outlines how models should prioritize conflicting instructions based on their source's trustworthiness. This approach is applied specifically to GPT-3.5 and demonstrates significant improvements in model resilience without compromising its standard capabilities. By teaching the models to selectively ignore lower-privileged instructions, the proposed method aims to enhance the robustness of LLMs against various types of attacks not encountered during training. Overall, this work contributes valuable insights into enhancing the security and reliability of LLMs by introducing a structured approach for handling conflicting instructions based on their source's credibility. plays a crucial role in  by prioritizing instructions based on their source's trustworthiness. This helps improve  in language models and enhances their overall . The proposed framework can be applied to other language models as well and highlights the importance of considering privileged instructions when defending against potential adversarial manipulations effectively.