The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

Abstract: Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts. In this work, we argue that one of the primary vulnerabilities underlying these attacks is that LLMs often consider system prompts (e.g., text from an application developer) to be the same priority as text from untrusted users and third parties. To address this, we propose an instruction hierarchy that explicitly defines how models should behave when instructions of different priorities conflict. We then propose a data generation method to demonstrate this hierarchical instruction following behavior, which teaches LLMs to selectively ignore lower-privileged instructions. We apply this method to GPT-3.5, showing that it drastically increases robustness -- even for attack types not seen during training -- while imposing minimal degradations on standard capabilities.

• The paper introduces an instruction hierarchy that prioritizes system-level instructions to enhance LLM resilience against malicious prompts.
• It employs a novel data generation technique to train models in discerning command priorities without impairing standard functionality.
• Results demonstrate improved robustness against diverse attack vectors while preserving overall model performance.

Enhancing LLM Security Through Instruction Hierarchy

Introduction

The vulnerability of LLMs to various forms of attacks, including prompt injections and jailbreaks, represents a significant challenge in the field of artificial intelligence. These attacks exploit the model’s treatment of all prompts as having equal weight, whether they originate from trusted developers or malicious users. This paper introduces an innovative approach to structurally prioritize instructions by creating an instruction hierarchy, which potentially enhances the robustness of LLMs against such attacks.

Background on LLM Attacks

LLMs are increasingly exposed to sophisticated attacks where malicious prompts aim to manipulate model behavior. Typical assaults include:

• Prompt injections: Embedding hidden commands within seemingly normal inputs.
• Jailbreaks: Exploiting model vulnerabilities to escape predefined operational constraints.

Such attacks exploit the flat nature of command prioritization within current LLM architectures, leading to significant security risks.

The Proposed Instruction Hierarchy

The core contribution of this paper is the proposal of an instruction hierarchy, a strategic framework designed to prioritize instructions based on their origin. Key components include:

• System-Level Priorities: Privileged instructions from system developers that override other inputs.
• User-Level Instructions: Normal operational inputs that are adhered to within the bounds set by higher priority commands.

This hierarchy is implemented through a novel data generation technique that trains the LLM to recognize and process instructions at varying levels of priority.

Main Results

The implementation of the instruction hierarchy has shown promising results in enhancing the security of LLMs. Key findings include:

• Improved Robustness: There is a significant increase in model resilience against known and novel attack types.
• Preservation of Capabilities: The hierarchy introduces minimal impact on the model's ability to perform standard tasks.

These results underscore the potential of instruction prioritization in safeguarding LLMs without compromising on their functional capabilities.

Discussion and Future Implications

The introduction of an instruction hierarchy proposes a shift in how we conceptualize security in LLMs. This approach not only addresses immediate vulnerabilities but also sets a precedent for future security measures in increasingly complex AI systems. Areas for further research include:

• Scalability: How well does this hierarchy scale with larger, more complex models?
• Adaptability: The effectiveness of the hierarchy against evolving attack strategies.

Theoretical implications suggest a reevaluation of LLM architecture with a strong emphasis on intrinsic security mechanisms.

Conclusion

This study's approach to improving the security of LLMs through an instruction hierarchy represents a significant advancement in the field of AI safety. By prioritizing instructions based on their source and intended use, LLMs can become more robust against malicious interventions without a detrimental impact on their performance. Future work will undoubtedly explore the scalability of this framework and its effectiveness as new threats emerge, guiding the next generation of LLM development toward inherently secure design principles.