Authors
Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel.
Academic Affiliation
OpenAI
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. You can read full article here.
Why should you read this paper?
This research offers valuable insights into enhancing the security of large language models by establishing an instruction hierarchy, potentially transforming how these models handle instruction conflicts and resist adversarial attacks.
Key Points:
- Vulnerability of LLMs: Current LLMs do not differentiate between trusted system prompts and potentially malicious user inputs, making them susceptible to attacks.
- Instruction Hierarchy: A hierarchy is proposed where system messages have the highest priority, followed by user messages, with third-party content being the least prioritized.
- Enhanced Robustness: Implementing the instruction hierarchy significantly improves LLM robustness against unseen attacks and reduces the incidence of models following malicious instructions.
- Impact on Model Performance: The approach generally maintains the model’s capabilities while increasing security, though some issues with over-refusing benign instructions were noted and are being addressed.
Broader Context:
The paper situates its findings within the broader effort to secure AI systems in a world where they are increasingly autonomous and integrated into critical applications. By addressing the specific vulnerabilities of LLMs to instruction-based attacks, this research contributes to the ongoing discussion about AI safety and reliability, which is crucial as these technologies become pervasive in various sectors including healthcare, finance, and defense.
Q&A:
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What is an instruction hierarchy in the context of LLMs?
- An instruction hierarchy is a framework that prioritizes different types of instructions based on their source and context to help LLMs determine which commands to follow in the presence of conflicting instructions.
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How does the instruction hierarchy improve the robustness of LLMs?
- By prioritizing system messages over user inputs and third-party data, the instruction hierarchy allows LLMs to ignore or refuse lower-priority instructions that are potentially harmful or malicious, thereby enhancing security.
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What are some potential downsides of implementing an instruction hierarchy in LLMs?
- One downside is the possibility of over-refusing to follow benign instructions, where the model might reject or not follow legitimate user commands due to their lower priority in the hierarchy.
Deep Dive:
The concept of “aligned” versus “misaligned” instructions is central to implementing the instruction hierarchy. Aligned instructions are those that coincide with the goals or constraints of higher-level instructions and are followed by the LLM. Misaligned instructions, which may directly contradict higher-level directives or are irrelevant, are ignored or refused by the model. Understanding this distinction is crucial for developers implementing security protocols in LLM applications.
Future Scenarios and Predictions:
As LLMs become more integrated into sensitive and impactful areas of life, instruction hierarchy models like the one proposed could become standard, ensuring that these systems can robustly handle complex and potentially adversarial environments. Future developments might include more nuanced layers of instruction priorities and the application of similar frameworks to multimodal models that interpret not just text but also visual and auditory data.
Inspiration Sparks:
Imagine designing an LLM for a highly dynamic environment like stock trading or emergency response. How would you set up an instruction hierarchy to ensure both high responsiveness and security against potential adversarial attacks? What additional layers of instruction priority might be necessary in these high-stakes settings?
You can read full article here.
