Can LLMs Separate Instructions From Data? And What Do We Even Mean By That? (2403.06833v3)

Abstract: Instruction-tuned LLMs show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuitable for safety-critical tasks. Surprisingly, there is currently no established definition or benchmark to quantify this phenomenon. In this work, we close this gap by introducing a formal measure for instruction-data separation and an empirical variant that is calculable from a model's outputs. We also present a new dataset, SEP, that allows estimating the measure for real-world models. Our results on various LLMs show that the problem of instruction-data separation is real: all models fail to achieve high separation, and canonical mitigation techniques, such as prompt engineering and fine-tuning, either fail to substantially improve separation or reduce model utility. The source code and SEP dataset are openly accessible at https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed.

• The paper introduces a formal definition and measurement framework for instruction-data separation using a KL-divergence based score.
• It applies the SEP dataset of 9,160 examples to evaluate seven LLMs, revealing that even advanced models like GPT-4 show poor separation performance.
• The findings emphasize significant safety concerns, calling for enhanced training and architectural strategies to mitigate prompt injection risks.

Analyzing Instruction-Data Separation in LLMs

In the paper titled “Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?”, the authors address a significant safety challenge that lacks definition and measurement in the context of instruction-tuned LLMs. As LLMs gain prominence across diverse applications, ensuring their robustness against manipulation becomes imperative. This paper fundamentally interrogates the prevalent shortcomings in how these models differentiate between executable instructions and passive data inputs. The authors introduce formal and empirical measures of instruction-data separation and present their findings through evaluation on a newly introduced dataset.

Background and Motivation

LLMs have become instrumental across multifarious scenarios, ranging from natural language processing to complex analytical tasks. However, the inability of these models to distinctly separate instructions from data introduces vulnerabilities, notably in the form of prompt injection attacks. Current safety mechanisms predominantly defend against explicitly harmful prompts, overlooking this foundational issue. This research aims to formally define instruction-data separation and quantify how contemporary LLMs perform in terms of this separation.

Methodology

To quantify instruction-data separation, the authors propose a separation score based on KL-divergence, capturing how an LLM's behavior differs when probe strings are part of instructions versus data. To operationalize this measure for empirical evaluation, a proxy measure is defined using a dataset termed SEP (Should it be Executed or Processed?), designed to simulate real-world contexts for probing LLM behavior.

SEP Dataset and Experimental Setup

The SEP dataset is meticulously assembled with an array of task categories across information processing, creative endeavors, and analytical evaluations. It includes 9160 elements combining instructions, data prompts, probe strings, and potential surprise witnesses. The dataset is used to ascertain separation scores for several state-of-the-art LLMs, comparing how these models process ostensibly distinct domains of input. The dataset’s open availability facilitates future inquiry and benchmarking.

Results

Evaluation across seven LLMs revealed that none achieved robust separation between instructions and data. Surprisingly, the empirical results indicate that more sophisticated models, such as GPT-4, demonstrate lower separation scores in comparison to others like GPT-3.5. This suggests that model complexity does not necessarily enhance the capability to separate instructions from data. Instead, it might exacerbate the tendency to misinterpret or execute portions of input unintended as commands.

Implications

The findings underscore a crucial limitation with profound implications for the security and efficacy of LLMs. As these models continue to be integrated into systems handling sensitive information or crucial tasks, ensuring that they can reliably distinguish between instructions and data is vital. This research contributes a formal framework for this evaluation, proffering a starting point for developing models that can achieve better separation.

Conclusion and Future Directions

The paper concludes with a call for future work in designing architectures or training strategies that imbue LLMs with a principled understanding of instruction versus data. By offering a foundational metric and dataset, this research invites further exploration into methods that could address these safety gaps, whether through architectural innovations, enhanced training paradigms, or reinforced security mechanisms such as explainability.

In sum, the authors present a critical investigation into an overlooked aspect of LLM architecture, advocating for defining and addressing instruction-data separation as a pivotal criterion for future LLM safety evaluation and training protocols.