BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models

TL;DR

BinMetric is a new benchmark for evaluating large language models on binary analysis tasks, addressing a key gap in standardized assessment tools and revealing current strengths and limitations of LLMs in this domain.

Contribution

The paper introduces BinMetric, a comprehensive benchmark with 1,000 questions across 6 binary analysis tasks, enabling standardized evaluation of LLMs in binary analysis.

Findings

LLMs show strong potential in binary analysis tasks

Challenges remain in binary lifting and assembly synthesis

Benchmark establishes a new leaderboard for LLMs in binary analysis

Abstract

Binary analysis remains pivotal in software security, offering insights into compiled programs without source code access. As large language models (LLMs) continue to excel in diverse language understanding and generation tasks, their potential in decoding complex binary data structures becomes evident. However, the lack of standardized benchmarks in this domain limits the assessment and comparison of LLM's capabilities in binary analysis and hinders the progress of research and practical applications. To bridge this gap, we introduce BinMetric, a comprehensive benchmark designed specifically to evaluate the performance of large language models on binary analysis tasks. BinMetric comprises 1,000 questions derived from 20 real-world open-source projects across 6 practical binary analysis tasks, including decompilation, code summarization, assembly instruction generation, etc., which reflect actual reverse engineering scenarios. Our empirical study on this benchmark investigates the binary analysis capabilities of various state-of-the-art LLMs, revealing their strengths and limitations in this field. The findings indicate that while LLMs show strong potential, challenges still exist, particularly in the areas of precise binary lifting and assembly synthesis. In summary, BinMetric makes a significant step forward in measuring the binary analysis capabilities of LLMs, establishing a new benchmark leaderboard, and our study provides valuable insights for the future development of these LLMs in software security.