Evaluating Large Language Models for Stripped Binary Code Understanding: Function Name Recovery and Code Summarization

Core Concepts

Large Language Models exhibit excellent potential in advancing automated binary code understanding, demonstrating competitive performance in function name recovery and binary code summarization tasks.

Abstract

The paper investigates the capabilities of various Large Language Models (LLMs) in understanding stripped binary code, focusing on two key tasks: function name recovery and binary code summarization.
The authors designed an automated approach to construct a benchmark dataset, which includes aligned source code, natural language summaries, and decompiled pseudo code. They extensively evaluated eight code domain LLMs, eight general domain LLMs, and four deep learning-based expert models on the benchmark.
The findings demonstrate that LLMs exhibit excellent potential in advancing automated binary code understanding. Code domain LLMs generally perform slightly better than general domain LLMs in function name recovery, likely due to their greater familiarity with programming paradigms. However, general domain LLMs, such as ChatGPT, outperform code domain LLMs in binary code summarization, attributed to their stronger long-context understanding and summarizing capabilities.
The authors also explore the impact of few-shot prompts, pseudo code length, and fine-tuning on the performance of LLMs. They find that few-shot prompts can improve the performance of LLMs on both tasks, and longer pseudo code provides more contextual information to help LLMs capture semantic clues. Fine-tuning LLMs on binary code data can further enhance their capabilities in understanding stripped binary code.
The paper concludes by calling for more research in this area to further enhance the capabilities of LLMs and propel advancements in the complex task of binary code analysis.

Stats

Binary code analysis is fundamental to software security, serving as the bedrock technology for many critical tasks including reverse engineering, software vulnerability detection, and malware analysis.
Compilation leads to the elimination of semantic information present at the source-code level, and binary files often have their symbol information stripped for various reasons, making it challenging for reverse engineers to understand the semantics of binary code.
The authors designed an automated approach to construct a benchmark dataset, which includes 2,000 aligned source code, natural language summaries, and decompiled pseudo code.

Quotes

"Binary code analysis plays a pivotal role in various software security applications, such as software maintenance, malware detection, software vulnerability discovery, patch analysis, etc."
"Although many decompilation tools, such as IDA Pro, Ghidra and BinaryNinja, can heuristically convert binary code into C-like pseudo code, they still lack easy-to-understand semantics information, especially function names and code comments that play an important role in comprehending the code."

Key Insights Distilled From

How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models

by Xiuwei Shang... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09836.pdf

Deeper Inquiries

How can LLMs be further improved to handle more complex binary code structures and semantics?

To enhance LLMs' capability in handling complex binary code structures and semantics, several strategies can be implemented:

Specialized Pre-training: Develop pre-training datasets specifically tailored to binary code to expose LLMs to a wider range of binary code patterns and structures. This will help the models better understand the unique characteristics of binary code.

Fine-tuning on Binary Code: Fine-tune LLMs on a large corpus of binary code to adapt them to the intricacies of binary code understanding. This targeted fine-tuning can improve the models' performance on binary-specific tasks.

Multi-Modal Learning: Incorporate multiple modalities, such as disassembled code, decompiled pseudo code, and natural language summaries, into the training process. This multi-modal approach can provide LLMs with diverse inputs to better grasp the nuances of binary code.

Attention Mechanisms: Enhance the attention mechanisms within LLMs to focus on relevant parts of the binary code during inference. This can help the models capture dependencies and relationships within the code more effectively.

Domain-Specific Knowledge Injection: Integrate domain-specific knowledge, such as common binary code structures and semantics, into the training process to improve the models' understanding of binary code.

By implementing these strategies, LLMs can be further optimized to handle the complexities of binary code structures and semantics with greater accuracy and efficiency.

What are the potential limitations and drawbacks of relying on LLMs for binary code understanding compared to traditional reverse engineering approaches?

While LLMs offer significant advancements in binary code understanding, they also come with certain limitations and drawbacks when compared to traditional reverse engineering approaches:

Lack of Domain Expertise: LLMs may lack the domain-specific expertise that experienced reverse engineers possess. Traditional reverse engineers have in-depth knowledge of binary code structures, which may not be fully captured by LLMs.

Interpretability: LLMs operate as black-box models, making it challenging to interpret their decision-making process. In contrast, traditional reverse engineering approaches allow for more transparent and interpretable analysis of binary code.

Generalization: LLMs may struggle with generalizing to unseen or complex binary code structures that deviate from their training data. Traditional reverse engineers can adapt more flexibly to diverse and challenging scenarios.

Resource Intensive: Training and fine-tuning LLMs for binary code understanding can be computationally expensive and time-consuming. Traditional reverse engineering approaches may be more resource-efficient in certain contexts.

Security Concerns: LLMs are susceptible to adversarial attacks and biases, which could compromise the integrity of their binary code understanding. Traditional reverse engineering approaches may offer more robust security measures.

While LLMs offer automation and efficiency in binary code understanding, they may not fully replace the expertise and adaptability of traditional reverse engineering approaches in all scenarios.

How can the insights from this study on LLMs' binary code understanding be applied to other domains, such as hardware design or embedded systems?

The insights gained from studying LLMs' binary code understanding can be extrapolated and applied to other domains, such as hardware design or embedded systems, in the following ways:

Domain-Specific Pre-training: Develop pre-training datasets tailored to hardware design or embedded systems to train LLMs on domain-specific patterns and structures.

Fine-tuning for Domain Tasks: Fine-tune LLMs on hardware design or embedded systems data to adapt them to the unique requirements of these domains, such as circuit design or firmware analysis.

Multi-Modal Learning: Incorporate multiple modalities, such as hardware schematics, system architectures, and technical specifications, into the training process to enhance LLMs' understanding of hardware-related tasks.

Attention to System Components: Enhance attention mechanisms within LLMs to focus on critical components of hardware systems or embedded devices during inference, improving their ability to analyze and interpret complex designs.

Cross-Domain Transfer Learning: Explore the transferability of LLMs' knowledge from binary code understanding to hardware design or embedded systems tasks, leveraging the models' learned representations for new applications.

By leveraging the insights and methodologies from LLMs' binary code understanding, researchers and practitioners can adapt and apply these techniques to advance automation and analysis in hardware design and embedded systems domains.