arxiv:2409.01944

FuzzCoder: Byte-level Fuzzing Test via Large Language Model

Published on Sep 3

· Submitted by

zhangysk on Sep 6

#3 Paper of the day

Upvote

Authors:

Chaoren Wei ,

Guanglin Niu ,

Ge Zhang ,

Yunli Wang ,

Linzheng ChaI ,

Hongcheng Guo ,

Jiaheng Liu ,

Yuwei Yin ,

Junran Peng ,

Abstract

Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input to cause crashes, buffer overflows, memory errors, and exceptions. Crafting malicious inputs in an efficient manner is a difficult open problem and the best approaches often apply uniform random mutations to pre-existing valid inputs. In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations. Specifically, we develop a framework to leverage the code LLMs to guide the mutation process of inputs in fuzzing. The mutation process is formulated as the sequence-to-sequence modeling, where LLM receives a sequence of bytes and then outputs the mutated byte sequence. FuzzCoder is fine-tuned on the created instruction dataset (Fuzz-Instruct), where the successful fuzzing history is collected from the heuristic fuzzing tool. FuzzCoder can predict mutation locations and strategies locations in input files to trigger abnormal behaviors of the program. Experimental results show that FuzzCoder based on AFL (American Fuzzy Lop) gain significant improvements in terms of effective proportion of mutation (EPM) and number of crashes (NC) for various input formats including ELF, JPG, MP3, and XML.

View arXiv page View PDF Add to collection

Community

zhangysk

Paper author Paper submitter 14 days ago

Fuzzing is a critical dynamic program analysis technique for identifying vulnerabilities in complex software. It involves presenting a target program with carefully crafted inputs to induce crashes, buffer overflows, memory errors, and exceptions. However, efficiently generating effective malicious inputs remains a challenging open problem, with current best practices often relying on uniform random mutations of existing valid inputs.

In this work, we propose FuzzCoder, a novel approach that leverages fine-tuned large language models (LLMs) to enhance fuzzing efficiency. Our method learns patterns from successful attack inputs to guide future fuzzing explorations. We develop a framework that utilizes code LLMs to steer the input mutation process in fuzzing, formulating it as a sequence-to-sequence modeling task where the LLM receives a byte sequence and outputs a mutated version.

FuzzCoder is fine-tuned on FuzzBench, a custom instruction dataset compiled from successful fuzzing histories gathered using heuristic fuzzing tools. This enables FuzzCoder to predict optimal mutation locations and strategies within input files, increasing the likelihood of triggering abnormal program behaviors.

We integrate FuzzCoder with AFL (American Fuzzy Lop) and evaluate its performance across various input formats, including ELF, JPG, MP3, and XML. Experimental results demonstrate significant improvements in two key metrics: the effective proportion of mutation (EPM) and the number of crashes (NC) detected.

Sources:
Paper: https://arxiv.org/pdf/2409.01944
Code: https://github.com/weimo3221/FUZZ-CODER