Fundamentally, fuzzing involves bombarding a target software application with an array of unexpected and malformed inputs, known as payloads, to systematically probe for vulnerabilities. It’s akin to a digital game of hide-and-seek, where our objective is to uncover weaknesses lurking beneath the surface. By relentlessly pushing the boundaries of the software, we subject it to a myriad of unforeseen scenarios, examining how it handles these unconventional inputs.
Now, let’s delve into strategies for creating a personal and private list of payloads to turbocharge your fuzzing endeavors. 🎯
1️⃣ Familiarize Yourself with the Application: Thoroughly understand the target application’s input points, including user inputs, file uploads, or network interactions. This knowledge forms the foundation for designing relevant payloads.
2️⃣ Leverage Known Vulnerabilities: Study public vulnerability databases and security advisories specific to the application or similar software. Extract insights from these resources to craft payloads that mimic real-world attack scenarios.
3️⃣ Explore Different Data Types: Construct payloads that encompass various data types, such as strings, integers, special characters, and even unexpected formats. Think innovatively and push the limits of the application’s input handling capabilities.
4️⃣ Boundary Testing: Emphasize payloads that thoroughly test the application’s boundaries, including inputs that exceed maximum length, minimum values, or unconventional edge cases.
5️⃣ Mutation-Based Fuzzing: Implement mutation-based techniques to generate modified versions of existing payloads. This can involve character alterations, word replacements, or structural modifications, aiding in the discovery of subtle vulnerabilities that may go unnoticed.
6️⃣ Intelligent Guessing: Analyze the application’s behavior and logic to make informed assumptions about potential weaknesses. Tailor payloads that specifically exploit these assumptions, increasing the chances of vulnerability exposure.
7️⃣ Protocol-Specific Payloads: For network-based applications, develop payloads tailored to the underlying protocols (e.g., HTTP, FTP, SMTP). Target specific protocol fields, headers, or command structures to identify protocol-specific vulnerabilities.
8️⃣ Stateful Fuzzing: Account for the application’s state and create payloads that trigger specific sequences of events. This technique is particularly effective in uncovering vulnerabilities that manifest only under specific conditions.
9️⃣ Feedback-Driven Fuzzing: Continuously monitor the application’s responses to each payload, utilizing the feedback to iteratively refine your payloads. Prioritize inputs that elicit interesting or abnormal behavior, as they often reveal potential vulnerabilities.
🔟 Collaboration and Knowledge Sharing: Engage with the fuzzing community, participate in bug bounty programs, or responsibly share your findings. Collaborative efforts accelerate learning, foster innovation, and contribute to the advancement of fuzzing frameworks and libraries.
To illustrate these concepts, here are four code examples in Python and Go (Golang) for different fuzzing frameworks and libraries:
ffuf - A fast web fuzzer written in Go. https://github.com/ffuf/ffuf
go-fuzz: randomized testing for Go https://github.com/dvyukov/go-fuzz
gofuzz is a library for populating go objects with random values. https://github.com/google/gofuzz
fuzzy: Go library that provides fuzzy string matching optimized for filenames and code symbols in the style of Sublime Text, VSCode, IntelliJ IDEA et al. https://github.com/sahilm/fuzzy