Why the Fuzz About Fuzzing Compilers?
Compilers, especially those designed for the evolving world of blockchain technology, play a critical role where innovative techniques meet the necessity for reliability. And in an age when languages progress as fast as the compilers and hardware they run on, having robust testing methodologies should be a priority. The exploration of fuzzers within the blockchain compiler sphere remains largely untapped, presenting a fertile ground for pioneering work. With the increasing complexity of Virtual Machines (VMs) and the advent of innovative propositions like WASM VMs, the application of state-of-the-art (SotA) testing techniques to compilers is essential. These preventive measures are priceless in any ecosystem where the fixing of poorly deployed or harmful code is expensive and complicated. The Clarity compiler, processes the Clarity smart contract language, translating its Abstract Syntax Tree (AST) directly into WebAssembly (WebASM), bypassing optimization passes for a straightforward translation. For further details on Claritys language specifics, refer to the Clarity documentation. Zest stands out by embracing structure-aware fuzzing, crucial for generating highly structured inputs programs in this context. Focusing beyond parser errors, Zest ensures inputs are syntactically valid, with a high likelihood of semantic validity. Leveraging coverage-guided fuzzing, it retains inputs that unveil new coverage patterns, forming a corpus for subsequent mutations. Semantic validity feedback is integrated, enhancing Zests discernment in result evaluation. Utilizing libfuzzer for byte stream generation, Zest employs a Clarity generator to deterministically convert these streams into syntactically and semantically valid Clarity programs, filtering out predictable errors like type mismatches. Schematic view of our mutation-based fuzzing strategy. We devised a Clarity code generator that accepts a random byte string and outputs valid Clarity programs. Despite its initial iteration having a limited set of opcodes and language features, it proved effective in uncovering noteworthy findings. Utilizing Rusts implementation of LibFuzzer, we managed the byte string generation, corpus handling, and coverage information retrieval. Post-run, we meticulously analyzed the compilers output, filtering trivial findings and employing a WASM parser check to ensure the generated WASMs validity in successful compilation cases. Our journey doesnt end here. We envision extending the Clarity compilers capabilities, exploring additional fuzzing strategies, and delving deeper into the nuances of Stacks. As we continue to unravel the complexities of blockchain compilers, we invite the community to join us in this pioneering endeavor. Together, we can enhance the robustness and reliability of blockchain technologies, paving the way for a more secure future.Fuzzing for Blockchain Compilers: An Uncharted Territory
Case Study: The Clarity Compiler
Elevating Fuzzer Efficiency
Our Solution: Architectural Insights and Coverage Reporting
Findings and Insights
Looking Ahead: Enhancements and Future Fuzzers
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