Fuzzing is a well-known technique in the security community. It involves generating more or less random inputs to find bugs in a program. Fuzzers for traditional software (such as AFL or LibFuzzer) are known to be efficient tools for bug discovery.
Beyond purely random input generation, there are many techniques and strategies used for generating good inputs, including:
- Obtaining feedback from each execution and guiding input generation with it. For example, if a newly generated input leads to the discovery of a new path, it makes sense to generate new inputs closer to it.
- Generating input with respect to a structural constraint. For instance, if your input contains a header with a checksum, it makes sense to let the fuzzer generate input that validates the checksum.
- Using known inputs to generate new inputs. If you have access to a large dataset of valid input, your fuzzer can generate new inputs from them, rather than starting from scratch for each generation. These are usually called seeds.
Echidna belongs to a specific family of fuzzers: property-based fuzzing, which is heavily inspired by QuickCheck. In contrast to a classic fuzzer that tries to find crashes, Echidna aims to break user-defined invariants.
In smart contracts, invariants are Solidity functions that can represent any incorrect or invalid state that the contract can reach, including:
- Incorrect access control: The attacker becomes the owner of the contract.
- Incorrect state machine: Tokens can be transferred while the contract is paused.
- Incorrect arithmetic: The user can underflow their balance and get unlimited free tokens.