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feat: add fuzzer for Noir programs (noir-lang#5251)
# Description ## Problem\* Step towards noir-lang#5249 ## Summary\* This PR adds a very simple fuzzer which can be used to find inputs to Noir programs which fail to execute. The motivation for this is to eventually allow `nargo test` to run on noir functions with arguments and automatically fuzz them in a similar fashion to `forge test`. It's currently very hit and miss on how quickly it can zero in on failing cases. For example, the program below is near-unfuzzable currently. ```rust fn main(x: u32, y: u32) { assert(x != y); } ``` ## Additional Context ## Documentation\* Check one: - [ ] No documentation needed. - [ ] Documentation included in this PR. - [x] **[For Experimental Features]** Documentation to be submitted in a separate PR. # PR Checklist\* - [x] I have tested the changes locally. - [x] I have formatted the changes with [Prettier](https://prettier.io/) and/or `cargo fmt` on default settings.
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[package] | ||
name = "noir_fuzzer" | ||
description = "A fuzzer for Noir programs" | ||
version.workspace = true | ||
authors.workspace = true | ||
edition.workspace = true | ||
rust-version.workspace = true | ||
license.workspace = true | ||
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html | ||
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[dependencies] | ||
acvm.workspace = true | ||
nargo.workspace = true | ||
noirc_artifacts.workspace = true | ||
noirc_abi.workspace = true | ||
proptest.workspace = true | ||
rand.workspace = true |
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//! This module has been adapted from Foundry's fuzzing implementation for the EVM. | ||
//! https://github.com/foundry-rs/foundry/blob/6a85dbaa62f1c305f31cab37781232913055ae28/crates/evm/evm/src/executors/fuzz/mod.rs#L40 | ||
//! | ||
//! Code is used under the MIT license. | ||
use acvm::{blackbox_solver::StubbedBlackBoxSolver, FieldElement}; | ||
use noirc_abi::InputMap; | ||
use proptest::test_runner::{TestCaseError, TestError, TestRunner}; | ||
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mod strategies; | ||
mod types; | ||
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use types::{CaseOutcome, CounterExampleOutcome, FuzzOutcome, FuzzTestResult}; | ||
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use noirc_artifacts::program::ProgramArtifact; | ||
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use nargo::ops::{execute_program, DefaultForeignCallExecutor}; | ||
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/// An executor for Noir programs which which provides fuzzing support using [`proptest`]. | ||
/// | ||
/// After instantiation, calling `fuzz` will proceed to hammer the program with | ||
/// inputs, until it finds a counterexample. The provided [`TestRunner`] contains all the | ||
/// configuration which can be overridden via [environment variables](proptest::test_runner::Config) | ||
pub struct FuzzedExecutor { | ||
/// The program to be fuzzed | ||
program: ProgramArtifact, | ||
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/// The fuzzer | ||
runner: TestRunner, | ||
} | ||
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impl FuzzedExecutor { | ||
/// Instantiates a fuzzed executor given a testrunner | ||
pub fn new(program: ProgramArtifact, runner: TestRunner) -> Self { | ||
Self { program, runner } | ||
} | ||
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/// Fuzzes the provided program. | ||
pub fn fuzz(&self) -> FuzzTestResult { | ||
let strategy = strategies::arb_input_map(&self.program.abi); | ||
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let run_result: Result<(), TestError<InputMap>> = | ||
self.runner.clone().run(&strategy, |input_map| { | ||
let fuzz_res = self.single_fuzz(input_map)?; | ||
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match fuzz_res { | ||
FuzzOutcome::Case(_) => Ok(()), | ||
FuzzOutcome::CounterExample(CounterExampleOutcome { | ||
exit_reason: status, | ||
.. | ||
}) => Err(TestCaseError::fail(status)), | ||
} | ||
}); | ||
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match run_result { | ||
Ok(()) => FuzzTestResult { success: true, reason: None, counterexample: None }, | ||
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Err(TestError::Abort(reason)) => FuzzTestResult { | ||
success: false, | ||
reason: Some(reason.to_string()), | ||
counterexample: None, | ||
}, | ||
Err(TestError::Fail(reason, counterexample)) => { | ||
let reason = reason.to_string(); | ||
let reason = if reason.is_empty() { None } else { Some(reason) }; | ||
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FuzzTestResult { success: false, reason, counterexample: Some(counterexample) } | ||
} | ||
} | ||
} | ||
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/// Granular and single-step function that runs only one fuzz and returns either a `CaseOutcome` | ||
/// or a `CounterExampleOutcome` | ||
pub fn single_fuzz(&self, input_map: InputMap) -> Result<FuzzOutcome, TestCaseError> { | ||
let initial_witness = self.program.abi.encode(&input_map, None).unwrap(); | ||
let result = execute_program( | ||
&self.program.bytecode, | ||
initial_witness, | ||
&StubbedBlackBoxSolver, | ||
&mut DefaultForeignCallExecutor::<FieldElement>::new(false, None), | ||
); | ||
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// TODO: Add handling for `vm.assume` equivalent | ||
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match result { | ||
Ok(_) => Ok(FuzzOutcome::Case(CaseOutcome { case: input_map })), | ||
Err(err) => Ok(FuzzOutcome::CounterExample(CounterExampleOutcome { | ||
exit_reason: err.to_string(), | ||
counterexample: input_map, | ||
})), | ||
} | ||
} | ||
} |
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use proptest::{ | ||
strategy::{NewTree, Strategy}, | ||
test_runner::TestRunner, | ||
}; | ||
use rand::Rng; | ||
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/// Strategy for signed ints (up to i128). | ||
/// The strategy combines 2 different strategies, each assigned a specific weight: | ||
/// 1. Generate purely random value in a range. This will first choose bit size uniformly (up `bits` | ||
/// param). Then generate a value for this bit size. | ||
/// 2. Generate a random value around the edges (+/- 3 around min, 0 and max possible value) | ||
#[derive(Debug)] | ||
pub struct IntStrategy { | ||
/// Bit size of int (e.g. 128) | ||
bits: usize, | ||
/// The weight for edge cases (+/- 3 around 0 and max possible value) | ||
edge_weight: usize, | ||
/// The weight for purely random values | ||
random_weight: usize, | ||
} | ||
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impl IntStrategy { | ||
/// Create a new strategy. | ||
/// # Arguments | ||
/// * `bits` - Size of int in bits | ||
pub fn new(bits: usize) -> Self { | ||
Self { bits, edge_weight: 10usize, random_weight: 50usize } | ||
} | ||
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fn generate_edge_tree(&self, runner: &mut TestRunner) -> NewTree<Self> { | ||
let rng = runner.rng(); | ||
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let offset = rng.gen_range(0..4); | ||
// Choose if we want values around min, -0, +0, or max | ||
let kind = rng.gen_range(0..4); | ||
let start = match kind { | ||
0 => self.type_min() + offset, | ||
1 => -offset - 1i128, | ||
2 => offset, | ||
3 => self.type_max() - offset, | ||
_ => unreachable!(), | ||
}; | ||
Ok(proptest::num::i128::BinarySearch::new(start)) | ||
} | ||
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fn generate_random_tree(&self, runner: &mut TestRunner) -> NewTree<Self> { | ||
let rng = runner.rng(); | ||
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let start: i128 = rng.gen_range(self.type_min()..=self.type_max()); | ||
Ok(proptest::num::i128::BinarySearch::new(start)) | ||
} | ||
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fn type_max(&self) -> i128 { | ||
if self.bits < 128 { | ||
(1i128 << (self.bits - 1)) - 1 | ||
} else { | ||
i128::MAX | ||
} | ||
} | ||
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fn type_min(&self) -> i128 { | ||
if self.bits < 128 { | ||
-(1i128 << (self.bits - 1)) | ||
} else { | ||
i128::MIN | ||
} | ||
} | ||
} | ||
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impl Strategy for IntStrategy { | ||
type Tree = proptest::num::i128::BinarySearch; | ||
type Value = i128; | ||
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fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> { | ||
let total_weight = self.random_weight + self.edge_weight; | ||
let bias = runner.rng().gen_range(0..total_weight); | ||
// randomly select one of 2 strategies | ||
match bias { | ||
x if x < self.edge_weight => self.generate_edge_tree(runner), | ||
_ => self.generate_random_tree(runner), | ||
} | ||
} | ||
} |
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