ConsenSysDiligence-Fuzzing
Free Software:Apache 2.0 license
You may use, copy, modify, merge, publish, distribute, sublicense, and sell the software.
The only obligation is to retain the copyright notice and the MIT license on any significant copy of the software.
This is an automated fuzzing tool for Solidity smart contracts. Its
main function is to discover bugs, logical flaws, and unexpected
vulnerabilities by generating millions of random or semi-directed
contract executions.
For Uniswap V4 Hooks developers:
The tool is especially valuable for the following 20 rSSSSSSSSS
- Free Software: Apache 2.0 license
You may use, copy, modify, merge, publish, distribute, sublicense, and sell the software.
The only obligation is to retain the copyright notice and the MIT license on any significant copy of the software.
This
is an automated fuzzing tool for Solidity smart contracts. Its main
function is to discover bugs, logical flaws, and unexpected
vulnerabilities by generating millions of random or semi-directed
contract executions.
For Uniswap V4 Hooks developers:
The tool is especially valuable for the following 20 reasons:
1-Automated tests with random inputs:
- Test
the hooks by subjecting them to a massive flow of random inputs and
pool operations. The main goal is to detect unexpected behaviors, such
as invariant breaks, race conditions, or unexpected rollbacks that
could exploit vulnerabilities. The tests simulate complex contract
states and unpredictable call sequences that conventional unit tests
don't cover. The focus is on ensuring that the hooks' custom logic
remains secure and functional under extreme or malicious conditions,
identifying potential logic flaws or reentrancy before deployment to
production.
2-Discovery of Logical Faults:
Focuses
on identifying complex logical flaws in smart contracts that manual or
unit tests would hardly detect. The system automatically generates
thousands of random transaction sequences and contract states to expose
race conditions, reentrancy vulnerabilities, and invariant breaks. By
simulating concurrent executions and unpredictable calling patterns,
fuzzing reveals subtle order dependencies and inconsistent states that
can be maliciously exploited. The tool monitors contract behavior under
extreme conditions, identifying problems that arise only in specific
combinations of parameters and execution flows, providing a critical
security layer for DeFi protocols like Uniswap V4.
3-Extensive Code Coverage:
Fuzzing
Technique: Designed to execute and validate a wide range of code paths
within a smart contract. By generating a large variety of random inputs
and transaction sequences, the tool forces the execution of conditional
branches, edge case functions, and state logic that are rarely
triggered in manual or unit tests. The goal is to reach even the most
obscure and unexpected code paths that developers may have overlooked,
ensuring that unexpected behaviors, unhandled exceptions, or validation
failures in rare scenarios are exposed before deployment.
4-Invariant Verification:
The
fuzzing technique specializes in testing and validating the fundamental
logical properties of a smart contract, known as invariants. The
process works by subjecting the contract to a massive flow of random
transactions and states, while continuously checking whether essential
rules—such as "the total contract balance must equal the sum of the
users' balances" or "a critical function should never be called from an
unauthorized address"—remain true. Fuzzing actively attempts to violate
these properties, executing complex sequences of operations to discover
edge case scenarios where an invariant can be broken, thus exposing
critical business logic vulnerabilities that conventional tests fail to
capture.
5- Decimal Precision Flaw Detection
This
approach focuses specifically on identifying vulnerabilities related to
numerical and arithmetic precision in hooks that operate with tokens of
different decimal places. The system generates thousands of random
scenarios involving calculations of exchange rates, liquidity values,
and swap amounts between tokens with varying decimal places (such as
ETH 18-decimals and USDC 6-decimals). The goal is to detect fund losses
due to incorrect rounding, overflow/underflow errors in mathematical
operations, and inconsistencies in calculations that rely on decimal
precision. The tool validates whether the calculation results maintain
financial integrity under different combinations of values and
decimal places, exposing subtle flaws that could be exploited for fund
draining or price manipulation.
6- Integration with Echidna and MythX:
Allows
combined use with established security tools such as Echidna and MythX,
creating a layered testing approach. The integration enables Echidna's
property tests and MythX's static analysis to be incorporated into the
fuzzing flow, significantly expanding audit coverage. The system
leverages the strengths of each tool – Echidna's property-based test
case generation and MythX's automated vulnerability detection – to
create a more robust test suite. This allows for the identification of
complex vulnerabilities that could go unnoticed using only a single
methodology, providing a more comprehensive security check for Uniswap
V4 hooks.
7- Identifying Failed Assertions:- This
fuzzing technique is designed to expose points in the code where
assertions, require, revert, or validation checks fail when confronted
with unexpected inputs or states. The system bombards the contract
hooks with a wide range of random values, invalid parameters, and
anomalous transaction sequences, specifically monitoring where these
security conditions are violated or where the contract unintentionally
reverts. The goal is to reveal edge case scenarios where the validation
logic is insufficient or incorrect, allowing developers to strengthen
the contract's defenses against malformed or malicious inputs before
deployment.
8- Exploiting Extreme Cases: - This
fuzzing technique tests the robustness of Uniswap V4 hooks by
systematically generating extreme and structurally unusual values for
all input parameters. The system focuses on absolute limits such as
token amounts equal to zero, maximum possible values
(type(uint256).max), special addresses (such as zero or pre-compiled
contract addresses), and unforeseen combinations of pool states. For
financial hooks, this includes simulating extreme liquidity imbalances,
negligible or astronomical exchange rates, and interactions with tokens
with different decimal places in their limit values. The goal is to
force code execution through exceptionally rare paths that manual and
unit tests almost never cover. The tool verifies that the contract
protections (require, revert, assert) and its internal mathematical
logic remain valid under this extreme stress. This process is crucial
for exposing vulnerabilities that only emerge under specific
conditions, such as rounding errors with minimum values,
overflows/underflows in operations with maximum values, or
authorization logic that fails with special addresses. Actively
exploiting these cases ensures that the hook maintains safe and
predictable behavior, reverting in a controlled manner when necessary,
even in the face of the most adversarial and unexpected inputs.
9- Detailed Report Generation: - This
fuzzing functionality transcends mere flaw detection, focusing on
producing accurate and actionable diagnoses. When a vulnerability is
discovered, the system generates a forensic report that not only
indicates the existence of a problem but also documents the exact
sequence of transactions, the contract state, and the input parameters
that led to the failure. Each report details the complete execution
path, including the values of all state variables at the time of the
error, the gas cost of each operation, and the specific line of source
code where the exception or unexpected behavior was observed. For
Uniswap V4 hooks, this might mean showing the exact combination of swap
amounts, pool liquidity states, and hook configurations that broke an
invariant or caused an unexpected rollback. The tool categorizes flaws
by severity and type, correlating them with known vulnerability
patterns (such as reentrancy or overflow). These reports are structured
in machine- and human-readable formats, facilitating integration with
CI/CD pipelines and allowing developers to reproduce the failure
scenario locally with absolute accuracy. The ability to trace the root
cause saves hours of manual debugging and is crucial for transforming
fuzzing findings into robust and verified code fixes.
10- Based on SMT Solvers:
- This
advanced fuzzing approach integrates formal verification techniques
with dynamic testing, using Satisfactory Model Theory (SMT) solvers to
generate intelligent, symbolically driven test inputs. Unlike purely
random fuzzing, the system models contract conditions and hook
execution paths as a set of constraints and logical formulas. The SMT
solver analyzes these formulas to generate concrete inputs that
systematically explore deep and complex contract states, especially
focusing on scenarios that satisfy specific edge conditions (e.g.,
"find values where the liquidity balance is at a minimum, but a swap
operation is requested"). For Uniswap V4 hooks, this means discovering
exact sequences of operations that may violate critical properties,
such as the preservation of a pool's k-invariant or a hook's
authorization conditions. The tool uses symbolic execution to explore
multiple code paths simultaneously, automatically identifying parameter
combinations that lead to rare or dangerous code branches. This
methodology is particularly effective for finding vulnerabilities that
rely on complex mathematical relationships between state variables,
such as precision flaws in multi-token calculations or race conditions
arising from specific interactions between functions. By guiding test
generation with formal mathematical reasoning, SMT-based fuzzing
achieves deeper and more meaningful code coverage than purely random
methods, exposing subtle logical flaws that would otherwise remain
hidden until production.
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