Free/Fuzzing Audits:Hooks-Uniswap V4-Foundry



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Update: december 30-  2025


 Hooks Uniswap V4:Auditing-Fuzzing
FOUNDRY




long video : duration: 50:42 mins
Uniswap v4 Hooks & Fuzzing Audit (Status: 12/22/25)
Hooks Architecture in v4: Hooks allow for custom logic in pools. The risk lies in manipulating the PoolManager. If security fails, an attacker can divert fees or lock up liquidity.
Fuzzing with Foundry: The Foundry tool is the standard for unit and fuzzing tests. It generates thousands of random inputs to test functions like beforeSwap, exposing rounding errors.
Invariants in Foundry: In Foundry, invariant testing (invariant_) is vital for hooks. It ensures that, no matter the sequence of transactions, certain rules (such as the pool balance) are never broken.
Echidna for Deep Fuzzing: While Foundry focuses on isolated transactions, Echidna explores complex contract states through grammatical properties, ideal for finding multi-step logic bugs.
Access Control Flaws: The video exemplifies how flaws in the owner() function allow attackers to hijack contracts. In hooks, this can lead to malicious alteration of fee parameters in real time.
Pause Vulnerability: The speaker demonstrates that hooks with poorly protected pause functionality can be used to freeze user liquidity on Uniswap v4.
Transient Accounting (EIP-1153): In December 2025, hook audits focus on the correct use of transient storage. Foundry should be used to ensure that the state is cleared after each swap.
Static vs. Manual Analysis: The video reinforces that Slither helps, but manual auditing with markings (Audit: Issue) is indispensable for understanding the economic integration between the hook and the pool.
Fee Fuzzing: Echidna is superior for testing whether the hook's fee distribution logic can be exploited via price manipulation (Sandwich Attacks).
Conclusion for Developers: Security in 2025 requires the use of Foundry for full code coverage and tools like Echidna to validate the protocol's economic resilience.



Here's a list of 10 specific classes of flaws that Foundry can detect in smart contracts (including hook logic like in Uniswap v4) when you write proper tests — each item with a helpful mitigation/detection link focused on Foundry/fuzz/invariant testing:
1-Violation of Liquidity Invariants
What it detects: situations where the expected sum of reserves is broken after arbitrary operations. 
Docs.foundry-Invariant Testing (Forge)

2-Inconsistencies in Token Storage
What it detects:Situations where the sum of users' balances does not match the totalSupply, or where tokens are improperly created or destroyed without correctly reflecting the overall state of the contract.
Zepelin-Secure development of smart contracts
3-Errors in Price Calculations
What it detects: Incorrect price calculation based on external inputs.
Docs.foundry-Cobre como testar funções matemáticas com inputs aleatórios.

4-Logical Overflow/Underflow (Property Violations)
What it detects: Mathematical operations breaking expected properties.
Docs.foundry-Invariant Testing (Para propriedades de estado)
5-Incorrect State Assumptions
What it detects: Logic depending on invalid or uninitialized states (e.g., balance ≠ 0 initially)
Fuzzing Testing Network Protocols for Humans
6-Unauthorized Access / Permission Failure
What it detects:Administrative functions accessed by unauthorized users
Inspector-forge
7-Dangerous Call Sequences (Stateful Bugs)
What it detects:Bugs become visible after specific sequences of calls.
Reentrancy-chamadas reentrantes para uma função.
8-Violation of Hook Properties V4
What it detects:Breaks price invariants - The asset price after transactions does not accurately reflect the supply/demand relationship.
Violates liquidity properties - The total value of the pool is not preserved or liquidity becomes inconsistent.
Creates unintentional arbitrage - Allows for risk-free value extraction.
Uniswap V4 Hooks Audit: Risks, Exploits, and Secure Implementation
9-Calculating Fees with Incorrect Input
What it detects: Incorrectly applied rates under extreme inputs.
Smart Contract Security
10-Pool State Manipulation (Simulated Front-Running)
What it detects: Inconsistent states when many operations are performed sequentially.
Market Manipulation vs. Oracle Exploits





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Updated on: december 30,2025