Free/Fuzzing Audits:Hooks-Uniswap V4-Foundry
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Update: january 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 HOME Disclaimer
Updated on: february 01,2025 Quora-