Pickle Finance Hack

TOTAL LOST $19.7M
High Other ethereum
Chain ethereum Primary network
Recovered - No recovery reported
Loss Rank #254 All-time
Audit Firms 1 Before incident

What happened

An attacker exploited Pickle Finance's ControllerV4 by deploying malicious smart contracts, leading to a loss of 19M DAI.

The attacker deployed two smart contracts with malicious logic, which were used to retrieve the amount available to withdraw from StrategyCmpdDaiV2. The ControllerV4.swapExactJarForJar() function was invoked, which doesn't check the Jars and calls them, withdrawing from StrategyCmpDAIV2. This transferred 19M DAI to pDAI.

The attacker then called pDAI.earn() three times, leading to a Compound deposit and the contract receiving cDAI. Three more smart contracts with malicious logic were deployed and the ControllerV4.swapExactJarForJar() function was invoked again, leading to the withdrawal of cDAI and transferring them to ControllerV4. The funds were then transferred to the attacker's smart contract, which redeemed cDAI for DAI from Compound and transferred DAI to the attacker's EOA.

The attacker's address:

https://etherscan.io/address/0x75aa9550…abe0f9

https://etherscan.io/address/0x02c83645…111ee6

The transaction behind the attack:

https://etherscan.io/tx/0xe72d4e7b…1087b0

Case & protocol details

Classification Yield Aggregator
Protocol Type Yield Aggregator
Affected asset / contract PICKLE
Official Website www.pickle.finance/
Protocol Twitter/X @picklefinance
Team Anonymous
Source Code Unverified

Market Context at Time of Hack

Token Categories
DeFi DAO Ethereum Ecosystem Yield Farming Yield Aggregator Yearn Partnerships Alameda Research Portfolio Arbitrum Ecosystem

Audit assessment

Review priorities based on the documented failure pattern in Pickle Finance (November 2020).

Critical checks

  • Verify every sensitive logic path is guarded by appropriate access controls and input validation

Review history

A prior review is not a guarantee of safety, particularly when code changes after the reviewed version.

Evidence & learning

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