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Detail Analysis

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Date:

Status:

Count:

Contributor:

April 2, 2022

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Verified

1

zerofriction.io

Loss Amount:

15,600,000

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Recovered Amount:

-

Currency:

Dollars

KYC By:

Audit By:

None

DefiSafety

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Website:

Twitter:

Discord:

No data

Telegram:

No data

Medium:

No data

Github:

No data

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Key Indicators

Platform:

Type:

Category:

Method:

Ethereum

Protocol

Lending

Contract Vulnerabilities

Extended Method:

Price oracle manipulation

Data Sources:

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The company wrote on Twitter that a hacker managed to manipulate its money market, Anchor, and increased the price of INV via Sushiswap – an open-source ecosystem of DeFi tools.

INV is an Ethereum token that powers Inverse Finance, a decentralized platform used for lending, borrowing, and creating synthetic assets.

The manipulation caused a sharp increase in the price of INV, allowing the hacker to borrow $15.6 million in the DOLA, ETH, WBTC and YFI cryptocurrencies against it.

“The manipulation was not a flash loan attack and was unrelated to Inverse’s smart contract or front end code. All future borrows on Anchor are temporarily paused,” the company said initially.

In a blog post on 4/4, Inverse Finance confirmed PeckShield’s analysis which found that the attacker withdrew 901 ETH from Tornado Cash and made a series of trades primarily in the INV/DOLA pool on SushiSwap on Saturday morning. Tornado Cash is a cryptocurrency mixer that allows people to hide the origin of funds.

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DISCLAIMER: While Zero Friction LLC has used the best efforts in aggregating and maintaining this database, Zero Friction LLC makes no representations or warranties with respect to the accuracy or completeness, and specifically disclaim any implied warranties of merchantability or fitness for any particular purpose. 

Under no circumstances, shall Zero Friction LLC be liable for any loss of profit or funds, any regulatory or governmental penalties, any legal costs, or any other commercial and non-commercial damages, including but not limited to special, incidental, consequential, or other damages from any or all usage of the dataset or information derived from our database.