How much was lost in the Yearn Machine Learning hack?

The Yearn Machine Learning hack resulted in $100K in losses on January 21, 2021.

What attack technique was used in the Yearn Machine Learning exploit?

The Yearn Machine Learning exploit utilized Rugpull. Understanding this attack vector is critical for smart contract auditors and developers building secure DeFi protocols.

How can developers prevent similar smart contract attacks?

Preventing smart contract hacks requires a combination of secure coding practices, thorough security audits, and understanding common attack vectors like reentrancy, oracle manipulation, and access control flaws. Smart Contract Hacking (SCH) provides hands-on training to help developers and auditors identify and prevent vulnerabilities before deployment.

Yearn Machine Learning Hack

TOTAL LOST $100K

Low Jan 21, 2021 Rugpull ethereum

Affected Chain ethereum Incident surface

Recovered - No recovery reported

All-Time Rank #1523 By amount stolen

Protocol Type Exit Scam/Rugpull Target category

Incident Overview

The project was holding a presale event using proxy:

shorturl.at/nrtwT

The liquidity wasn't added.

The stolen Ether (90.30 ETH) was deposited into the Binance exchange:

https://bloxy.info/txs/transfers_from/0x432cab60…887c69?currency_id=1

Incident Report

Protocol / Project Yearn Machine Learning

Date of Incident January 21, 2021

Affected Chain(s) ethereum

Attack Technique Rugpull

Classification Yield Aggregator

Primary Source View Post-Mortem

Protocol Information

Protocol Type Exit Scam/Rugpull

Affected Token YML

Official Website yearnml.finance/

Protocol Twitter/X @yearnmlfinance

What the Attacker Needed to Succeed

Understanding the prerequisites for this type of attack helps auditors identify protocols that are most at risk and helps developers build better defenses.

Technical Knowledge Deep understanding of rugpull and Solidity and EVM internals

Capital Required Seed capital to cover gas and initial position setup

On-Chain Access Ability to interact with ethereum smart contracts and deploy a custom exploit contract

Protocol Analysis Identification of the exploitable vulnerability in Yearn Machine Learning's contract logic - root cause: yield aggregator

Execution Speed Precise transaction ordering and timing to exploit the vulnerability within a single atomic block

Obfuscation Plan A strategy to launder and move stolen funds - typically through mixers, cross-chain bridges, or decentralized DEX swaps to resist tracing

What Auditors Should Check

Could this have been caught in audit? Hard to catch — private key / OpSec failures are outside smart contract audit scope

If you're auditing a protocol with similar architecture to Yearn Machine Learning, these are the critical security checks that could have prevented this incident (January 2021).

Verify all logic paths related to Rugpull are guarded by proper access controls and input validation
Review privileged functions (owner, admin, governance) for potential abuse vectors - centralization risks should be documented and bounded with timelocks or multi-sigs

Master these auditing techniques with hands-on labs and real exploit scenarios in the Smart Contract Hacking course.

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Sources & References

01
Source 1 https://twitter.com/SolidityFinance/status/1351222770898788360
02
Web Archive https://web.archive.org/web/20210111093558/https://yearnml.finance/

Threat Assessment

Severity Level

LOW

Attack Vector Rugpull
Root Cause Yield Aggregator
Year 2021

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