How much was lost in the Kizuna AI hack?

The Kizuna AI hack resulted in $135K in losses on August 29, 2023.

What attack technique was used in the Kizuna AI exploit?

The Kizuna AI 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.

Kizuna AI Hack

TOTAL LOST $135K

Low Aug 29, 2023 Rugpull

Affected Chain 2023 Incident surface

Recovered - No recovery reported

All-Time Rank #1424 By amount stolen

Protocol Type Exit Scam/Rugpull Target category

Incident Overview

Quick Summary

Kizuna AI token project rugpulled by deployer, removing liquidity worth 134,863 USD from UniSwap pool in multiple transactions.

Details of the Exploit

Kizuna AI (KIZUNA) is an ERC20 token trading on UniSwap. On August 29, 2023, the deployer performed an exit scam by removing liquidity from the LP pool in multiple small transactions. The stolen funds, equivalent to 78.5 ETH or 134,863 USD, were distributed to multiple EOA addresses in small amounts of ETH.

Block Data Reference

Deployer Address:

https://etherscan.io/address/0x1CAFE95d…d54763

Liquidity Removal Transaction:

https://etherscan.io/tx/0xbf29cf83…491c81

Funds Transfer Transaction:

https://etherscan.io/tx/0x7b5aefff…396ad5

Incident Report

Protocol / Project Kizuna AI

Date of Incident August 29, 2023

Attack Technique Rugpull

Classification Token

Primary Source View Post-Mortem

Protocol Information

Protocol Type Exit Scam/Rugpull

Affected Token KIZUNA

Official Website kizunatoken.io/

Protocol Twitter/X @kizunatoken

Team Anonymous

Source Code Verified On-Chain

Market Context at Time of Hack

Token Categories

AI & Big Data Memes Ethereum Ecosystem Generative AI AI Memes

Protocol Links

GitHub

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 smart contracts and deploy a custom exploit contract

Protocol Analysis Identification of the exploitable vulnerability in Kizuna AI's contract logic - root cause: token

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 Kizuna AI, these are the critical security checks that could have prevented this incident (August 2023).

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/DeDotFiSecurity/status/1696823573678051683

Threat Assessment

Severity Level

LOW

Attack Vector Rugpull
Root Cause Token
Year 2023

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