[Paper Review] The Blockchain Imitation Game

Kaihua Qin, Stefanos Chaliasos+4 more|arXiv (Cornell University)|Mar 31, 2023

Blockchain Technology Applications and Security4 citations

TL;DR

This paper introduces Ape, a generalized imitation attack methodology for EVM-based blockchains that uses dynamic program analysis to automatically synthesize adversarial smart contracts, enabling real-time front-running of profitable transactions without prior knowledge of the victim's logic. Over one year, Ape could have extracted $148.96M on Ethereum and $42.70M on BSC, while also successfully imitating 13 and 22 known DeFi attacks on each chain, demonstrating its potential as both a malicious exploit and a defensive whitehat intrusion prevention mechanism.

ABSTRACT

The use of blockchains for automated and adversarial trading has become commonplace. However, due to the transparent nature of blockchains, an adversary is able to observe any pending, not-yet-mined transactions, along with their execution logic. This transparency further enables a new type of adversary, which copies and front-runs profitable pending transactions in real-time, yielding significant financial gains. Shedding light on such "copy-paste" malpractice, this paper introduces the Blockchain Imitation Game and proposes a generalized imitation attack methodology called Ape. Leveraging dynamic program analysis techniques, Ape supports the automatic synthesis of adversarial smart contracts. Over a timeframe of one year (1st of August, 2021 to 31st of July, 2022), Ape could have yielded 148.96M USD in profit on Ethereum, and 42.70M USD on BNB Smart Chain (BSC). Not only as a malicious attack, we further show the potential of transaction and contract imitation as a defensive strategy. Within one year, we find that Ape could have successfully imitated 13 and 22 known Decentralized Finance (DeFi) attacks on Ethereum and BSC, respectively. Our findings suggest that blockchain validators can imitate attacks in real-time to prevent intrusions in DeFi.

Motivation & Objective

• To investigate the feasibility of generalized, real-time smart contract imitation attacks on EVM-based blockchains without prior knowledge of victim transaction logic or contract behavior.
• To design and implement an automated system that synthesizes adversarial smart contracts to bypass common protections like authentication and obfuscation.
• To evaluate the financial impact of such imitation attacks on major DeFi platforms like Ethereum and BSC over a real-world timeframe.
• To explore the dual-use potential of imitation as a defensive mechanism—acting as a whitehat intrusion prevention system by mimicking attacks and returning stolen funds.

Proposed method

• Ape employs dynamic taint analysis to trace data flows from pending transactions to identify critical execution paths and contract dependencies.
• It uses program synthesis to automatically generate new adversarial smart contracts that replicate the logic of victim transactions while bypassing access controls like authentication.
• The framework leverages EVM-level instrumentation to enable real-time analysis and execution, overcoming the time constraints of traditional symbolic execution.
• Ape synthesizes and deploys new contracts on-the-fly to replace protected or obfuscated victim contracts, ensuring correct invocation and revenue capture.
• The system operates without requiring access to the victim’s ABI or source code, enabling it to target closed-source, obfuscated, or authenticated smart contracts.
• It supports end-to-end imitation of complex DeFi workflows, including flash loan attacks and AMM arbitrage, by analyzing dynamic control-flow graphs (DCFGs) of transaction execution.

Experimental results

Research questions

• RQ1 Can a generalized, real-time imitation attack be constructed on EVM-based blockchains without prior knowledge of the victim’s transaction logic or contract source code?
• RQ2 To what extent can dynamic program analysis techniques like taint analysis and program synthesis enable automated adversarial contract generation for profitable front-running?
• RQ3 What financial gains can such an imitation attack achieve on production blockchains like Ethereum and BSC over a one-year period?
• RQ4 Can the same imitation mechanism be repurposed as a defensive whitehat intrusion prevention system to detect and neutralize active DeFi exploits?
• RQ5 How effective are common protection mechanisms like authentication and obfuscation against such a generalized imitation attack?

Key findings

• Over a one-year period (August 2021–July 2022), Ape could have extracted $148.96 million in profit on Ethereum and $42.70 million on BNB Smart Chain (BSC).
• Ape successfully imitated 13 known DeFi attacks on Ethereum and 22 on BSC, demonstrating its capability to replicate complex, composable attack patterns.
• The tool operates in real time using dynamic analysis, overcoming the performance limitations of symbolic execution and enabling practical front-running despite short time windows.
• Ape can bypass common protection mechanisms such as authentication and basic obfuscation by synthesizing new contracts that replicate the victim’s logic without requiring source code or ABI access.
• The study reveals that blockchain validators could use Ape-like mechanisms as a defensive whitehat strategy to detect and neutralize attacks by mimicking them and returning stolen funds to victims.
• While obfuscation techniques can slow down Ape, they are not foolproof and increase gas costs, suggesting they are insufficient as standalone defenses against generalized imitation attacks.