How Does AI Identify MEV Patterns Like Sandwich Attacks & Front-Running?

Blockchain networks are transparent and trust-minimized, but this transparency has also led to the development of complex trading techniques that benefit at the cost of common users. Among the most popular trends in this area is the concept of Maximal Extractable Value (MEV). It has become increasingly important to understand how AI recognizes MEV patterns such as sandwich attacks and front-running bots, especially in the context of growing decentralized finance (DeFi) transactions.

AI algorithms process blockchain data to identify irregularities in the sequence and timing of transactions that are indicative of MEV activity. This article describes how AI is used for this purpose and why it is important to the overall cryptocurrency environment.

What Is MEV and Why Does It Matter?

MEV stands for Maximum Extractable Value. It is the maximum value that can be extracted from ordering transactions in a block. MEV is extracted beyond the standard block reward and transaction fees. Although MEV can be harmless at times (such as in the case of arbitrage, which enhances price efficiency), it can also cause harmful activities.

Common Activities Associated with MEV

• Sandwich attacks on user swaps

• Front-running bots that take advantage of the transparency of transactions

• Back-running to capitalize on large transactions

• Liquidation sniping on lending protocols

Some of these activities are commonly linked to market manipulation, especially when they consistently hurt retail traders or when they affect fair price discovery.

Why AI Is Applied to Find MEV Patterns

The data on the blockchain is public, but it is also enormous, very fast, and very complex. It is not possible to manually analyze it to keep up with thousands of blocks and millions of transactions.

• AI is applied because it has the ability to:

• Process enormous amounts of data on the blockchain in real-time

• Detect patterns that are not easily recognizable by humans

• Adjust to changing bot tactics

• Minimize false positives compared to rule-based systems

How AI Detects MEV Patterns: A Step-by-Step Explanation

AI models use a systematic pipeline to detect MEV patterns. Here is a simplified explanation of the process.

Main Steps in AI-Based MEV Pattern Detection

• Data import from mempools, blocks, and smart contracts

• Feature extraction (timestamps, gas prices, slippage, ordering)

• Pattern detection via machine learning algorithms

• Anomaly detection for suspicious behavior

• Categorization of recognized MEV tactics

Learning Sandwich Attacks via AI Analysis

A sandwich attack consists of two transactions surrounding a victim’s trade:

1. A front-running transaction to shift the price

2. The victim’s transaction

3. A back-running transaction to reap the profit

How AI Models Detect Sandwich Attacks

AI models search for:

• Correlated transaction pairs from the same address

• Irregular price action just before and after a trade

• Consistent profit-taking patterns per block

• Tight temporal relationships between transactions

AI models learn these patterns to differentiate between accidental ordering and malicious exploitation.

How AI Identifies Front-Running Bots

Front-running bots monitor the mempool to detect pending transactions that are likely to move prices. They then submit competing transactions with higher gas fees.

Signals AI Looks For

• Transactions consistently paying above-average gas fees

• Repeated targeting of large swaps or liquidations

• Very short time gaps between victim and bot transactions

• Predictable profit margins regardless of market direction

Over time, AI systems build behavioral profiles that help separate bots from normal traders.

Machine Learning Techniques Used in MEV Detection

Different AI techniques are applied depending on the problem being solved.

Common AI Approaches

• Supervised learning: Trained on labeled examples of known MEV attacks

• Unsupervised learning: Finds hidden clusters and anomalies without labels

• Graph analysis: Maps relationships between wallets and contracts

• Time-series models: Detects repeated patterns across blocks

Each method contributes to a more complete understanding of MEV behavior.

Data Sources Used by AI Systems

AI models rely on diverse on-chain and off-chain data.

Typical Data Inputs

• Transaction metadata (gas, nonce, timestamps)

• Smart contract call traces

• Token price movements across DEXs

• Mempool transaction sequences

• Historical block data

Combining these sources allows AI to detect coordinated strategies rather than isolated events.

Short Comparison Table: Traditional Analysis vs AI-Based Detection