Back Running Definition

Back running is a trade placed right after a large onchain transaction to capture the price effect or arbitrage that the first trade created. It is classed as a form of MEV, where searchers or validators reorder or insert transactions to extract value. 

How it works in practice

Back-runners watch the public mempool for high-value swaps. When a big order moves an automated market maker’s pool, it nudges prices away from the broader market. A bot then submits a follow-up transaction immediately after the target trade to harvest the leftover price gap, often by buying on the moved venue and selling where prices have not yet adjusted. 

Why it happens

AMMs adjust prices based on the ratio of tokens in a pool. A large buy pushes the bought token up in that pool and the sold token down, which briefly creates an arbitrage window between venues. Back running tries to close that window and keep the profit. 

Who performs it

Specialized MEV searchers and, in some cases, block builders or validators, scan pending transactions and sequence their own orders right after profitable ones. Their edge comes from tooling that monitors the mempool and submits transactions with fees and parameters tuned for priority. 

Relationship to front running and sandwich attacks

Back running on its own does not worsen the price a user was quoted. It mainly means the user missed the chance to capture the arbitrage their trade created. When a front-run is added before the victim’s trade and a back-run is added after, the result is a sandwich attack that pushes the victim into worse pricing. 

Typical sequence

1. A large swap is detected in the mempool.
2. The swap executes and moves the AMM price.
3. The back-runner immediately sends a trade that exploits the price gap across venues.
4. The gap closes and the bot keeps the spread. 

Effects on users and markets

For most retail traders, back running represents opportunity cost rather than direct loss, since their trade still clears at the quoted terms. Advanced traders may view it as profit left on the table. At scale, bots tend to monopolize these opportunities, which shifts that profit from users to searchers. 

Ways to reduce exposure

• Trade through systems that hide or batch order flow so it is harder to target single transactions, for example batch auctions with uniform clearing prices or delegated execution. 
• Use MEV-aware routing that shares or rebates back-running revenue to the user rather than to third-party bots. 
• In general MEV contexts, avoid very large, slippage-sensitive trades in public mempools and consider settings that limit how much price movement a trade will accept.

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