NFT MEV

The automated detection and purchase of undervalued NFTs listed below their market price before other traders can react. Bots monitor new listings across marketplaces and execute purchases in the same block.

• Example: Buying a Bored Ape listed for 10 ETH when the floor is 50 ETH.
• Tools: Use custom scripts or services that provide real-time listing feeds and high-speed transaction submission.

Placing bids on multiple NFTs within a collection to artificially inflate the perceived floor price, then selling one's own holdings into the inflated market. This creates a false signal of demand.

• Mechanism: A bot places bids just below the current floor on many assets, raising the collection's listed 'top bid'.
• Risk: Requires significant capital and carries the risk of having bids filled, acquiring unwanted NFTs.

Profiting from price discrepancies between the cost of a bundle of NFTs and the sum value of its individual components. Bots identify bundles listed for less than their constituent parts' market value, purchase the bundle, and immediately sell the items separately.

• Market Focus: Common on marketplaces like Blur that support bundle listings.
• Complexity: Requires rapid assessment of individual NFT valuations and gas-efficient batch transactions.

A searcher provides instant liquidity to an NFT lending pool only when a specific, profitable loan is about to be liquidated. The searcher repays the underwater loan, acquires the NFT collateral at a discount, and immediately sells it for profit.

• Platforms: Occurs on NFTfi, Blend, and other peer-to-peer lending protocols.
• Execution: Requires monitoring loan health and being the first to supply capital for the liquidation.

Exploiting the public mempool by seeing a profitable NFT trade (like a large bid or ask) and submitting a transaction with a higher gas fee to execute a similar trade first. This preempts the original trader's profit opportunity.

• Context: More relevant in off-chain order book models or when transactions are broadcast publicly.
• Mitigation: Traders use private transaction relays or Flashbots Protect to avoid exposure.

Purchasing NFTs from a newly minted collection before the metadata is revealed, based on statistical analysis or insider knowledge predicting which token IDs will have rare traits. After the reveal, the rare NFTs are sold at a premium.

• Data Analysis: Involves analyzing minting patterns, rarity models, or contract interactions.
• Timing: The critical window is between mint completion and the official metadata reveal by the project.

A dominant NFT MEV strategy where a searcher identifies an undervalued NFT based on its rarity traits before a marketplace listing updates. The searcher's bot front-runs the listing transaction to purchase the NFT at a lower price, then immediately resells it for profit. This exploits the latency between an NFT's trait reveal and its price reflection on platforms like OpenSea or Blur.

• Mechanism: Bots monitor mint transactions and rarity calculations.
• Impact: Creates a toxic environment for organic collectors and increases gas wars.

Exploiting price discrepancies for the same NFT across different marketplaces. A searcher buys an NFT on Marketplace A (e.g., a listing with a stale, low price) and simultaneously places a sell order on Marketplace B (e.g., with a higher floor price), profiting from the spread. This relies on atomic transactions (bundles) to eliminate execution risk.

• Tools: Use Flashbots-style bundles to ensure the arbitrage trade either succeeds completely or fails, preventing partial execution.
• Example: Profiting from differences between OpenSea, Blur, and LooksRare floor prices.

Manipulating the NFT order book for profit. This includes bid sniping—canceling a bid just before it would be accepted by a seller's transaction—and order book front-running, where a searcher sees a large incoming bid and places their own bid just ahead of it to acquire the asset first.

• Impact: Undermines trust in bidding systems and can lead to failed transactions for legitimate users.
• Protocol Response: Marketplaces like Blur have implemented gas-gated listings and bids to mitigate this.

An emerging, protocol-level solution inspired by Jito on Solana, which captures and redistributes MEV. For NFTs, this could involve a dedicated auction block space for searchers to bid on the right to reorder transactions within an NFT-centric block. The proceeds from these auctions are then distributed to NFT holders or stakers as a dividend.

• Concept: Formalizes and democratizes the value extracted from NFT transaction ordering.
• Potential: Turns a parasitic activity into a protocol revenue stream and holder incentive.

NFT MEV forces protocol designers to build with ordering fairness in mind. Key design responses include:

• Commit-Reveal Schemes: Hiding transaction details (like trait data) until a later block.
• Fair Sequencing Services (FSS): Using a decentralized sequencer to order transactions neutrally.
• Protected Listings: Time-delayed or stealth listings to prevent front-running.
• Gas Optimization: Designing mints and trades to minimize opportunities for predatory gas bidding.

The broader negative effects of rampant NFT MEV on the ecosystem:

• User Experience Degradation: Failed transactions and gas price inflation deter casual users.
• Centralization Pressure: Only well-funded searchers with sophisticated infrastructure can compete, pushing out smaller players.
• Data Integrity Issues: MEV distorts on-chain data, making true floor prices and rarity scores harder to ascertain.
• Innovation Tax: Developers must spend significant resources on MEV mitigation instead of core features.

An attack where a validator or searcher intentionally prevents a competing bid from being included in a block to win an NFT auction. This can be done by paying a high fee to have their own transaction included while the competitor's is excluded, or by sandwiching the bid transaction between others to invalidate it. This undermines the fairness of English auctions and blind auctions.

MEV bots can exploit the atomic composability of smart contracts to purchase an NFT and sell it in the same block, but route the sale through a marketplace with zero or lower royalty fees. This extracts value that would have gone to the original creator as royalties, violating the intended economic model of the NFT. This attack vector has driven the shift towards enforced creator fees at the protocol level.

During high-demand NFT mints, searchers compete to have their mint transactions processed first to secure rare traits or a lower mint number. They submit transactions with exorbitant priority gas fees, creating a gas war that drives up network costs for all participants and often results in failed transactions for regular users due to gas estimation errors and reverts.

The public nature of the mempool exposes all pending NFT transaction intentions. A user's wallet address, target NFT, and bid amount are visible before confirmation, making them a target for MEV attacks. This lack of transaction privacy forces users into strategies like using private RPCs, Flashbots Protect, or commit-reveal schemes to shield their intent.

The profitability of NFT MEV creates incentives for validators (or block builders in Proposer-Builder Separation models) to centralize order flow and extract maximum value. This can lead to centralized block building and potential long-term censorship risks, where certain NFT collections or marketplace transactions are systematically excluded from blocks for competitive advantage.