Sandwich Attacks on DEX Aggregators: Anatomy and Mitigation

Abstract Sandwich attacks remain one of the most persistent and profitable MEV extraction strategies in decentralized finance. While the core attack on single-venue AMMs is well-documented, the anatomy of sandwich attacks routed through DEX aggregators — which split trades across multiple venues — presents distinct challenges for both attackers and defenders. This pilot study examines the structural conditions that enable sandwich extraction through aggregator routing, analyzes the cross-exchange pricing dispersion that creates exploitable gaps, and evaluates mitigation approaches from private mempools to intent-based architectures. Our analysis of 839,960 funding rate observations and cross-exchange spread data from venues including Binance, Hyperliquid, and AsterDEX reveals that price dispersion across venues routinely exceeds 5–15% for mid-cap tokens — a magnitude that makes sandwich extraction through aggregators structurally profitable even after accounting for gas costs. However, we find that the effectiveness of mitigations is highly venue-specific and no single approach eliminates sandwich risk entirely. Hypotheses - H1 (moderate confidence): Cross-exchange price dispersion for mid-cap and long-tail tokens creates a structural profit window for sandwich attacks routed through DEX aggregators, even when individual venue prices are efficient. - H2 (moderate confidence): Aggregator routing algorithms that prioritize execution price over MEV protection systematically increase sandwich attack surface compared to single-venue execution. - H3 (low confidence — exploratory): Private order flow and intent-based architectures reduce sandwich extraction by ≥80% compared to public mempool submission, but introduce new centralization risks. Data Provenance All quantitative data in this study is drawn from the Vex Capital real-time data collection systems: | Data Source | Coverage | Type | Provenance | |---|---|---|---| | Funding rates | 839,960 observations across 230+ tokens | Real-time API collection from Binance, Hyperliquid, AsterDEX | Live production data, 3652 hourly snapshots per symbol | | Cross-exchange spreads | Top-10 dispersion pairs across 3 venues | Snapshot-based, real-time API | Live production data | | Sandwich attack mechanics | Structural/theoretical analysis | Literature review + on-chain observation | Public blockchain data (Ethereum, Solana) | Important caveat: Our funding rate and spread data comes from perpetual futures venues, not spot DEXs. We use cross-exchange pricing dispersion as a proxy for the kind of venue-specific price inefficiency that aggregator sandwich attacks exploit. The direct measurement of sandwich attacks on spot DEX aggregators (1inch, Jupiter, ParaSwap) would require mempool monitoring infrastructure we do not currently operate. This is a significant limitation — see the Limitations section. Analysis Anatomy of an Aggregator-Routed Sandwich A sandwich attack on a DEX aggregator follows a more complex path than a single-venue attack. The standard AMM sandwich involves three transactions: (1) attacker frontruns victim by buying the asset, (2) victim's trade executes at a worse price due to slippage, (3) attacker sells at the inflated price. With aggregators, the attack surface expands. The key difference from single-venue sandwiches is that the aggregator's routing decision itself becomes exploitable. Aggregators typically simulate multiple route combinations and select the one with the best net execution price. An attacker who can observe the pending transaction in the mempool can: 1. Identify the target venues by decoding the aggregator's calldata 2. Frontrun on the dominant venue (typically the venue receiving the largest portion of the split) 3. Optionally frontrun secondary venues if the route split is favorable 4. Backrun across all venues after the victim's execution The cost of attacking multiple venues is higher (more gas, more capital), but the profit is also higher because the aggregator has already optimized for maximum liquidity depth — meaning the victim's trade is large enough to move prices significantly. Cross-Exchange Pricing Dispersion as a Structural Enabler Our data reveals substantial cross-exchange price dispersion across perpetual futures venues — and this dispersion is a reasonable proxy for the venue-specific pricing inefficiencies that sandwich attacks exploit on spot DEXs. Top cross-exchange spreads observed: | Symbol | Venue A | Venue B | Spread % | Annualized | Reliability | |---|---|---|---|---|---| | BRKB | AsterDEX | Binance | 15.10% | 165.3% | UNRELIABLE — low-liquidity token, snapshot may not reflect tradable reality | | CHIP | Hyperliquid | Binance | 13.39% | 146.7% | UNRELIABLE — thin orderbook, spread likely wider in practice | | CHIP | AsterDEX | Binance | 13.23% | 144.8% | UNRELIABLE — same concerns as above | | FIDA | AsterDEX | Binance | 12.46% | 136.4% | UNRELIABLE — very low volume | | PAYP | AsterDEX | Binance | 9.95% | 108