Exclusive data from EigenPhi reveals that sandwich attacks on Ethereum have waned

MEV and sandwich attacks: what they are and why they happen

What is MEV and why does it matter?

Maximal extractable value (MEV) captures the extra profit that can be extracted by miners or, more accurately in today’s Ethereum ecosystem, by block builders who can influence the order, inclusion, or exclusion of transactions within a block. The title “MEV” hides a large spectrum of possibilities—from legitimate arbitrage that improves liquidity to malicious strategies that siphon value from everyday users. In the context of this article, MEV is the umbrella term under which sandwich attacks live: a deliberate manipulation of transaction ordering that harms the victim while rewarding the attacker with a spread.

How do sandwich attacks work in practice?

A typical sandwich attack unfolds like this: a victim submits a swap on a DEX (decentralized exchange). An attacker detects the pending swap, places a front-running transaction to buy the same asset just before the victim’s swap, then places a back-running transaction to sell after the victim’s trade, effectively raising the price for the victim and pocketing the difference. The attacker hopes to secure a profit margin despite gas costs. In simple terms, the attacker “sandwiches” the victim’s order, squeezing out extra value from the price impact. The title here is the idea that ordering is not inevitability—it is weaponizable in real time, particularly on Ethereum where transaction ordering is visible to searchers and opportunists.

Temporal snapshot: how the landscape evolved from late 2024 to late 2025

Sharp decline in overall extraction, steady risk to users

EigenPhi’s dataset, spanning almost a full year, reveals a pronounced decline in gross MEV extraction from sandwich attacks in 2025. Monthly extraction volumes dropped from roughly $10 million in late 2024 to approximately $2.5 million by October 2025. The title of this trend is “decline in extracted value,” but the risk is not eradicated. The net profit after gas costs hovered around $260,000 per month on average in 2025, with a notable outlier in January 2025 when a single attack yielded more than $800,000. When viewed in the broader context of rising on-chain activity, this pattern suggests attackers shifted toward high-volume, lower-margin strategies and away from occasional blockbuster wins.

DEX volumes rise, but attacks remain common

From Q1 to Q3 of 2025, monthly DEX volumes on Ethereum rose from about $65 billion to well over $100 billion. This expansion in on-chain activity coincided with a reduced extraction per attack but a sustained number of attacks overall. The 2025 average monthly attack count sits in the 60,000 to 90,000 range, signaling that the attack surface remains large even as profitability compresses. The title here emphasizes the paradox: more activity yet less bundling profit per event, which incentivizes attackers to pursue quantity over megaprofits in some cases.

Profitability per attack compresses, but total opportunities persist

The economics of sandwich attacks shifted in 2025: average profit per attack stayed meager—roughly a few dollars to a few tens of dollars in many cases, with outliers driving the totals higher. In aggregate, profits remained hollowed out by competition among attackers, high gas costs, and the expansion of liquidity-protective strategies. Yet even with compressed margins, the soup of opportunities persisted, especially for bots that could chain multiple victims within a single center transaction or manipulate liquidity to tilt prices further for subsequent victims. The title is a reminder: even when the headline risk wanes, the vulnerability remains embedded in market structure and protocol economics.

Who is doing the sandwich attacking? The players and the patterns

Jared from subway.eth: a persistent and innovative MEV actor

One name dominates the public narrative: Jared, the pseudonymous MEV searcher jaredfromsubway.eth. In 2025, roughly 70% of all sandwich attacks were associated with Jared, highlighting how a single actor can scale activity through automation and sophisticated strategies. Jared’s v2 bot has introduced capabilities to target up to four victims in a single operation, sometimes inserting a center transaction to push swap rates even further for subsequent victims. The attacker can also influence price by adding or removing liquidity from pools, expanding the footprint of a single strategy across multiple opportunities. The title emerges here as “platform dominance and the cumulative power of a single sophisticated operator.”

The role of other attackers and smaller botnets

While Jared accounted for the lion’s share of activity, the dataset shows a constellation of other attackers—thousands of distinct sandwich bots active over 2025. In October 2025, there were 515 distinct bots, but only a little over 100 engaged in typical-month sandwich activity. This concentration suggests a bifurcated market: a handful of high-velocity operators with deep capital, and a broader ecosystem of niche bots chasing a high-frequency, low-margin path. The title here is “distribution of attention,” underscoring how concentration shapes risk, profitability, and competition for opportunities.

Where do these attacks land? Pools, tokens, and slippage risk

Target pools: low-volatility pools and stable assets

Data shows that around 38% of attacks targeted low-volatility pools that included stablecoins, wrappers, and LSTs (liquid staking tokens) tied to Ether and Bitcoin. Approximately 12% of all sandwiches hit stable swaps—places where slippage risk can be especially damaging and often unexpected for users who assume stability in these channels. The title in this section highlights the “risk clustering” around stable assets, a paradox where the most liquid and supposedly secure pools still become fertile ground for manipulation when order flow is exposed.

Memecoins and other high-variance assets as magnets

Beyond stable assets, the memecoin MANYU paired with WETH emerged as an actively traded target outside the stable and wrapped categories. Jared has repeatedly targeted this pool since July 2025, extracting nearly $19,000 across 65 sandwich attacks. This example illustrates how even seemingly marginal pools can offer recurring, if small, revenue streams for persistent attackers. The title here underscores the “diversification of targets” as attackers pursue a mix of high- and low-margin opportunities to keep revenue streams alive in a compressed-margin regime.

Economic reality: profits, losses, and the gas cost dynamic

Gas costs as a decisive factor

Gas fees have remained a critical factor in the economics of sandwich attacks. In 2025, gas costs stayed relatively low compared to the per-attack revenue that attackers could secure, which helped keep the overall viability of the strategy high even as gross profits shrank. The title here is “gas as a moderator of MEV profitability”—a reminder that external costs can shape the profitability curve as much as the opportunity count.

Profitability by attacker and by strategy

Profitability is no longer a uniform, one-size-fits-all equation. Some attackers achieve meaningful returns through high-volume, low-margin strategies that prioritize coverage of many opportunities. Others chase fewer but larger opportunities. The data indicate that only a handful of attackers generated more than $10,000 in total profit across 2025, highlighting the intense competition and the narrow margin thresholds that define successful MEV bot strategies. The title here emphasizes the shift from singular mega-wins to scalable, repetitive gains, driven by bots like Jared’s that optimize throughput and capture small profits with high frequency.

Defensive trends: protection tools and the policy debate

MEV protection tools gaining traction

Traders and developers are increasingly turning to protective tooling to mitigate sandwich attacks. Notable innovations include threshold encryption approaches and batched threshold encryption schemes that obscure the exact ordering opportunities from attackers. Shutter’s threshold encryption and Batched Threshold Encryption (BTE) are part of a growing toolbox aimed at reducing the usefulness of order flow to MEV searchers. The title here is “protection technologies in deployment,” signaling practical steps users can take and signaling to policymakers that protocol-level safeguards may be necessary.

Native MEV protection: a looming protocol question

There is a vibrant debate about introducing native MEV protection at the Ethereum protocol layer. Some propose on-chain defenses that prevent front-running or impose fairer transaction ordering. Others argue that any protocol change must balance decentralization, efficiency, and complexity. The title phrase in this section points to the policy angle: “will protocol-level protections become a standard feature of Ethereum, or will protection rely on external tools and exchanges?” The answer will shape both future product design and network governance.

What traders can do now: practical takeaways

Adopt MEV-protection tools and practices

Traders who want to reduce exposure to sandwich attacks can adopt a mix of protective strategies. Use wallets and services that integrate MEV protection features, enable transaction batching, or leverage front-run resistant routing where available. Consider employing private transaction relays or builders that commit to fair ordering practices. The title for this section is “practical defense measures,” underscoring that protection is not a single tool but a layered approach combining software, routing, and governance-aware behavior.

Slippage controls and transaction design

Design swaps with slippage-conscious parameters and implement price impact checks that detect suspicious order-flow patterns. Break large swaps into smaller chunks to reduce the incentive for attackers to sandwich a single large order, while balancing the cost of multiple transactions and gas. The title here is “design choices that reduce exposure,” reflecting how user behavior can shape vulnerability profiles.

Education and awareness: reading the data title

Understanding the data behind sandwich attacks—what EigenPhi and other researchers publish—helps traders and compliance teams avoid overconfidence in “safe” pools. The title component of this section emphasizes the importance of reading, understanding, and acting on the data to create safer trading practices, even as total volumes rise and risk persists in nuanced ways.

Conclusion: what the Exclusive data actually tell us

The data from EigenPhi paints a nuanced picture. Sandwich attacks have waned in terms of gross extraction in 2025, but the underlying risk remains real for ordinary users. The title of this conclusion is clear: the market adapted to a more competitive environment, where attackers rely on quantity and speed rather than single grand wins. While the total monthly profits after gas costs declined, the consistent stream of tens of thousands of attacks per month implies persistent vulnerability in the system. As the ecosystem evolves, the community has reason to pursue native MEV protections, improved governance of block-building markets, and more robust protective tooling to shield users from sandwiching — without sacrificing the core principles of open, permissionless finance.

FAQ: common questions about MEV, sandwich attacks, and the 2024–2025 window

1. What is MEV, and how does it relate to sandwich attacks?

   MEV stands for maximal extractable value, the extra profit that can be extracted by reordering, including, or excluding transactions within a block. A sandwich attack is a specific MEV tactic where an attacker frontruns and backruns a victim’s swap to capture the price movement. The title here is the key concept: MEV is a broad ecosystem phenomenon, and sandwich attacks are among the most visible, damaging, and well-studied manifestations.

2. Why has sandwich extraction waned in 2025?

   The decline in gross extraction reflects several factors: intensified competition among attackers, higher gas costs relative to per-attack revenue, and broader availability of MEV-protection tools. However, the risk persists due to the sheer scale of on-chain activity and the continuous availability of order-flow data to sophisticated bots. The title emphasizes the paradox: fewer profits per attack, but a steady stream of opportunities keeps the attack surface active.

3. Who are the main actors in this space?

   One dominant actor appeared to be Jared (jaredfromsubway.eth), responsible for roughly 70% of observed sandwich attacks in 2025. Jared’s strategies include multi-victim targeting and center transactions that amplify price impact for subsequent victims. While Jared is prominent, a broader ecosystem of hundreds of bots exists, with only a subset engaging in regular sandwich activity. The title here highlights how one prolific operator can shape the narrative around MEV in a crowded space.

4. What pools and tokens are most affected?

   Attacks have concentrated around low-volatility pools with stablecoins and wrapped assets, including Ethereum-native LSTs and Bitcoin wrappers. Stable swaps also see meaningful slippage risk. The notable exception is the many smaller pools that nevertheless yield repeat profits, underscoring a diversified risk landscape. The title signals the importance of understanding pool characteristics when assessing vulnerability.

5. What can be done to protect ordinary users?

   Protection strategies include protocol-level debates about native MEV safeguards, deployment of threshold encryption techniques, and consumer-facing tools that reduce exploitable order-flow visibility. Traders can employ MEV-protection wallets, transaction batching, and caution in high-volume periods. The title here is “defense in depth”—no single fix will eliminate risk, but a layered approach can reduce exposure significantly.

6. Is a protocol-level solution coming?

   There is ongoing debate among developers, researchers, and stakeholders about embedding MEV protections directly into the Ethereum protocol. While consensus is not yet achieved, the momentum behind native protections is growing, driven by the amount of capital at stake and the persistent frustration of ordinary users who experience slippage and losses. The title here is a forward-looking question: will protocol changes finally curb sandwiching without compromising decentralization and performance?