Multi-chain sybil ops: how to farm 5 chains from one operations base

The airdrop meta has shifted. Three years ago you could deploy a single wallet, bridge a few hundred dollars, do some swaps, and collect. That window is gone. Every serious L2 and appchain now runs a sybil filtering pass before snapshot. Arbitrum’s 2023 airdrop famously clawed back tokens from wallet clusters using on-chain graph analysis. LayerZero’s 2024 distribution used a proof-of-donation system with mandatory self-reporting of sybil wallets, which tells you how bad the cheating had gotten. The protocols know what’s coming.

The response from the operator community has been predictable: more chains, more wallets, tighter operational separation. Running five chains from one operations base is not about volume for its own sake. It’s about diversification against snapshot timing risk, partial filtering, and chain-specific eligibility thresholds. If you’re only farming one chain and the team decides your cluster is sybil, your entire position evaporates. Spread across five with proper separation and even a 50% filter rate leaves you with meaningful exposure.

This piece assumes you know the basics: you’ve run wallets before, you understand gas, you’ve read at least one post-mortem on why a wallet cluster got filtered. What I want to cover here is the operational architecture, the systematic mistakes people make scaling from one chain to five, and what the behavioral fingerprint of a legitimate user actually looks like so you can build toward it rather than away from it.

background and prior art

The sybil farming playbook was effectively documented in public by the Hop Protocol airdrop in 2022, where the team published their filtering methodology. They looked at funded-from relationships, transaction timing clusters, and bridge usage patterns. That analysis is worth reading because it established the template most protocols now follow. The core problem they identified was not that sybil wallets existed, it was that they were operationally identical: same funder, same timing, same gas price, same sequence of actions.

The Arbitrum airdrop in March 2023 went further. Post-distribution, on-chain analysts at Nansen and independent researchers identified clusters using graph distance from funding sources, address similarity scoring, and activity pattern matching. Roughly 150 million ARB was flagged across identified sybil clusters, though the distribution had already gone out by then. The lesson was that post-hoc filtering is a real threat to future airdrops, not just current ones. Protocols share data. If your wallet cluster is identified on chain A, that graph can be pulled for chain B’s analysis.

More recently, zkSync’s 2024 airdrop distribution explicitly excluded addresses that appeared in known sybil databases and flagged wallets with transaction patterns inconsistent with genuine usage. The zkSync developer documentation doesn’t detail the filtering criteria publicly, but independent analysis in the airdrop community consistently found that time-between-transactions and gas-price variance were strong signals. Wallets that executed actions in machine-like intervals with identical gas parameters were filtered at much higher rates.

the core mechanism

Running five chains from one operations base means you need to solve four distinct problems: infrastructure isolation, capital routing, behavioral independence, and temporal separation. Most people get the infrastructure part roughly right and then fail on the other three.

infrastructure isolation

Each wallet or wallet group needs its own network identity. This is non-negotiable. A residential proxy pool rotated per-session is the minimum viable setup. Datacenter proxies from a single ASN are a known bad pattern, the activity looks like it originates from a server farm because it does. For the five-chain setup I run, each wallet group (I typically use 5-10 wallets per group) gets assigned to a specific proxy provider and geographic region. I use different providers per chain. Mixing providers per chain creates another layer of variance.

Browser fingerprinting matters less for on-chain activity but matters for anything that involves a frontend. If you’re doing governance votes, quest completions, or testnet faucet claims through a web interface, you need antidetect browser profiles with consistent, unique fingerprints. Antidetect Review has detailed comparisons of the main options if you need a starting point for tooling selection.

For wallet generation, I use a deterministic setup per chain: each chain gets its own derivation path namespace within a hardware wallet seed, or better, entirely separate seeds per chain. The key rule is that no two chains should share a derivation path. Metamask’s default HD wallet derives all chains from the same seed with the same account index, which means your Arbitrum address 0 and your zkSync address 0 are trivially linkable. Use separate seeds or at minimum separate account indices with a documented mapping.

capital routing

This is where most ops fall apart. The naive approach is to fund all wallets from one exchange withdrawal address. That creates a star topology in the funding graph with your exchange KYC account at the center. Any analyst with a block explorer can pull that graph in twenty minutes.

The correct approach is layered funding with temporal and amount variance. I use three layers. Layer one is a set of “mixer” wallets that receive from exchange and then wait a randomized period (days, not hours) before forwarding. Layer two is a set of “distribution” wallets that receive from layer one and split to the operational wallets. Layer three is the operational wallets themselves. Each hop introduces variance in timing and amount.

For cross-chain deployment, I use different bridges per chain deliberately. If all five chains are funded through the same bridge contract from the same source address, you’ve recreated the star topology on-chain. Use Stargate for one chain, use the native bridge for another, use a third-party bridge for a third. Amount variance matters here too. Don’t bridge exactly 0.1 ETH to every chain. Bridge 0.087 to one, 0.113 to another, 0.094 to a third.

The Ethereum developer documentation has a clear explanation of the UTXO-like account model that explains why on-chain graph analysis is so tractable. Every ETH transfer is a public edge in a public graph. There’s no technical mitigation for this, only operational mitigation through layering and variance.

behavioral independence

Each chain should have an activity pattern that could plausibly represent a distinct person with distinct interests. This sounds obvious but it’s operationally difficult to maintain at scale. The failure mode is running the same script on five chains simultaneously: same dApps, same actions, same timing, same gas parameters.

For each chain, I maintain a protocol list. Arbitrum wallets interact with GMX, Camelot, and Pendle. zkSync wallets use Syncswap, Mute, and the native bridges. Base wallets use Aerodrome, Morpho, and Coinbase’s onchain products. The overlap in DeFi primitives is unavoidable (everyone swaps, everyone bridges) but the specific application layer should differ.

Gas parameter variance is important. Set maxPriorityFeePerGas with a small random offset per transaction rather than using the default suggested value. A cluster of wallets all paying exactly the same priority fee is a clustering signal.

temporal separation

Don’t run all five chains on the same schedule. Use a different day-of-week pattern per chain, different hours of day (ideally matching the time zone of the geographic region your proxies simulate), and different transaction frequency. One chain might average 3 transactions per week with high variance, another might average 1 per day with low variance.

I keep a simple spreadsheet tracking last-activity date per wallet per chain. The goal is that no two chains have synchronized activity windows. If you’re doing 10 actions on all 5 chains every Sunday evening, that’s a detectable pattern even without IP-level analysis.

worked examples

example 1: Arbitrum + Optimism separation

These two chains get conflated because they share the same EVM architecture and many of the same DeFi protocols exist on both. I use distinct wallet sets with a 30-day gap between initial funding on each. For Arbitrum, the focus is GMX perpetuals, Camelot V3 LPs, and occasional governance participation on Arbitrum DAO. For Optimism, the focus is Velodrome liquidity provision and OP Stack bridges to Base.

Capital routing: Arbitrum wallets funded from Binance via Arbitrum native bridge in variable amounts ($80-$150 equivalent). Optimism wallets funded from Kraken via Hop Protocol bridge, different amounts, 2-3 weeks later. No overlap in funding source or bridge. Over 6 months, Arbitrum wallets accumulate 15-25 transactions each. Optimism wallets accumulate 20-35 transactions each, slightly higher because Velodrome rewards incentivize more frequent claiming.

example 2: zkSync wallet setup

zkSync’s sybil analysis has historically focused on contract interaction diversity and time-on-chain. The target for a zkSync wallet in my setup is: first transaction at least 90 days before any expected snapshot, interaction with at least 4 distinct protocols, at least one bridge in and one bridge out, and gas fees paid across at least 8 weeks.

I fund these wallets from CEX withdrawals using the official zkSync bridge (zkPortal), with amounts ranging from $60-$200 equivalent. The wallet then does a sequence over 90+ days: initial swap on Syncswap, a liquidity provision on Mute, a transaction using zkSync’s native account abstraction features (this specifically looks like genuine user behavior since few scripts use AA), and periodic small swaps. The AA interaction is the part most scripts skip and it’s a meaningful differentiator.

Gas costs for this setup: roughly $15-30 per wallet over the activity period given zkSync’s low fees. Total capital deployed per wallet for meaningful DeFi activity: $100-$300 including LP positions.

example 3: Base + Blast parallel farming

Base and Blast ran overlapping hype cycles in 2023-2024. The key for these is that they’re both Coinbase-adjacent ecosystems where genuine users have identifiable characteristics: Base users often have Coinbase-linked wallets, Blast users in the early days were often DeFi-native with prior Ethereum mainnet history.

For Base, I use wallets that have prior mainnet activity (funded initially with some mainnet history established, then bridged to Base using the official bridge). For Blast, I use a separate wallet set with no mainnet overlap. The Blast set gets funded through a different CEX and different bridge path. Activity on Base focuses on Aerodrome, friend.tech adjacent products, and Coinbase’s onchain name registrations. Activity on Blast focuses on the native yield products and Thruster DEX.

The Arbitrum developer documentation and Optimism developer documentation are both worth reading for understanding the technical differences between these chains that genuine users would plausibly know and act on.

edge cases and failure modes

centralized exchange withdrawal linkage

The most common failure. You withdraw from Binance to fund 30 wallets across 5 chains. Binance’s withdrawal address is public. Anyone can see that your “30 independent users” all received their initial funding from the same CEX hot wallet address within a 48-hour window. Solution: use multiple CEXs, space withdrawals over weeks, and vary amounts. If you can use peer-to-peer or OTC for some funding, that breaks the CEX linkage entirely.

RPC endpoint clustering

If all your wallets use the same RPC endpoint (e.g., the default Infura endpoint or a single Alchemy key), your transactions may share metadata that’s visible to the node operator even if not on-chain. This is harder to defend against but the mitigation is using multiple RPC providers per chain: some wallets through Alchemy, some through QuickNode, some through public endpoints. For the multi-account operations setup I run, I’ve documented this more thoroughly in a separate guide on proxy and RPC architecture if you want the full breakdown.

temporal correlation attacks

If you run all your wallets in a nightly batch job, the timestamps of your transactions will cluster around the same time window. Even without IP-level data, a protocol can identify that 47 wallets all made their first transaction within a 3-hour window on the same night. Use a scheduler with genuine randomness (not just a random seed, but actual random delays pulled at runtime), and run some wallets in the morning, some in the afternoon, some irregularly.

gas price correlation

I mentioned this in the mechanism section but it deserves its own failure mode entry. Scripts that use eth_gasPrice RPC call and then use that exact value for all transactions in a batch create a tight correlation. Wallets that independently used the network would have slightly different gas prices because they submitted at different times. Add a random offset of ±5-15% to any gas parameter your script pulls from the network.

over-optimization on known criteria

This is the meta-game failure. You read that protocol X values 6+ months of activity, so you backdate all your wallets to 6 months before snapshot. But you’ve done it so uniformly that your entire cluster hits the 6-month mark within a 2-week window and has suspiciously identical transaction counts. Genuine users have a distribution: some wallets 3 months old, some 9 months, some 18 months. Model a distribution, not a threshold.

The LayerZero documentation and their approach to cross-chain messaging illustrates why bridging patterns are so revealing. Every bridge transaction is a cross-chain event that links wallet state on two networks. If 40 wallets all bridged exactly 0.1 ETH via the same bridge in the same direction on the same day, that’s a cluster.

what we learned in production

Running this setup for about 18 months across active farming periods, the biggest surprise was how much the behavioral layer matters relative to the infrastructure layer. We put enormous effort into proxy setup, browser profiles, separate seeds, different CEX accounts for funding. That work is necessary but not sufficient. The wallets that got filtered in our post-mortems were almost always filtered for behavioral reasons: identical sequences of interactions, machine-like timing, no variance in DeFi protocols used.

The shift in our approach was treating each wallet group as representing a specific user persona with a specific risk profile and a specific DeFi interest area. One group is the “yield optimizer” persona: they LP, they claim rewards frequently, they bridge between chains looking for better APR. Another group is the “governance participant” persona: lower transaction count, but they vote, they hold governance tokens, they use longer time horizons. Another is the “degenerate trader” persona: higher frequency, perp trades, more chains, smaller position sizes. These personas naturally produce different behavioral signatures without requiring us to write entirely different scripts for each.

The cost management aspect is also more complex than it looks from the outside. Across 5 chains with 40-50 wallets, gas costs alone can run $500-$1,000 per month during active farming periods, more on Ethereum mainnet. Capital deployed in LP positions to generate genuine DeFi history can be $10,000-$30,000 if you’re doing it properly. The economics only work if you’re capturing meaningful airdrop value, which is not guaranteed. I want to be direct about this: this is not a guaranteed return activity. Protocols filter, snapshots get delayed, token prices disappoint. Treat it as speculative.

For the infrastructure side, I document my tooling choices and proxy setup in a different post on the blog index. The short version is: residential proxies from separate providers per chain, hardware wallets for seed management, a custom Python script for transaction scheduling with proper randomization, and a simple Postgres database for tracking activity state per wallet.

references and further reading

Ethereum Developer Documentation, Ethereum Foundation - the canonical reference for understanding EVM account model, gas mechanics, and transaction structure. Essential reading for understanding why on-chain clustering is tractable.
Arbitrum Developer Documentation, Offchain Labs - covers the specific technical characteristics of Arbitrum One and Nova, useful for understanding the chain-specific behavior patterns that distinguish genuine Arbitrum users.
Optimism Developer Documentation, OP Labs - OP Stack chain documentation, including the standard bridge architecture that many L2s now use. Understanding the bridge mechanics informs how to vary bridging patterns.
zkSync Developer Portal, Matter Labs - covers account abstraction and the specific transaction types native to zkSync Era. The AA features in particular are worth understanding because genuine power users tend to use them.
Related reading on multi-account infrastructure and behavioral separation: Multi-account operational guides at multiaccountops.com and proxy setup deep-dives at proxyscraping.org.

Written by Xavier Fok

disclosure: this article may contain affiliate links. if you buy through them we may earn a commission at no extra cost to you. verdicts are independent of payouts. last reviewed by Xavier Fok on 2026-05-19.