As the crypto ecosystems scale across different blockchains, threat actors are starting to exploit an unexpected weakness: users’ reliance on visual similarity and, by extension, their familiarity with wallet addresses. One new attack vector involves multichain address impersonation, where scammers spoof wallet identities across different blockchains to gain a user’s trust. This phenomenon plays an important part in building the credibility of so-called address poisoning schemes—fraudulent schemes in which attackers insert lookalike wallet addresses into a user’s transaction history with the intent of tricking the user into sending funds to the wrong destination.
This article looks at how multichain impersonation amplifies trust, why users fall for such schemes, common risk indicators, and practical defense strategies.
Understanding Multichain Address Impersonation
What is multichain address impersonation?
Impersonation of multichain addresses involves the creation of identical or near-identical wallet addresses on different blockchains to pose as legitimate. Since most chains make use of similar address formats, for example, EVM-compatible chains, scammers are able to easily reproduce the first and last characters of a target address in order to make the impostor look authentic.
Why It Works
Users often do not verify the full address, but depend on patterns they are familiar with:
The first 4–6 characters
The last 4–6 characters
Addresses appearing previously in transaction history
Familiarity from multi-network usage
What is assumed is that identical prefixes mean identical owners.
It's this psychological familiarity that becomes hazardous when mixed with address poisoning.
How Multichain Impersonation Strengthens Trust in Address Poisoning
1. Exposure Across Multiple Chains Creates Credibility
If a user sees the same lookalike address on Ethereum, BNB Chain, Polygon, and Base, they naturally assume it belongs to a legitimate contact or the same counterparty. This cross-chain visibility increases confidence, even though it may be a malicious replica.
Psychological impact:
"Repeated exposure" creates perceived authenticity.
2. Multichain Presence Mimics Professional User Behavior
Large businesses, investors, and protocols often maintain wallets across a multitude of networks.
By impersonating this behaviour, an attacker's impersonated identity will appear more trustworthy.
Users may be misled to think that:
This address belongs to a verified exchange
A partner they recently transacted with
A platform performing cross-chain operations
A liquidity provider or DApp contract
3. It makes address poisoning look like a real transaction
This is how it usually happens in an address poisoning scheme: scammers send small token transfers to get their impersonated address into your wallet history. And if that same lookalike address also appears on other blockchains you use, well, the user feels even more assured that it is familiar.
This is precisely the reinforcement loop scammers use:
Impersonation across chains creates recognition
Recognition minimizes doubt.
Reduced doubt increases the likelihood to copy the poisoned address
The user's actual transaction is received by the attackers.
4. Users tend to reuse contacts across chains
The tendency of users to send funds with the same counterparties across networks is another big driver of trust. Attackers know this and hence place their impersonated addresses where the user operates.
Example:
If you just paid a freelancer on Polygon, such a scammer could contrive a similar address on Ethereum to deceive you during your next payout.
5. Multichain Transaction Aggregators Multiply the Deception
Wallets and portfolio trackers displaying cross-chain data within one user interface are inadvertently contributing to impersonation tactics. When the same fake address is showing up more than once across different chains, this suggests to the user:
A multi-network wallet that is verified
A contact that is used often
A trustworthy record
This UI-driven trust is precisely what scammers try to hijack.