When ‘Recent Interactions’ Lie: How 0-Value Lookalikes Steal Funds

On-chain users often treat “recently interacted” addresses as a sign of trust — and attackers have found a cheap, effective shortcut: send 0-value transfers to lookalike addresses so those fake addresses show up in wallets’ “recent interactions” or autocomplete lists. Victims then copy the wrong address and send real funds. PandaTool breaks down the attack, explains why it works, and provides practical defenses and deployable detection ideas for both users and product teams.

1. Attack logic 

• Attacker crafts an address that looks “very similar” to the target (similar tail, a few characters different, or common character substitutions).

• Attacker sends 0-value on-chain interactions (0 ETH / 0 USDT / 0 tokens or other events) to that fake address, creating on-chain history and Transfer events.

• Wallets or block explorers include the fake address in “recent interactions” or autocomplete dropdowns.

• Victim sees an address with “interaction history,” assumes it’s trustworthy, copies/selects it, and sends funds — which go to the attacker.

2. Why 0-value transactions work

• On-chain footprint: Even with value = 0, events/transactions are recorded and indexed (transfer events, to/from logs).

• Very low cost: Attackers only pay gas/fees and can mass-produce “pseudo-trust history.”

• Human blind spots: Users often check only tails or rely on “recent interaction” trust, not full character verification.

• UI/display issues: Wallets/browsers usually display all interactions equally and don’t treat 0-value events as suspicious by default.

3. Attack scenario

Alice intends to send 10,000 USDT to Bob. The attacker creates an address whose tail differs from Bob’s by only 2–3 characters and floods it with 0 USDT transfers. That fake address appears in Alice’s “recent interactions.” Alice selects it without character-by-character verification and ends up sending 10,000 USDT to the attacker.

4. Practical user safety guidelines 

1. Do not blindly copy large addresses from “recent interactions” — treat them only as clues.

2. Compare pasted addresses character-by-character: use a comparison tool or paste both addresses into a text editor and verify.

3. Prefer ENS / human-readable names or wallet contact/address-book entries.

4. Send a small test amount first (e.g., 0.001 ETH or a tiny token amount) to confirm recipient control.

5. Use address white-lists/contacts and only send large amounts to whitelisted addresses.

6. Use hardware wallets and out-of-band signing workflows for high-value transfers.

5. Product recommendations for wallets / explorers / exchanges 

1) Exclude 0-value interactions from “recent / frequent” lists

• By default collapse/hide 0-value interactions (or place them into a “suspicious / zero-value interactions” group) and show them only when the user explicitly requests “all interactions.”

2) Add lookalike detection and clear warnings

• When a pasted/selected address closely resembles a user’s historical/whitelisted address (Levenshtein distance ≤ threshold or tail difference small), show a prominent warning and highlight differing characters:

  “Warning: this address differs from your usual address by 3 characters — it may be a phishing address. Please verify.”

3) Display address fingerprints and checksum status

• Show address avatars, ENS names, and EIP-55 checksum verification. Highlight when checksum/ENS is absent or mismatched.

4) Strengthen paste/confirmation flows

• After pasting, display the full address and visually highlight changed segments. Require an explicit confirmation step (e.g., “I have verified the first/last 6 characters”) before allowing high-value sends.

5) Risk controls for bulk 0-value activity

• Automatically tag addresses that receive many 0-value interactions in a short window as “suspect” and exclude them from autocomplete/smart suggestions pending review.

6) Offer a “white-list only” transfer mode

• Allow users (especially enterprises and high-net-worth) to enable a mode where transfers are only permitted to white-listed addresses.

6. Backend detection idea

This snippet can run in a wallet backend or block explorer to detect “0-value + lookalike” patterns and mark/hide suspicious addresses:

Optimizations: use tail substring prechecks to avoid expensive Levenshtein calls, include common character substitutions (O↔0, l↔1, I↔l), and combine with rate analysis (many 0 transfers from many senders to the same address) for higher confidence.

7. Suggested front-end messages (copy-ready)

• Warning bar: “This address differs from your usual address by 3 characters — it may be a phishing address. Please verify.”

• Action buttons: “Send a test amount (recommended)”, “I have verified character-by-character — proceed” (with explicit second confirmation).

• Tooltip: “Why hide zero-value interactions? Attackers use 0 transfers to fabricate pseudo-trust history; we collapse these by default to protect you.”

8. Advanced / ecosystem defenses

1. Shared suspicious-address feeds: build a shared on-chain blacklist of addresses used for 0-value lookalike campaigns that wallets can subscribe to.

2. On-chain metadata & signatures: promote use of ENS, signed website statements, or other out-of-band verification from projects and counterparties.

3. Enhanced confirmations for large/new addresses: require delays, multi-sig, or manual review for large transfers to new recipients.

4. UI + education: embed short, actionable education in the wallet UI to train users to verify addresses.

9. Quick product rollout checklist (deliverable within ~3 days)

1. Hide 0-value interactions from “recent addresses” (backend rule + frontend fold).

2. Add tail-difference detection and offer an immediate popup warning on paste/select.

3. Deploy a simple server-side tail compare + common substitution filter to start tagging addresses.

4. Add a one-click “send test amount” prompt and A/B test the message copy.

Conclusion (PandaTool stance)

The “0-value + similar tail” phishing pattern is a social-engineering attack amplified by visible on-chain traces. It’s cheap for attackers and scalable, but product and UX changes — combined with user education — can materially reduce its success. PandaTool, as a token creation and on-chain tools provider, recommends prioritizing “zero-value filtering, lookalike detection, and whitelist workflows” in security roadmaps.

If you’d like, we can convert the detection pseudo-code to a runnable implementation in Node.js, Go, or Python, or draft a PRD with APIs, risk rules, and frontend copy/mockups — tell us which and we’ll produce it.