Strong signals include repeated exposure to known destination addresses, consolidation from many victim wallets, and a visible path from suspicious wallets to centralized exchanges. Investigators should also look for clusters that match approval phishing behavior, especially when several accounts show similar funding, approval, and draining patterns. Those signals help prioritize leads and identify accounts worth freezing or escalating.
What the right cluster looks like in a blockchain tracing workflow
The strongest cluster candidates usually share both movement patterns and destination behavior. Investigators are looking for wallets that interact with the same drain address, route value through a small set of repeat receivers, or converge into a common exit point such as a centralized exchange. The point is not just “shared activity,” but a chain of transactions that is operationally consistent with theft, laundering, or scam cash-out.
A good cluster will usually survive simple noise reduction. If the same destination keeps appearing after many victim deposits, or if the suspected wallets only make sense when viewed as a group rather than one by one, that is stronger than an isolated transfer. This is why tracing tools and manual review are both useful: clustering exposes the structure, while analyst judgment tests whether the structure is actually explanatory.
One practical way to think about it is attribution by repetition. Reused destination addresses, repeated intermediary hops, and a consistent path toward exchange infrastructure all increase confidence that the cluster is real and actionable. By contrast, one-off transfers, unrelated counterparties, or flows that stop at a wallet with no further pattern are weaker signals and often belong in a lower-priority bucket.
Why approval-phishing patterns matter so much
Approval phishing often creates a recognizable transaction signature because the attacker is not simply receiving funds, but using victim-granted permissions to drain assets after an initial deceptive interaction. That means multiple accounts can show similar funding sources, similar approval transactions, and similar downstream draining behavior even when the victims never interacted with one another.
When several wallets exhibit the same funding and approval sequence, the cluster becomes more credible as an attack cluster rather than a coincidence. Investigators should look for common token approvals, repeated contract interactions, and a shared destination pattern that links the individual victims back to the same operational flow. The more the sequence repeats, the more likely the cluster reflects a campaign rather than random wallet activity.
This pattern is especially useful because approval-based abuse can look benign if each wallet is reviewed separately. The transaction cluster only becomes obvious when the investigator compares the sequence across accounts and sees the same setup, trigger, and drain path repeated at scale.
How investigators should treat cluster confidence and escalation
Cluster confidence should be based on convergence, not just volume. A cluster is more actionable when many wallets point to the same destination, when the path from suspicious wallets to an exchange is visible, and when the pattern aligns with known scam behavior such as approval abuse or coordinated draining. That combination usually justifies escalation, wallet freezing where possible, and broader tracing into adjacent wallets.
It is also important to separate attribution confidence from response urgency. A cluster can be worth freezing or flagging before every node is fully understood, especially when the pattern shows active victim harm or a likely cash-out path. Investigators do not need perfect certainty to treat a cluster as operationally significant, but they do need enough structure to avoid chasing unrelated background activity.
The most useful clusters are the ones that help answer a practical question: which wallets are part of the same scam operation and which are just incidental transfers? If the answer is not improving as more transactions are added, the cluster may be too weak to drive action.
Risk and Threat Considerations
Weak clustering creates two common failure modes: false positives that waste investigation time, and false negatives that let a scam campaign continue because the wallets were reviewed as isolated events. Approval phishing is especially risky because the approval transaction can look legitimate until the draining step appears.
Failure mechanism: Attackers reuse the same drain infrastructure, route proceeds through repeat intermediary wallets, and exploit victim-granted approvals so the transaction graph only becomes obvious when multiple accounts are correlated.
Impact: Misclassified clusters can delay freezing, allow additional victim losses, and reduce the investigator’s ability to identify the exchange or exit point where funds can still be intercepted.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK and OWASP API Security Top 10 address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| MITRE ATT&CK | T1071 — Application Layer Protocol | Tracing scam exits often follows repeat communication and transfer paths. |
| Recommendation — Map repeated exit paths and hunt for correlated transfer infrastructure in your detection workflow. | ||
| CIS Controls v8 | CIS-8 — Audit Log Management | Wallet clustering depends on retaining traceable transaction evidence and reviewable logs. |
| Recommendation — Preserve transaction and case logs so analysts can correlate wallets and verify cluster patterns. | ||
| NIST CSF 2.0 | DE.AE-02 — Detected events are analyzed to understand targets and tactics | Analysts must interpret transaction patterns to confirm scam clusters and prioritise response. |
| RS.MI-01 — Incidents are contained | Confirmed scam clusters drive containment actions such as freezing, blocking, or escalation. | |
| Recommendation — Analyze correlated wallet activity to distinguish campaign patterns from isolated transactions. Contain confirmed scam clusters by freezing exposed wallets and restricting further loss. | ||
| OWASP API Security Top 10 | API2 — Broken Authentication | Approval phishing abuses authorization-like consent paths to drain assets after deceptive access. |
| Recommendation — Treat repeated approval abuse as a high-confidence indicator of a credential or consent compromise pattern. | ||
Practitioner Guidance
What to verify: Confirm that the cluster has both structural similarity and destination convergence. A repeated drain pattern without a shared cash-out path is weaker than a cluster that repeatedly ends at the same exchange or known receiver.
Decision rule: If the wallets share approval behavior plus the same downstream destination pattern, treat the cluster as higher priority even if each individual wallet looks small. If the pattern is inconsistent across accounts, keep it in review rather than escalating too early.
Practitioner takeaway: The best clusters are the ones that explain the scam mechanically, not just statistically, so prioritize repeated victim-to-drain-to-exit patterns over isolated suspicious transfers.
Related resources from NHI Mgmt Group
- What are the signs that a crypto transaction may be part of a scam network?
- What are the signs that an AI-generated crypto scam is being used?
- What are the signs that transaction monitoring is too weak to support crypto compliance?
- What are the signs that a crypto investigation is failing because teams are not reporting or coordinating early enough?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
