The Tag That Saves Millions: How Address Labeling Became Crypto’s Silent Defense System
In 2013, a researcher at the University of California, Sarah Meiklejohn, decided to test a myth: is Bitcoin truly anonymous? She bought alpaca socks, ventured into the darknet, and conducted hundreds of transactions. The outcome was devastating for the myth — every operation left a digital trail.
That discovery overturned perceptions of cryptocurrencies: Bitcoin turned out not to be “faceless digital cash,” but an open ledger where all entries are visible. All it took was attaching tags like “exchange,” “scammer,” or “darknet” to addresses — and the anonymous map would transform into a network of connections and crimes. Thus the practice of labeling cryptocurrency addresses was born.
What Is Cryptocurrency Labeling and Why Do It
Cryptocurrency labeling (or tagging) is the process of assigning a blockchain address — or even a cluster of addresses — a label describing its role, origin, and risk level.
Essentially, it’s a way to understand where funds came from and what “trail” they carry — whether lawful, suspicious, or outright criminal.
Why labeling is useful:
What Is “Tainted” (a.k.a. “Dirty”) Cryptocurrency
“Dirty” cryptocurrency refers to coins associated with illicit or suspicious activity. Its identification is based on data from a variety of sources:
These sources form the foundation of so-called “taint labeling” — databases where each address receives a mark reflecting its origin and risk level.
How Labeling Works: From Report to DisseminationWho applies the labels?
Labels are applied by:
What happens after a report?
A victim or service reports an address. It is verified, and if the evidence holds, it is assigned a category (“hack,” “phishing,” “scammer,” etc.) and added to a database.
How does this information propagate in the market?
Connected exchanges, crypto services, and swap platforms receive the signal almost instantly. If such an address tries to conduct a transaction, the system may immediately trigger a warning or block it.
Why does speed matter?
Because in crypto, everything hinges on speed. The faster a signal about a fraudulent address reaches market participants, the higher the chance of freezing funds before they vanish.
This isn’t theoretical. Over recent years, labeling has helped uncover and prevent several high-profile cases — from darknet marketplaces to multi-billion hacker attacks.
When Labeling Saves Funds: Real Cases
Cryptopia (2019). After the New Zealand exchange was hacked, attackers attempted to withdraw millions of dollars. But the addresses were labeled quickly, and as soon as some funds hit Binance, the exchange was able to freeze them. This was one of the first real examples of how fast labeling can safeguard assets.
Upbit (2019 → 2020). Hackers stole $50 million in ETH and began fragmenting transactions. One of the outgoing transfers triggered an alert: the address had been labeled and tracked. Binance got the signal and, within half an hour after the arrival, managed to freeze 137 ETH.
Why Even Fast Labeling Sometimes Fails
The examples with Cryptopia and Upbit show that labels can stop fraudsters and return millions — but there is a flip side. Not every story ends with a freeze, and it’s often not because “nobody noticed,” but because of how the market itself is structured.
Labeling is not a single global database, but a multitude of fragmented systems. Each provider has its own address database, and not all exchanges are connected to them. Exchange A may see a label from Database X; Exchange B only sees entries from Database Y. An address labeled in one system may be “invisible” to another, letting transactions slip through.
Scammers are well aware of this. They don’t try to cash out where the risk is high — they seek weak links: small exchanges and obscure services with lax checks. There, verification is weaker, and the chance to go unnoticed is higher.
Hence, even rapid labeling does not always work: the signal must not only appear in time but also reach as many nodes in the ecosystem as possible. Until that happens, some stolen funds will continue to leak.
What’s Changing: A Move Toward New Infrastructure
The main weaknesses in current labeling practices are speed and coverage. As long as analysts manually vet reports, time works in favor of fraudsters. And fragmented databases create “blind spots” through which millions continue to slip.
Market players are beginning to launch solutions intended to close these gaps. One such tool is AI Crypto Officer from Match Systems. The system automatically reviews victim reports and labels suspicious addresses within 10–15 minutes — a task that used to take hours or even days.
The key difference is openness. Match Systems is building not a closed service for select clients, but an infrastructure accessible to any crypto project. Anyone can integrate and receive real-time alerts about suspicious addresses.
That reduces the reaction time, expands coverage, and gradually shapes a new ecosystem — one in which security stops being a service and becomes a rule of the game.
That discovery overturned perceptions of cryptocurrencies: Bitcoin turned out not to be “faceless digital cash,” but an open ledger where all entries are visible. All it took was attaching tags like “exchange,” “scammer,” or “darknet” to addresses — and the anonymous map would transform into a network of connections and crimes. Thus the practice of labeling cryptocurrency addresses was born.
What Is Cryptocurrency Labeling and Why Do It
Cryptocurrency labeling (or tagging) is the process of assigning a blockchain address — or even a cluster of addresses — a label describing its role, origin, and risk level.
Essentially, it’s a way to understand where funds came from and what “trail” they carry — whether lawful, suspicious, or outright criminal.
Why labeling is useful:
- Understanding the provenance of assets. You can categorize transactions into exchanges, mixers, darknet, “tainted” assets, etc.
- Risk assessment. You can detect operations that may be related to money laundering or fraud.
- Compliance with AML norms. Labeling helps exchanges, swapping services, and crypto projects avoid dealing with suspicious addresses and fulfill regulatory requirements.
What Is “Tainted” (a.k.a. “Dirty”) Cryptocurrency
“Dirty” cryptocurrency refers to coins associated with illicit or suspicious activity. Its identification is based on data from a variety of sources:
- Sanctions lists. Addresses listed by OFAC and other regulators.
- Investigative records. Addresses mentioned in criminal cases and court rulings.
- Public reports and media. Research papers, publications, leaks, journalism investigations.
- Darknet markets. Platforms like Silk Road, Hydra, and their clones involved in trade of prohibited goods/services.
These sources form the foundation of so-called “taint labeling” — databases where each address receives a mark reflecting its origin and risk level.
How Labeling Works: From Report to DisseminationWho applies the labels?
Labels are applied by:
- Analytic firms that build address databases
- Law enforcement and regulators publishing addresses involved in investigations
- Exchanges and wallets tagging scammers in their systems
- Independent users and researchers reporting suspicious addresses
What happens after a report?
A victim or service reports an address. It is verified, and if the evidence holds, it is assigned a category (“hack,” “phishing,” “scammer,” etc.) and added to a database.
How does this information propagate in the market?
Connected exchanges, crypto services, and swap platforms receive the signal almost instantly. If such an address tries to conduct a transaction, the system may immediately trigger a warning or block it.
Why does speed matter?
Because in crypto, everything hinges on speed. The faster a signal about a fraudulent address reaches market participants, the higher the chance of freezing funds before they vanish.
This isn’t theoretical. Over recent years, labeling has helped uncover and prevent several high-profile cases — from darknet marketplaces to multi-billion hacker attacks.
When Labeling Saves Funds: Real Cases
Cryptopia (2019). After the New Zealand exchange was hacked, attackers attempted to withdraw millions of dollars. But the addresses were labeled quickly, and as soon as some funds hit Binance, the exchange was able to freeze them. This was one of the first real examples of how fast labeling can safeguard assets.
Upbit (2019 → 2020). Hackers stole $50 million in ETH and began fragmenting transactions. One of the outgoing transfers triggered an alert: the address had been labeled and tracked. Binance got the signal and, within half an hour after the arrival, managed to freeze 137 ETH.
Why Even Fast Labeling Sometimes Fails
The examples with Cryptopia and Upbit show that labels can stop fraudsters and return millions — but there is a flip side. Not every story ends with a freeze, and it’s often not because “nobody noticed,” but because of how the market itself is structured.
Labeling is not a single global database, but a multitude of fragmented systems. Each provider has its own address database, and not all exchanges are connected to them. Exchange A may see a label from Database X; Exchange B only sees entries from Database Y. An address labeled in one system may be “invisible” to another, letting transactions slip through.
Scammers are well aware of this. They don’t try to cash out where the risk is high — they seek weak links: small exchanges and obscure services with lax checks. There, verification is weaker, and the chance to go unnoticed is higher.
Hence, even rapid labeling does not always work: the signal must not only appear in time but also reach as many nodes in the ecosystem as possible. Until that happens, some stolen funds will continue to leak.
What’s Changing: A Move Toward New Infrastructure
The main weaknesses in current labeling practices are speed and coverage. As long as analysts manually vet reports, time works in favor of fraudsters. And fragmented databases create “blind spots” through which millions continue to slip.
Market players are beginning to launch solutions intended to close these gaps. One such tool is AI Crypto Officer from Match Systems. The system automatically reviews victim reports and labels suspicious addresses within 10–15 minutes — a task that used to take hours or even days.
The key difference is openness. Match Systems is building not a closed service for select clients, but an infrastructure accessible to any crypto project. Anyone can integrate and receive real-time alerts about suspicious addresses.
That reduces the reaction time, expands coverage, and gradually shapes a new ecosystem — one in which security stops being a service and becomes a rule of the game.
Blog