Author: Zero Time Technology

AI Mirror, Revealing the Truth on the Chain

The decentralization and anonymity of virtual currencies should signify technological progress, yet they have become the "invisibility cloak" for gray and black industries—money laundering, pyramid schemes, and gambling platforms have constructed layers of disguised financial networks on the chain. Traditional manual tracing is overwhelmed by the massive volume of transactions and multi-layer interactions.

The rise of AI is reversing this situation: it can automatically identify suspicious addresses, penetrate multi-layer money laundering chains, and even counteract the AI tools of black industries.

In early June 2026, TesseraDao on BNB Chain was attacked by hackers, minting 99 million tokens for sale and cross-chain laundering; a verification vulnerability in the Syscoin cross-chain bridge led to the minting of 5 billion unauthorized tokens. Black industry methods are rapidly evolving, and AI governance has moved from an "optional question" to a "mandatory question."

Part 01 - Four Typical Schemes of Virtual Currency Gray and Black Industries

The core logic of on-chain gray and black industries remains consistent despite changes, and the following four categories are the main targets for AI strikes:

1. Online Gambling Fund Inflow and Outflow Models

Gambling platforms use virtual currencies for deposits (mainly USDT), and gamblers deposit via channels like USDT-TRC20. The platform aggregates the gambling funds through a large number of dispersed addresses, then circulates them to laundering address groups to evade bank risk controls through rapid inflow and outflow and significant fund accumulation.

2. Money Laundering Score Running Models

"Score-running" gangs use personal wallets or score-running platforms, recruiting accomplices under the guise of "part-time collection," splitting the illegal proceeds into accomplices' wallets, then transferring through mixers and cross-chain bridges, ultimately exchanging for fiat to complete the cleansing. The funding chain displays the typical characteristics of "scattered inflow → concentrated outflow → mixing → cashing out."

3. Staking and Mining Models

Project parties promote "hashrate mining, passive income" and require users to stake mainstream currencies. They claim, "the more subordinates you recruit, the higher your hashrate, and the more you earn." After accumulating the fund pool, the project party directly runs off with the staked coins. This is common during DeFi booms, exploiting users' blind pursuit of "mining."

4. Wealth Management Wallet Models

Using "arbitrage across exchanges, AI smart wealth management" as a gimmick, they issue air coins. Users must register by paying a fee through referrals from uplines, and earnings are linked to the recharge amount and the number of downlines. The platform manipulates coin prices in the backend, creating a facade of profit; when the funding chain breaks, it collapses. Such models are often packaged as "high-yield wealth management apps" viral spread in communities.

The image below shows the typical money laundering path from the initial fund wallet to final cashing out—multi-address dispersion, mixer obfuscation, cross-chain jumping, exchange cashing; each link increases tracking difficulty.

⚠️ Features: Multi-layer jumping, cross-chain dispersion, use of mixers, increasing tracking difficulty.

Part 02 - How AI "Tags" On-Chain Addresses, Leaving Gray and Black Industries Nowhere to Hide

AI acts like creating a "criminal file" for each address—how much has been mixed, where it flows, and who it is associated with, all at a glance.

1. AI Automatic Tagging: One-Click Classification of Mixers, Gambling, and Black Industry Addresses

AI automatically extracts behavioral patterns of black industry funds through machine learning (frequent interactions with mixers, entry and exit from gambling platforms, rapid aggregation after multi-layer transfers) to generate risk scores and tags for addresses. For example, an address with high correlation with dark web addresses or heavily interacting with mixers would be tagged as "high risk" by the system. When you receive a transfer from that address, your wallet will pop up a warning, helping you avoid pitfalls in a timely manner.

2. Clustering Algorithms: Taking Down All Addresses of a Gang at Once

Black industry gangs typically do not operate with only one address but spread out across hundreds or thousands of wallets to form "address clusters." AI's clustering algorithms can automatically group these dispersed addresses under the same gang based on transactional regularities, shared mixer behaviors, synchronized operation times, and other characteristics. In a $27 million hacker case at the beginning of 2026, the attackers used 50 different wallets, but each wallet sent a request to the mixer within the same second—this type of "synchronized" behavior is easily recognized by AI.

3. Full-Link Penetration: No Hiding Even with Cross-Chain Jumping

The biggest breakthrough for AI is "full-link penetration." No matter how many chains the funds cross—from Ethereum mixing to BNB Chain, then from BNB Chain to Solana, AI can connect these isolated transactions into a complete flow diagram of funds. Even when funds enter mixers like Tornado Cash, AI can still reassemble "drained funds" scattered across different addresses after they exit the mixer using time sequences, amounts, Gas fee patterns, and other characteristics.

Part 03 - AI Can Track Multi-Layer Money Laundering: How Far Can It Trace?

Humans may break down after tracing to the second layer, while AI can trace to the sixth layer or even further, as long as the funds have not entered the mixer black hole.

The image below shows how AI automatically tracks the flow of funds across cross-chain jumps—from Ethereum wallets to cross-chain bridges, then to Arbitrum, Polygon, ultimately marking high-risk, medium-risk, low-risk addresses and exchange exits.

🔍 Upward Tracing: Finding the Source

Tracing where the money comes from, judging whether it originates from known scams, gambling, or dark web addresses. AI can start from the target address and traverse all upstream transactions to draw a complete fund source tree.

🔁 Downward Tracing: Finding the Destination

Tracking where funds ultimately flow, looking for cashing out exits (such as exchange deposit addresses) and suspect-controlled addresses. AI automatically tags all downstream branches until the funds are pooled or enter a mixer.

⚠️ Technical Boundary Reminder

Once funds enter mixers like Tornado Cash, AI temporarily loses the path (a "data cliff" occurs). The effective approach is not to ignore mixers but to re-associate after funds exit—combining time-related correlations, amount models, Gas fee patterns, and other multidimensional data to reassemble the "drained funds" scattered to different addresses back to the same starting point. Currently, AI can achieve partial cross-mixer tracking but complete decryption still requires off-chain intelligence coordination.

📊 Practical Effectiveness

In scenarios such as cross-chain analysis, mixer tracking, and score running identification, AI has shortened the analysis cycle from several weeks to several hours or even minutes. However, the "last mile"—matching dozens of suspect addresses to real individuals—still requires collaboration with off-chain intelligence (such as exchange KYC, social account associations).

Part 04 - The Black Industry is Also Using AI: Forging Addresses, Concealing Transactions, How Do We Counter?

The black industry is also upgrading its arsenal: using AI to generate fake addresses, forge transaction records, simulate normal user behavior to evade detection, and even bulk-generate phishing websites and scam scripts. In the face of "AI against AI" warfare, the defensive side is implementing three layers of countermeasures:

First Layer: Counteracting Forged Transactions

Merely looking at transaction frequency and amounts is no longer sufficient. AI risk control systems analyze dozens of dimensions simultaneously, including transaction time distribution, Gas fee payment patterns, address association depth, and interaction protocol diversity. Real user habits are chaotic and inefficient, while "perfect" transactions forged by the black industry actually become identifiable characteristics.

Second Layer: Counteracting Clone Wallets

User level: Cultivate the habit of "not blindly trusting search ads and only obtaining links from official channels." Platform level: Security agencies have established a multi-chain phishing website blacklist database, and mainstream wallets and browser plugins can intercept malicious domains in real time. In the May 2026 spoofing attack on TronLink, the AI system detected anomalies through code similarity analysis on the same day it was listed.

Third Layer: Counteracting Clone Wallets

Many security teams are beginning to use AI to generate simulated attacks, proactively detecting their system's identification blind spots, and optimizing models accordingly. The speed of vulnerability discovery has increased exponentially, dramatically reducing the "window" exploited by black industries.

Part 05 - How Can Ordinary Users Check if an Address Has "Black History"?

You don’t need to become an on-chain detective, the following steps can quickly help you determine:

✅ Check Tags on Blockchain Explorers

Enter the address in Etherscan or Tronscan; if you see "Phishing," "High Risk," or other red markers, or it shows frequent interactions with mixers or gambling platforms, directly reject the transfer.

✅ Address Health Check Tools

Use Revoke.cash to view the authorization history of the address—if it has authorized many unknown contracts, it may be a phishing address. DeBank's "address analysis" feature can generate risk reports, labeling fund sources and destinations.

✅ Self-Check Behavioral Characteristics

An address created recently but frequently sees large inflows and outflows, has a single Gas fee payment pattern (always using a fixed rate), transaction times concentrated in the early morning, and frequently interacts with large mixers— even without clear labels, remain vigilant.

The principle in one sentence: If you receive an unknown transfer or participate in a strange project, take 10 seconds to check the other party's address. One more verification can prevent a pitfall.

Conclusion

The essence of the gray and black industries in virtual currencies is "using anonymity to carry out money laundering." AI is breaking the information asymmetry upon which black industries rely—while black industries use AI to forge, we use AI to penetrate disguises. In this "AI against AI" battle, what we ultimately protect is the asset security of every ordinary investor.

Ordinary users only need to remember: do not blindly trust high returns, do not authorize arbitrarily, and check address risks before transferring.

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