The CoinDCX breach of July 2025 demonstrated a sophisticated cross-chain laundering operation that moved $44.2 million from Solana through multiple intermediary wallets and bridges before consolidating on Ethereum. For security researchers, compliance professionals, and concerned users, understanding how to trace stolen funds across blockchain boundaries is an increasingly essential skill. This advanced tutorial walks through the tools, techniques, and methodologies for cross-chain fund tracking using the CoinDCX incident as a real-world case study.
The Objective
By the end of this tutorial, you will be able to trace fund flows across Solana and Ethereum, identify bridge transactions and privacy mixer usage, cluster related wallet addresses, and produce a comprehensive chain-of-custody report suitable for law enforcement coordination. This is an advanced guide requiring familiarity with blockchain explorers, transaction analysis, and basic scripting.
Prerequisites
Before starting, ensure you have access to the following tools and platforms:
– Solscan or Solana Explorer for Solana transaction analysis
– Etherscan with a registered API key for Ethereum transaction queries
– A blockchain analytics platform such as Chainalysis KYT, Elliptic Navigator, or the open-source Blockscout
– Python 3.10+ with web3.py and solana-py libraries installed
– Basic understanding of cross-chain bridge mechanics, particularly deBridge, Wormhole, and Allbridge
Step-by-Step Walkthrough
Step 1: Identify the Source Transaction and Wallet
Begin with the known compromise point. In the CoinDCX case, the compromised operational wallet on Solana was publicly identified. Using Solscan, query the wallet address and filter for large outbound transfers on or around July 12, 2025. Look for transfers in the 1,000 to 4,000 SOL range, which indicate the automated or semi-automated extraction pattern described in the Merkle Science analysis.
Document every receiving address. Each of these first-hop wallets represents a potential branching point where the attacker fragmented funds to obscure traceability.
Step 2: Trace Conversion to SOL
The attacker converted stolen USDC and USDT into SOL before bridging. On Solana, use Jupiter aggregator records to identify swap transactions from the first-hop wallets. Jupiter’s on-chain records show the input token, output token, amounts, and the DEX route used for each swap.
Calculate the total SOL accumulated across all intermediary wallets. This total should approximate the original $44.2 million value at prevailing market prices. SOL was trading near $177 on July 12, so you would expect approximately 250,000 SOL accumulated across the attacker’s wallets.
Step 3: Identify Bridge Transactions
Cross-chain bridges create the most challenging tracing scenarios. The CoinDCX attacker used deBridge to move funds from Solana to Ethereum. Bridge transactions appear differently on each chain. On the source chain, you see a lock or burn transaction. On the destination chain, you see a mint or unlock transaction. The critical link is the bridge’s message ID or nonce, which connects the two transactions.
Query deBridge’s frontend or API using the source transaction hashes. The bridge maintains a mapping between source and destination transactions that allows you to identify the Ethereum receiving addresses. These destination addresses on Ethereum are your next targets for analysis.
Step 4: Trace the Tornado Cash Trail
The attacker’s initial funding came through Tornado Cash, and the proceeds were likely laundered through the same mixer on Ethereum. Tornado Cash uses fixed-denomination deposits — 0.1, 1, 10, 100, and 1,000 ETH — making it possible to identify withdrawal transactions matching these amounts.
While Tornado Cash’s zero-knowledge proofs prevent direct linkage between deposits and withdrawals, you can apply heuristic analysis. Look for withdrawals that occur within a short time window after the attacker’s deposits, originate from fresh addresses with no prior history, and are immediately routed to the same consolidation wallets.
Step 5: Build the Cluster Map
Using your collected data, construct a comprehensive address cluster map. Group addresses that share behavioral characteristics: funded from the same source, interacting with the same bridges, or sending to the same consolidation points. Tools like GraphSense or custom Python scripts using network analysis libraries can automate this clustering.
The final output should be a visual graph showing the flow of funds from the original compromised wallet through intermediary addresses, across the bridge, and into Ethereum consolidation wallets or mixer deposits.
Troubleshooting
If you encounter addresses that have not been tagged by blockchain explorers as suspicious, do not assume they are clean. The Merkle Science analysis noted that Etherscan failed to tag one of the exploiting wallets in the Peapods Finance hack. Cross-reference multiple analytics platforms and use behavioral analysis rather than relying solely on platform tags.
When bridge transactions appear to dead-end, check whether the bridge has a fallback mechanism or whether funds were routed through an intermediate chain. Some attackers use multiple bridges in sequence — Solana to Polygon, then Polygon to Ethereum — to create additional layers of obfuscation.
If you lose the trail at a centralized exchange deposit, the trail effectively goes cold from a public blockchain analysis perspective. At this point, law enforcement coordination is necessary to obtain exchange records identifying the account holder.
Mastering the Skill
Cross-chain fund tracking is a rapidly evolving discipline. As bridges add more chains and attackers develop new laundering techniques, the tools and methodologies must evolve accordingly. Stay current with blockchain analytics research, participate in bug bounty programs that include forensics components, and practice on publicly documented cases like the CoinDCX breach.
The $285 million lost to crypto crimes in July 2025 alone demonstrates the critical need for skilled on-chain investigators. Whether you are a security professional, a compliance officer, or a concerned community member, the ability to trace stolen funds across chains is a skill that directly contributes to making the crypto ecosystem safer for everyone.
Disclaimer: This article is for informational and educational purposes only. It does not constitute legal, financial, or investment advice. On-chain analysis should be conducted responsibly and in compliance with applicable laws and regulations.
the $44.2M CoinDCX trail going solana to eth through bridges in under 2 hours. response windows are shrinking while laundering complexity grows. bad combo
the Solana to Eth bridge hop in under 2 hours with $44M is wild. by the time law enforcement gets involved the trail is already cold across 3 chains
compliance_kat the gap between attacker speed and defender tooling keeps growing. cross chain automation for compliance is maybe the most urgent infrastructure need in crypto right now
Extremely detailed breakdown of the transaction flow. Mapping the jump from Solana’s Wormhole to Ethereum L2s is usually where most trackers lose the trail, but your analysis of the liquidity pool imbalance is spot on. It’s a stark reminder that cross-chain bridges remain the most vulnerable link in our current infrastructure.
man it’s wild how fast these hackers move once they hit the bridge. i always thought solana txns were hard to track compared to eth but this shows everything is visible if you know where to look. thanks for breaking down the mixer signatures, definitely going to be more careful with which dexes i use for swapping after seeing this.
the speed is the scary part. by the time you notice the breach the funds are already 3 bridges deep. response windows are measured in minutes not hours
response windows measured in minutes and compliance teams still using excel. the gap between attackers and defenders keeps widening
chain_whisp3r the minutes not hours framing is critical. most incident response plans assume hours. bridge speed makes that timeline obsolete
44.2M moved through multiple wallets and bridges. tracing cross-chain is still painfully manual even with elliptic and TRM. we need automated tools not spreadsheets
Lena Ostrova agree on the manual part. elliptic and chainalysis are great but cross-chain tracing still requires a human connecting dots across 3-4 different block explorers. automation cant come fast enough
the wormhole bridge hop from Solana to ETH L2s is where most trail analysis breaks down. liquidity pool imbalance tracking is smart but requires real time monitoring