Markfxx
commented
2 years ago I look it up the address yesterday and don't think all the addresses are correlating.
They interacted with this address 0x80c67432656d59144ceff962e8faf8926599bcf8 which is the unknown bridge at blacklist https://github.com/hop-protocol/hop-airdrop/blob/master/src/data/blacklists/blacklist.ts , look like they control by different person but because they interacted with the blacklist contract its can make they have correlation
And i think bridging $1000 was a thing in some airdrop group/source, so there it makes sense if people try to bridge around 0.2 (look like its ~$500?) a couple of times.
rotate-eth
winning-sky
shanefontaine
⚠ Please take care with this issue. I was contacted by someone admitting to control 35 addresses in this list, who offered me a bribe to delete this report. They were using different accounts on GitHub, Discord, and Twitter. They had also commented here as
dexterethbut now appear to have deleted their comments. Just be aware that some of the sybil attackers are here trying to misdirect or discredit reports.UPDATE 2: One of the Twitter accounts that offered me the bribe has a DeBank link in their profile, for an address that is connected on Polygon to the farming cluster below. I ran a sybil search from that address and found one more eligible address I missed earlier. I have added it to the list.
Screenshot in case it is removed:
https://debank.com/profile/0x9679539722bd7854a7f715a8a720b6ac2c0e35a8
https://polygonscan.com/address/0x9679539722bd7854a7f715a8a720b6ac2c0e35a8
Related Addresses
51 related addresses:
Reasoning
These addresses are part of a bridge airdrop farming chain. They are connected by transfers on Polygon and Arbitrum, but mostly in a "chain" rather than direct connections to any one address which makes it harder to fully map the cluster.
The addresses have near identical behaviour which involves bridging ~ 0.2 ETH through multiple bridge protocols & blockchains, then transferring the ETH/WETH to the next address.
A detailed example is given below:
Methodology
I analysed the Hop user data, looking for hot spots with a large number of eligible addresses that transferred similar volume at similar times. This was achieved by binning the provided data on multiple axis, grouping, then sorting.
Looking through the addresses contained within my highest-density bin, I found the example address above.
In examination with Etherscan/Arbiscan/Polygonscan this address had transfers in and out to a few other addresses with apparently identical transaction patterns. Those addresses in turn linked to other addresses also the same.
I created a Python script to map the cluster by following the chain of addresses forwards and backwards on both Polygon and Arbitrum. I ran this script several times with different starting points that were manually selected from my investigation. Chains were terminated at addresses with large numbers of transactions (assumed to be bridges or exchanges). I then filtered the resulting addresses to only those present in the eligibility list. Finally, I manually sampled addresses from the list to re-check on Polygonscan.
Additional details and Python code can be provided upon request.
Rewards Address
rotate.eth