New set of 93 suspect addresses:

'0x1501a306356a9ca71f66985dbff6d1978e2c2c79',
'0x33482803274c5e08661843636dcee8e9a68c7252',
'0x89c505b005d1ca6d1f5d8533f906e0f0d513a61a',
'0x7a4c8e3655d665f3e25ccd6b60b1b024629b3705',
'0x2875403e0d69641cf631f10e48e63c40e4b27fc3',
'0x6c14cdc6b4b935614d2aa42635ed79b4617da0f5',
'0x8b1af9cf98dc5e8ac3c24b4dfefe019b2d3f24cb',
'0x4df599c19ec1c904994115b2c23329a3ef2fb928',
'0x98a97c80447bd8a5a84d487651728a98d26e85ef',
'0xac3c3c2c533619568bc34a331d3fb87121bf79ab',
'0x0388eaf12db6b8a977525d0ee0ec8f964f8aa9db',
'0x5840d518098a0790c18a5b4ef2aaf20275cab1a4',
'0x1a62e6f3fd7574666b84efd59763e807988829f5',
'0x1429bb62c3fc8cbfcbddf88e49e04bd93be00670',
'0x15bd6e69f1f895ecc2f253156f76c6bd069f5e3f',
'0x97339369814b53952eab6f3a8a37733a81df3257',
'0x1d88f5a58624688627fccd370dc8eac3a700c138',
'0xc9a5a7fda5f10f701e74902cade11b104fe30fbe',
'0xffebf6747d8b46852e552d0ffebe71d47ac9fcae',
'0x66787a9aa595a75168018bc9137c9907dd54483b',
'0xcff1e58f8286a69ee09c25aa47f153f2d4c6f588',
'0x616b141299d0786fa25907b1f879d489263f95d3',
'0xd27412131896b2defe03f66f4afce1e6d728d970',
'0xdaaa47da50c945bc97602cafe755cc2520ef2767',
'0xa56402fb07ece948941a04ca2ac8c3b28d28877e',
'0x0585c0d0aedf071a07992ad650bdc047cef0c04f',
'0x9e73201903164d3b476505f3c54f86cdcd4010df',
'0x7022f4cca0ab6632c6582c383635a58c10cd8206',
'0xf8b9130c48bfc12a3df4a6bd2aad9511d32d83aa',
'0xdacec2905604458e3208649682ab8d5f9f4a5edf',
'0x393d635b39f2351aefcf84cf5f8c755a038cb427',
'0x90fe5af1d4c992718641bccdef1520e45e78d5c6',
'0x1cfaf69c935e66e94162545a2e66a33804c5a2ea',
'0xd7d97a98065f1bfe284ac869cc249f76c03326ea',
'0x85c49aca0abb871e5c876e6e5d5395bd88fa3f20',
'0x7609250783e636240b1b8d6ce53614a18b101be2',
'0x3c9052e1e1594902b4a7a43d58321dfc097a1719',
'0xfadd0b6ae096c432fc4dc7fe0b93c959add05442',
'0x949ad06a4c904cd0b31ae2f56aae0ec68c16ccac',
'0x585f9da4aaae242c4b72201dd31bdf6bfc2b4b97',
'0xafa626ecbaa3e1970c689a3cbe0530017fa4f2f4',
'0x8286acba2bc29a288ed0f7310c32d6b800a70907',
'0xae680ed83baf08a8028118bd19859f8a0e744cc6',
'0xc1e84835e43d72d2de3c0f0026e264e5714e0443',
'0x831ae90da4ff946090992e98882ff16e79fbcfd6',
'0x60cfc61488b167365c91cfca7e615a7bf1ed8194',
'0x94649845604d943cf105220045054f8bb3e3ce21',
'0x69389398e4d903b02010e20d308ee58ea8d6d714',
'0x51007ed400611829e137170db0cb6949b8191fb5',
'0xa218d0ce0693c41a96f58667ba34f5f489e75628',
'0x2481fef92f6358a410f4b42a7672f23cac514c2a',
'0xd9bdbcf4c7e8bfc96686a4728c7873c4e20ab994',
'0x5be4797e82a4826664c8431af4be44b82d212299',
'0xda0a3c890603ae8248df00bdfecc49a94ce2dac2',
'0x52edb787c1473724f5d2b651229db1e2bd852b4a',
'0x93ea069784ba3580805ca976455201dff1cdc53f',
'0x9f1995bf9dc4c2e19d7bec35cb7387a435e5495f',
'0xb1f0b017a8a1cf913056bd813c3e1958045feca4',
'0xd59c44ecb7ac8ad708674871677465a4110a8cd7',
'0xfbe6915fb22dfe6b42a7c50b43b45bf692681e5d',
'0x99d305dfc38cefa91cb1129387681ede4c93e87d',
'0xb64057d1b26d50094c0439c21f787c5d605e83db',
'0x58caa9f6f5ada966b6bebc5b3335ee9e41f2ed0c',
'0x0fe805304e49e993b2c59287000a2726330d2055',
'0x075030bbeed2cb50fd90b66051024f35054063a8',
'0x8d5dfe87469adc2069d6e0676a1351ed5827065c',
'0x1537c0ee8ed1ed158145f898c2ddedacf2fc09b8',
'0x46671979c6188ebc1c795f6eae5ce209005f2dee',
'0x5ff99d2712c61382c1483c61251a705051431850',
'0x135af87eff1d773e3aef90a7c1c971872b75452b',
'0xe05cdf159909179018c59cee21356246891ef692',
'0x76a5bc9eb03be30b552330ad6faf73da4e71909f',
'0x37247eb611a592af4ca2d9f8d722810d13438f60',
'0xc799b64934ce1afb866ea3cb17716cf5400e4593',
'0x05699eb25a059e6db3610145e3a3f56526988847',
'0x9dae7a5b0ffd8c364d32136359fc91cd2a7cdc3b',
'0x87cab35e044b2e692341f4c6f767e4df58f38474',
'0x74aa8463874aebb1ae29be8f8ba1adc322a9402d',
'0x1ec83f24f016e6b455002f15e5a6933e9f199fcd',
'0x2f14c570932ce6d4ce87d510e629c628a27f6c10',
'0x48bc6c71f73f171a5deee36c59f8c51542504060',
'0x71f41fe15543f8e82e3268809b685481fbcab7b0',
'0x8fd1afef6b9245fa3abb02899320d37a7a12427a',
'0x336a2941fb2882e3c40f4ce79d25068579b82c12',
'0x347bb83e4ab465c8c6b7afab3beb373410a9cae2',
'0xa4ba02a9771d528496918c8d63c6b82bf14f7e2d',
'0x0725d35e752c8801e014a0ac1f0bc2b7fdf571ca',
'0x9fd9bb687696f19038892989876bd6dc96abd769',
'0x7d733f761559743f244766e402d0ea014c18d9fd',
'0x2553206fed22408e29c607470d679528ee902eac',
'0x96a85cd7e5d7a08f668054394f028290f57aceaa'

Refer to this repo for details

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0xff36b9cb75c9178841d8b75baf9776bfa59fbe9d
0xbf5310fae4d7a0c6df0452bcda09aefa2748ad59
0x39cc43b3068a208ae87042f927fb53b85586c654
0xb1f0b017a8a1cf913056bd813c3e1958045feca4

Reasoning

These addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x02b538a1927cea7015539e236172d2aa53c4fa59d245ff0c6d5cd97617895b1c 0xbf-->0xff 0.095E

https://etherscan.io/tx/0x0fa18850c93c2a216c459d77bc2631c6cd2a708bb43bb19ce029018c6a9079e7 0xff-->0xbf 0.015E

https://etherscan.io/tx/0x0e59407bf417f75abab81137840ca00d2c86e33faddb168a34625d3381a97e1c 0xbf-->0x39 0.064E

https://etherscan.io/tx/0x1fb18142cc676d062fdc7b08b18297cdeb69ce854e1063efdd57227dc4b89cf7 0xb1-->0xbf 0.05E

https://etherscan.io/tx/0x36e1c99d57dd70b3402a31440b1cd7d5670aa2409c3e6968bf9b20d96adaf803 0xbf-->0xb1 0.015E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

Related Addresses

3 related addresses.

0xbda0020b6e44db02ca73e65819ad33eea4a2111c
0x24621aa4dde07f04c57146d83468969010b7489e
0x4fecce1be339db44a4fd36f2fef18c6401ac2f8b

Reasoning

1. All wallets made transfers between each other on ethereum mainnet

2. All wallets used same pattern sturdy using:
   0xbda0020b6e44db02ca73e65819ad33eea4a2111c deposit 08/25/2022 502 USDC, withdraw 09/19/2022 404 USDC
   0x24621aa4dde07f04c57146d83468969010b7489e deposit 08/25/2022 504 USDC, withdraw 09/19/2022 406 USDC
   0x4fecce1be339db44a4fd36f2fef18c6401ac2f8b deposit 08/25/2022 501 USDC, withdraw 09/19/2022 403 USDC

Methodology

I looked at which wallets made transfers between each other and found the same behavior using the protocol.

Rewards Address

0x7d3f6048091aEdE6198736b8C6ac527c314e25ff

Related Addresses

0xf54d6f29a19bc8317fe966e0b8a57c28844176b2
0x3186a49932a0652f2c725bdc11f248fded8362dd
0x377cf7555fd49930412baae234424336b79b46a8

Reasoning

These addresses are directly connected on chain. This group may have been missed by earlier investigators as it is only clear to be a group of 3 when combining multiple blockchains into one graph.

Connections:
🔗 polygon 0x3186a49932a0652f2c725bdc11f248fded8362dd > 0xf54d6f29a19bc8317fe966e0b8a57c28844176b2
🔗 bsc 0x3186a49932a0652f2c725bdc11f248fded8362dd > 0x377cf7555fd49930412baae234424336b79b46a8
🔗 ethereum 0xf54d6f29a19bc8317fe966e0b8a57c28844176b2 > 0x377cf7555fd49930412baae234424336b79b46a8
🔗 polygon 0xf54d6f29a19bc8317fe966e0b8a57c28844176b2 > 0x377cf7555fd49930412baae234424336b79b46a8

Methodology

1. Load transaction histories of all eligible airdrop recipients across many chains.
2. Build a graph of their connections.
3. Remove sybil groups already reported in earlier GitHub issues.
4. Identify connected clusters.

Rewards Address

rotate.eth

Sybil Detection Models

We found several sybils in Strudy based on our newly released Sybil Scoring Product TrustScan. In the report, we explain some reasoning why we think these addresses are sybils; and also briely explain the idea. In the methodology part，we recommend to use our product at https://www.trustalabs.ai/trustscan . To use, you need to connect with your wallet and try the single query function.

Our team，Trusta Labs has won the Gitcoin Open Data Community Hachthon in Oct 2022. For the detailed methodology that we adopt to detect sybils, plz refer to https://github.com/0x9simon/slaysybil/blob/main/antisybil-V6.pdf
The TrustScan has implemented five Sybil identification models based on AI, Knowledge Graph Mining. They are Bulk Operation mining, Behavior Sequence mining, Chainlike Assets Network mining, Starlike Assets Network mining and Blacklist based method. The list of Sybils discovered on the strudy project falls into three categories:

1. Chain-Like Assets network mining：From the Starting address，funds are transferred one after the other. Finally, the fund transfer forms a chain-like path. All the addresses along the path might interact with possibly the same smart contract, in this case, the Strudy contract.
2. Starlike Assets Network: There is a funding resource address that distributed funds to multiple possible sybil addresses. All the addresses got funded might interact with the Study contract. Finally, the remining fund are collected towards one address upon the project interaction completion
3. Blacklist: We collecte the list of syils that are found in provious project airdrops, sucha as HOP, Paraswap, OP and ENS.

Related Addresses

0x2481fef92f6358a410f4b42a7672f23cac514c2a
0x39cc43b3068a208ae87042f927fb53b85586c654
0x7394dd6e970ecce75b1c36631c170d66aa172fc6
0x7faf03e5964c96c6a22de45f278c0e63aec6c11c
0xa56402fb07ece948941a04ca2ac8c3b28d28877e
0xb1f0b017a8a1cf913056bd813c3e1958045feca4
0xbf5310fae4d7a0c6df0452bcda09aefa2748ad59
0xd2033db9c5370ac76abd80823b5c5adc097e2fbf
0xe4fb9ca52e39b83be69e74e7fac8f807a9c16ef9

Reasoning

Sybil Addresses Found in Chain-Like Attack

• 1. 0xd2033db9c5370ac76abd80823b5c5adc097e2fbf,
• 2. 0x7394dd6e970ecce75b1c36631c170d66aa172fc6,
• 3. 0xa56402fb07ece948941a04ca2ac8c3b28d28877e,
• 4. 0x2481fef92f6358a410f4b42a7672f23cac514c2a,
• 5. 0x7faf03e5964c96c6a22de45f278c0e63aec6c11c

From the Strudy airdrop list, these 5 sybil addresses are discovered performing the Chain-Like attack. The details are shown in the table below, where every row represents a transfer with "Transfer hash". The sender and receiver are the transfer's FROM and TO addresses. Also, we show the time of transaction and the amount of funds transferred. In each table，the Receiver_address of Line n is the Sender_address of Line n+1. So totally all the tranfers form a Chain.

• 1. 0xd2033db9c5370ac76abd80823b5c5adc097e2fbf

• 2. 0x7394dd6e970ecce75b1c36631c170d66aa172fc6

• 3. 0xa56402fb07ece948941a04ca2ac8c3b28d28877e

• 4. 0x2481fef92f6358a410f4b42a7672f23cac514c2a
• 5. 0x7faf03e5964c96c6a22de45f278c0e63aec6c11c

Sybil address Found in Blacklist

1. 0x39cc43b3068a208ae87042f927fb53b85586c654
2. 0xb1f0b017a8a1cf913056bd813c3e1958045feca4
3. 0xbf5310fae4d7a0c6df0452bcda09aefa2748ad59
   The reasoning is quite obvious. We collected the list of syils that were found in previous airdrops, such as HOP, Paraswap, OP and ENS. These projects discovered them and made them public. These 3 addrress were found both in HOP and Paraswap Airdrop's Sybil Blacklist. With high probability, they are sybils.

Sybil Address Found in Star-Like Attack

1. 0xe4fb9ca52e39b83be69e74e7fac8f807a9c16ef9
   This address is found in a group of 547 addresses. At the day of 20220713, they were funded by the same 4 addresses and then transfered funds to the same 6 addresses.
   
   For a more detailed explanation, you can query using TrustScan.

Methodology

Our team，Trusta Labs has won the Gitcoin Open Data Community Hachthon in Oct 2022. For the detailed methodology that we adopt to detect sybils, plz refer to https://github.com/0x9simon/slaysybil/blob/main/antisybil-V6.pdf
Based on the methodology introduced in the deck, we built and released our sybil detection product called TrustScan at https://www.trustalabs.ai/trustscan （To use, you need to connect with your wallet and try the single query function）。TrustScan has implemented five Sybil identification models based on AI, Knowledge Graph Mining. They are Bulk Operation mining, Behavior Sequence mining, Chainlike Assets Network mining, Starlike Assets Network mining and Blacklist based method. We use the product to find the sybils in the report for Strudy project . All the sybils are fall into 3 categories:

1. Chain-Like Assets network mining：From the Starting address，funds are transferred one after the other. Finally, the fund transfer forms a chain-like path. All the addresses along the path might interact with possibly the same smart contract, in this case, the Strudy contract.
2. Starlike Assets Network: There is a funding resource address that distributed funds to multiple possible sybil addresses. All the addresses got funded might interact with the Study contract. Finally, the remining fund are collected towards one address upon the project interaction completion
3. Blacklist: We collecte the list of syils that are found in provious project airdrops, sucha as HOP, Paraswap, OP and ENS.

Rewards Address

0xC4137Ee33e81B181fB0a6511835eCef6a6740d97

Related Addresses

6 related addresses.

0x4e3e782053e560fdebd2be4a6c9a1f26c9a0faa4
0x7a4c8e3655d665f3e25ccd6b60b1b024629b3705
0x0fe805304e49e993b2c59287000a2726330d2055
0x75d25dfffff585b83b3f535b6ecad842fae3edd2
0xabc67a49d7d75bd04fa1dae22cd4b8c4f796540a
0xdaaa47da50c945bc97602cafe755cc2520ef2767

Reasoning

1. All wallets made transfers between each other on ethereum mainnet

2. All wallets used same pattern sturdy using: 91 days ago 0.10-0.12 eth deposited, 10-12 USDC burrow.

Methodology

I looked at which wallets made transfers between each other and found the same behavior using the protocol.

Rewards Address

0x7d3f6048091aEdE6198736b8C6ac527c314e25ff

Related Addresses

4 related addresses.

0x9ce91fec8421881b3643a2cb3fbd5b705bc40273
0x13fcb7b29f135d3740ca8c6e3f775df6994a3d8e
0xcbe3b686c1c8037892211b37f51d1d04d1bebb51
0xcea0b7f22aaf244e16298f309cb61c91aabbcfd5

Reasoning

1. All wallets made transfers between each other on ethereum mainnet
   

2. All wallets used same pattern sturdy using:
   0x9ce91fec8421881b3643a2cb3fbd5b705bc40273 deposit 1k USDC and sent 1k USDC to other 3 wallets Oct-20-2022 12 PM UTC
   

Other 3 wallets deposit 1k USDC from first wallet Oct-21-2022 11 PM UTC
https://etherscan.io/tx/0xebb089d7106ac13a2c7b80443b97ee44651932ca0bc459fb67424e2fe8c6f0ad
https://etherscan.io/tx/0x5e6699b34b519280e2f5413f06a09f5beb27d4ffebca52cf9d7fd1d9fa93f032
https://etherscan.io/tx/0x9a1d0ef3457fc1303c95031a47c34f6a19cf862ac0ec4da0a32c996806a21896

Methodology

I looked at which wallets made transfers between each other and found the same behavior using the protocol.

Rewards Address

0x7d3f6048091aEdE6198736b8C6ac527c314e25ff

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0x7553fa99ab1f429551eec660708c08e14e30584f
0xc79a1415847ed193b64d400c94a220bfb33d0e73

Reasoning

These three addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x1db645a422300b29c3e09fc8ddb94c7bf686abea379102ac1e6ec987f091cc5f 0xc7-->0x75 0.01E

https://etherscan.io/tx/0x586def818e254bd1ce31d496854af9e718f6da3cb78dce9691a6ae14e745ff26 0x75-->0xc7 0.1E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

Related Addresses

0x130111927e4d46b15919177e3f83869e463a815a
0xc8255af4091da943b8bcf665c2e2ea87e312c690
0xF11207Fb54DD02D5C52d2ae746e80D5FBEE1E522
0x1040a43093b700d9bfe90346990afaa4d1151508

Reasoning

Above addresses transfer funds among each other on Fantom mainnet, after/before interacting with the sturdy contract.

Methodology

Some cases prove the funds' transfers.

From 0x1040a43093b700d9bfe90346990afaa4d1151508 to 0x130111927e4d46b15919177e3f83869e463a815a
https://ftmscan.com/tx/0xbed1a7afc86d30dffaa7ea2a792a22ee1ba738909ad05fca36a9259a21f324c2

From 0x130111927e4d46b15919177e3f83869e463a815a to 0xc8255af4091da943b8bcf665c2e2ea87e312c690
https://ftmscan.com/tx/0xb53cf0638d7a1f61d60863d46969e317bd2df178b526abd43301fb2b0085e00c

From 0xc8255af4091da943b8bcf665c2e2ea87e312c690 to 0xF11207Fb54DD02D5C52d2ae746e80D5FBEE1E522
https://ftmscan.com/tx/0xe170b2e9d564cee030de83b4874187a0367f17c369b8397c5d85c743c9f80512

Rewards Address

0xFa4EF3fD6221698fF37754C39e734ED7d532E2f3

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0x93ea069784ba3580805ca976455201dff1cdc53f
0x74aa8463874aebb1ae29be8f8ba1adc322a9402d

Reasoning

These addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x3a23cc35fd3a5c75646c1dae7544efa91234ea94fe8e101c40e3e1cc703fdc1f 0x93-->0x74 0.016E

https://etherscan.io/tx/0x6014b016fc1e171b1ed2c10ec104861ce53c9131c5c56a9bf0b4c76512de60f4 0x74-->0x93 0.032E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0xabc67a49d7d75bd04fa1dae22cd4b8c4f796540a
0xdaaa47da50c945bc97602cafe755cc2520ef2767
0x7a4c8e3655d665f3e25ccd6b60b1b024629b3705

Reasoning

These three addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x5939134beeafc9488ceeffc41f68252445a4dd41b73b0fb30d45a9df60ee3425 0xab-->0xda 0.01E

https://etherscan.io/tx/0x63bbcfb686229871b02e16b992fd18c9c18af80a2980ca6d8d9d5d5aa34bba0a 0xda-->0xab 0.02E

https://etherscan.io/tx/0x8332698a668d708350c909d5a8ee06ab32daba68f51a4b058c9f17745ca9f289 0x7a-->0xab 0.03E

https://etherscan.io/tx/0xdb29906efd336f19cb58b33fdf157285e775ee152ce90dd8ed0681e8d6646f6f 0xda-->0x7a 0.05E

https://etherscan.io/tx/0xfc14e33fe1b91fa33963a4f0e1110639eeba8c4acf12f40f74e71f00fa3fd5f5 0xab-->0x7a 0.001E

https://etherscan.io/tx/0x24057f5e86b03a4669b7048a33f4d3d7507a23f50cea203ae42a77637e3812d9 0x7a-->0xda 0.04E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

Related Addresses

0x95de01e57e899723655433dbee53222a09331e81
0x588c1a695a2fedf02a237072ae1fdbad389466e6
0x3c8de8a456d5175e75f9eb42ada116fd201613f0
0xf410caa369133e30ee0567b3736d649452d79f93
0x472cae057b1843907b56292fc5c57c98e6eb369b
0x30bf85e1d0bc7fdedcbeeff39225d8037cb0cb7c

Reasoning

Above address get the initial fund from the same address 0x30bf85e1d0bc7fdedcbeeff39225d8037cb0cb7c via Disperse.app

Methodology

0x30bf85e1d0bc7fdedcbeeff39225d8037cb0cb7c distribute 1 FTM to above 5 addresses each get 0.2 FTM.

tx can be found in below link
https://ftmscan.com/tx/0xcc3f1daeb828b39d775a4703c7b07bec28299ed52f82ffb11b0e1eee136c3cba

Rewards Address

0x3363111f99768BC90Aa84962bec7F2f4ca0F392F

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0x0fe805304e49e993b2c59287000a2726330d2055
0x75d25dfffff585b83b3f535b6ecad842fae3edd2
0x4e3e782053e560fdebd2be4a6c9a1f26c9a0faa4

Reasoning

These three addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x07c22b36e66c7f35f94201900a5ffcc14d717d3b79547f39dba99343623f1fae 0x75-->0x0f 0.007E

https://etherscan.io/tx/0x17a6484a188aa743b371ac7d2126c4012456c6612e6a3cef37d9cf201093c75e 0x0f-->0x75 0.01E

https://etherscan.io/tx/0x1aac5b0ed1569412405c609462956d95620c2a0f248494a462a70eb13d7961a8 0x75-->0x4e 0.01E

https://etherscan.io/tx/0x56ac3f2036a5ac1cfed84a16110805a90b188ae1c9ff584fc8b8b6c6e4fd4456 0x0x-->0x4e 0.004E

https://etherscan.io/tx/0x6729bc12b3757a2ef3726d4c70bc690c14647bb380e5e5116b97e41daa2f8e7f 0x4e-->0x75 0.02E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

name: Sybil Attacker Report
about: Report a Sybil attacker of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: 'skyone'

Related Addresses

0x4fecce1be339db44a4fd36f2fef18c6401ac2f8b
0xbda0020b6e44db02ca73e65819ad33eea4a2111c
0x24621aa4dde07f04c57146d83468969010b7489e

Reasoning

These addresses have many internal ETH transfers, back and forth, and the amounts are relatively small

such as

https://etherscan.io/tx/0x45d185aee338b05d932e149482162708bebe7fe06511e1e27fe13dfbdaecb0b4 0xbd-->0x4f 0.03E

https://etherscan.io/tx/0x9dd4e9481d4dd8474d67d1426cfa760ca9b0701a0ecf36cbb1b199e0c569b8b5 0x4f-->0xbd 0.03E

https://etherscan.io/tx/0x6038e8e3ed8f2a8fd6f246565a594352c1c4f6d6544cf23b0043314cb2fd8325 0x24-->0xbd 0.01E

https://etherscan.io/tx/0xeb6b3ce5da6fdf348a1192d9d345c6f24e93861118d82a38af3c7de002ef694c 0x4f-->0x24 0.025E

https://etherscan.io/tx/0x0b66f250a6983a7fd0da08da6a1a20813ec358c1a40c44cf50f2d75da00af903 0x24-->0x4f 0.025E

Methodology

I just gave a few examples of txs, but there are many more. Due to the following reasons:

1. Internal transfers, back and forth
2. Very small amounts, all less than 0.1 ETH

it is very suspicious.

Rewards Address

0x8a09fb23e1c8d0ff9c9fef0bcfc9b9e07b1f0667

name: Sybil Attacker Report - Frank Maseo
about: Report 72 Sybil attackers of Sturdy
title: Sybil Attacker Report
labels: Sybil Report
assignees: ''

Related Addresses

0x94649845604d943cf105220045054f8bb3e3ce21
0x5be4797e82a4826664c8431af4be44b82d212299
0x1429bb62c3fc8cbfcbddf88e49e04bd93be00670
0x393d635b39f2351aefcf84cf5f8c755a038cb427
0x48bc6c71f73f171a5deee36c59f8c51542504060
0xa4ba02a9771d528496918c8d63c6b82bf14f7e2d
0x5840d518098a0790c18a5b4ef2aaf20275cab1a4
0xf8b9130c48bfc12a3df4a6bd2aad9511d32d83aa
0x1501a306356a9ca71f66985dbff6d1978e2c2c79
0x1cfaf69c935e66e94162545a2e66a33804c5a2ea
0xd27412131896b2defe03f66f4afce1e6d728d970
0x5ff99d2712c61382c1483c61251a705051431850
0xac3c3c2c533619568bc34a331d3fb87121bf79ab
0xe05cdf159909179018c59cee21356246891ef692
0x58caa9f6f5ada966b6bebc5b3335ee9e41f2ed0c
0x15bd6e69f1f895ecc2f253156f76c6bd069f5e3f
0x616b141299d0786fa25907b1f879d489263f95d3
0xb1f0b017a8a1cf913056bd813c3e1958045feca4
0xa218d0ce0693c41a96f58667ba34f5f489e75628
0x7a4c8e3655d665f3e25ccd6b60b1b024629b3705
0xdaaa47da50c945bc97602cafe755cc2520ef2767
0x52edb787c1473724f5d2b651229db1e2bd852b4a
0x1a62e6f3fd7574666b84efd59763e807988829f5
0x76a5bc9eb03be30b552330ad6faf73da4e71909f
0x8fd1afef6b9245fa3abb02899320d37a7a12427a
0x89c505b005d1ca6d1f5d8533f906e0f0d513a61a
0x97339369814b53952eab6f3a8a37733a81df3257
0x4df599c19ec1c904994115b2c23329a3ef2fb928
0xfbe6915fb22dfe6b42a7c50b43b45bf692681e5d
0x949ad06a4c904cd0b31ae2f56aae0ec68c16ccac
0x0585c0d0aedf071a07992ad650bdc047cef0c04f
0x9fd9bb687696f19038892989876bd6dc96abd769
0x99d305dfc38cefa91cb1129387681ede4c93e87d
0x69389398e4d903b02010e20d308ee58ea8d6d714
0x347bb83e4ab465c8c6b7afab3beb373410a9cae2
0x33482803274c5e08661843636dcee8e9a68c7252
0x9e73201903164d3b476505f3c54f86cdcd4010df
0xd9bdbcf4c7e8bfc96686a4728c7873c4e20ab994
0x87cab35e044b2e692341f4c6f767e4df58f38474
0xafa626ecbaa3e1970c689a3cbe0530017fa4f2f4
0xc9a5a7fda5f10f701e74902cade11b104fe30fbe
0x96a85cd7e5d7a08f668054394f028290f57aceaa
0x7022f4cca0ab6632c6582c383635a58c10cd8206
0x831ae90da4ff946090992e98882ff16e79fbcfd6
0x3c9052e1e1594902b4a7a43d58321dfc097a1719
0x1537c0ee8ed1ed158145f898c2ddedacf2fc09b8
0xc1e84835e43d72d2de3c0f0026e264e5714e0443
0x60cfc61488b167365c91cfca7e615a7bf1ed8194
0x1d88f5a58624688627fccd370dc8eac3a700c138
0x37247eb611a592af4ca2d9f8d722810d13438f60
0x585f9da4aaae242c4b72201dd31bdf6bfc2b4b97
0x90fe5af1d4c992718641bccdef1520e45e78d5c6
0x1ec83f24f016e6b455002f15e5a6933e9f199fcd
0xa56402fb07ece948941a04ca2ac8c3b28d28877e
0x2481fef92f6358a410f4b42a7672f23cac514c2a
0x05699eb25a059e6db3610145e3a3f56526988847
0xd7d97a98065f1bfe284ac869cc249f76c03326ea
0xd59c44ecb7ac8ad708674871677465a4110a8cd7
0x0725d35e752c8801e014a0ac1f0bc2b7fdf571ca
0xc799b64934ce1afb866ea3cb17716cf5400e4593
0x51007ed400611829e137170db0cb6949b8191fb5
0x8b1af9cf98dc5e8ac3c24b4dfefe019b2d3f24cb
0xffebf6747d8b46852e552d0ffebe71d47ac9fcae
0x2875403e0d69641cf631f10e48e63c40e4b27fc3
0x0388eaf12db6b8a977525d0ee0ec8f964f8aa9db
0xfadd0b6ae096c432fc4dc7fe0b93c959add05442
0x2553206fed22408e29c607470d679528ee902eac
0x8286acba2bc29a288ed0f7310c32d6b800a70907
0xda0a3c890603ae8248df00bdfecc49a94ce2dac2
0x7d733f761559743f244766e402d0ea014c18d9fd
0x85c49aca0abb871e5c876e6e5d5395bd88fa3f20
0xae680ed83baf08a8028118bd19859f8a0e744cc6

Reasoning

Same deposit time + same funding address

Methodology

check this repo

Rewards Address

• 0x3476613f6b2865aBf6f9fA621Cf018D810000f70
• frankmaseo.eth

Related Addresses

3 related addresses.

0xc79a1415847ed193b64d400c94a220bfb33d0e73
0x7553fa99ab1f429551eec660708c08e14e30584f
0xdfa0387f173904fbf36ad26b63279ac47624b55e

Reasoning

1. All wallets made transfers between each other on ethereum mainnet

2. All wallets used same pattern sturdy using:
   0xc79a1415847ed193b64d400c94a220bfb33d0e73 deposit 10/21/2022 2.7k USDC, withdraw 01/04/2023 2.7k USDC
   0x7553fa99ab1f429551eec660708c08e14e30584f deposit 10/21/2022 2k USDC, withdraw 01/04/2023 2k USDC
   0xdfa0387f173904fbf36ad26b63279ac47624b55e deposit 10/21/2022 1.5k USDC, withdraw 01/04/2023 1.5k USDC

Methodology

I looked at which wallets made transfers between each other and found the same behavior using the protocol.

Rewards Address

0x7d3f6048091aEdE6198736b8C6ac527c314e25ff