Bounty Hunter Report 22086 URL: LayerZero – Cross Chain Sybil Addresses (commonwealth.im)

Report Summary:

This guy claims that got a final list of 1.5k addresses that were flagged as sybils, precisely, 1533 addresses. The Methodology is walking through these addresses’ cross chain transactions, identifying transaction interactions of each of cluster. They did quite well, but they mixed some “innoncent” addresses, I qoute innoncent, because, these addrs are just cannot be idenfied using their method, u can let them go or use other way checking these innocent addrs.

They offers two files cross_chain_sybil_addresses.csv and

cross_chain_transactions.csv which can be downloaded in the report.

This report lacks data to backup his full list of sybil wallets. Many Cluster IDs in his sybil list “cross_chain_sybil_addresses.csv” don’t appear in his dataset “cross_chain_transactions.csv”.

The report also clearly lacks manual verification by ignoring submission criteria and including a large number of families under 20 addresses. The dataset "cross_chain_transactions.csv” file doesn’t even include addresses that somehow make it into the final generated list. If you were to replicate this now with his dataset he has uploaded in the report “cross_chain_transactions.csv”, the final list gets cut nearly in half."

Their Mistake:

after replicate their code, I just found 855 sybil addresses with 75 clusters.

this is the top10 cluster they show in the report:

this is the whole clusters after I run their code(added some codes for neccessary)：

The number of total sybil addresses are 855 rather than 1.5k！

Here is my code below, get some edited based on the code phaal348 offers.

import matplotlib.pyplot as plt
import pandas as pd
import networkx as nx
cross_chain_sybil_addresses = pd.read_csv("cross_chain_sybil_addresses.csv") # new node
num_likely_sybil_addresses = cross_chain_sybil_addresses.shape[0] # new node
num_real_sybil_addresses = set() # new code

cross_chain_transactions = pd.read_csv("cross_chain_transactions.csv")
G = nx.from_pandas_edgelist(cross_chain_transactions, 'source_user_address', 'destination_user_address')
connected_components = list(nx.connected_components(G))
connected_components = sorted(connected_components, key=len, reverse=True)
cols = 4
rows = (len(connected_components) + cols - 1) // cols
plt.figure(figsize=(cols * 4, rows * 3))
for i, component in enumerate(connected_components):
    num_real_sybil_addresses.update(component) # added
    plt.subplot(rows, cols, i + 1)
    subgraph = G.subgraph(component)
    pos = nx.spring_layout(subgraph, k=0.15)
    color_map = []
    size_map = []
    for node in subgraph:
        color_map.append('blue')
        size_map.append(50)
    nx.draw(subgraph, pos, with_labels=False, node_color=color_map, node_size=size_map, edge_color='grey')
    plt.title(f'Cluster {i+1} (Size: {len(component)})')
plt.tight_layout()
plt.show()

print("number of sybil addresses:", len(num_real_sybil_addresses)) # new code
print("innocent addresses:", num_likely_sybil_addresses-len(num_real_sybil_addresses)) # new code

here is the screenshot of code, print result and plot graph :

Disclosure of Interest:

My Address 0x216A1f39d4EC2436E1D5E4244432Fd291E06de2D is marked sybil, and is one of the 678 innocent addresses, ironically, I cannot even find transactions involving my address in the cross_chain_transactions.csv file they offer and acoording to the code and result, my address are not one of the clusters they present.

please download these files and their code, run it, verify the result. U’re gonna find they have a hidden agenda.

furthermore, I added some code to export the real sybil list which can be downloaded from this url, acoording to the sybil rules, I deleted clusters including addrs less than 20, if u don’t think that’s ok, just delete the corresponding code.

the real sybil list :

0x55ccfb2916e84973c884db9da9cd711e02c3514d
0xd4bc9e23a3c788e696564b99a073f8b40d4254bb
0x7a659211f7ad21518a6defa420dda74106e0daeb
4AzxK5W9NtpRBUsZPWCz5Wj7ockPwQdvix4sPVH1w7cc
0xbbbba9ebe50f9456e106e6ef2992179182889999
0x38451ee0b382bc1292d95cead8c0890d9e90380e
9mf5mPVhcUDMVmyV19Gj8TY8NRh1tK3Sqacz3q8WrFmv
0xf64fa134212e20f666e1f9c7beaac90c9bc5a49f
0xb7388e4f58b555ab65fe7ace315f54b649482743
terra1h2tzx4qznhj2yhumyx3lxx9j0p5yk6t7prk3rm
0xa541af332315b4bb71c836dde88ff3797dddbed2
0x8dda2bc4c32b1eb9fbacf53950e029af24292631
0x8951dbd19e70a732cb856afb968ddfe297e7babd
0xf5db0faed38352416867eae99fb11531b64ec335
0x23c0ec36fc45ae1d4fba4e011193ff6be8199199
0x05315352489a97c50b80dcde4b566469aa25eb24
36nJWrEM38BjcKiPdThxBMy7m5L3vyEZvxqpwbPkkbgM
0x27868e8b084e46d7987a9a9762ccd1119dcf6de2
0x8a22d9919d826bcea15ae138dc8f614199e6332c
0x1582cd409436616480d1619671ed0207608fe928
0x750df4a8d557204c7dec1e4700a247f2d6ed3b60
E345MSomSVFgDSzJjEU6zYp8Gw9DuKGyvPjjXScqhNh1
0xcaf58be688d2e6b3339a36c8b20331a005bab5da
0x6cc6c3dd36668fca15940a8541b3f0f9330f8888
0xf03fee12ed1c42b1760ece85fdbdd817eabe90df
0x3f4c7a297895082c58002037f76b1cd263289e8c0307fa4658b5c3a134ce523c
evmos14vmfwkseq0g54g283ckv4w7s5lpy9z0pl87tkp
0x1ac1fd3b50a4df3ba28de731abe135bcfc0593b6
0x38954a4e0025375715a464a541dd30a7b49b9e72
0x8803f6b6138c1121f1296ccb919c62798ada95d7
0x5916fa4bf86872d250abbd286e71e72bd8096a3b
0x6e59f2776a4c953308f41909660f7d998f1b5eb1
0xb17f37452ccf77e56062256831d506ed36bd962a
GYMFPX2YdiRAF2KzHC1v6Pz6mhgK81adiLwPcYtRKPFL
Gd2cDx9RhnYCnNVRYFx8PHbg3ywGCxJiqUNaj87AsLq6
0xee13ce50e256fde66ebec93e09c7104c7b46620d
0xd2f7bb62504fbe33e3038bf2fefaeab42bad88b3
0xbeaed0288a59e9a7b55457c06ba2806b7ac31a07
DH34LNX7XUCK5LDVS2FTQLELHGQOIYG7XZMWAZZLRPW2ZZK3KMSJJU3EIQ
0xcef4aa0b2beabab79742079611b99d61ed2ad482
0x858e396ea8b5834b826e63dacef139bc897bc1e5
raZb6ort5CP36JF6wiyRpGxJvY9KCKP6z6NBiZX796X
evmos1t0xayymqnf2yvysvfe87q95jscdvdpv5qc82uk
0x9a128aa7be7beba1207da9955821f831dd0455be
0x6f95bebc13974965c8f2193dfda617f25e8c2d32
0xa191220ae1b794a74334684ec4ff26892c855615
osmo17pdh8relx0lafzs8x9v3rxesjla5ctxle7w348
0xac5ba492657713d8d74955283051c7750cf651b6
0x27885dccbdb3f6284091a08a20104b20f8105994
0x8b5c2f71efa2d88a20e0e1c8edfea3767b2ab230
0x26f4bb3f91868004ddec6d36f618aafc2660d787
0x64ef03b7f3ec30fd875f7280e6b66928dcba92b3
0x1543337c118b65c61883162fd4ddb0472493a3e5
0xfe5428e46616c5c833add93b5a7f743bddddaf44
0x4d8c47c1674e799978b6616d16cda65d9d720ff9
0x6b80145d2a0ecd4a45b144443ba2e1bcff551aba
0xad3f97f61af9d418218e06e68355c3d893c7abe0
0x019bb70cc458a678ab6ea0c95963f3c0885a1a34
0x98a18dd06d989c28336dfc8951d34667f0fd62e8
0x500f6378bdfb101f9380d35098838f7643393d5e
terra1j5dnr9sjv4573d6cunagy5kd5vas5pj9x6ss75
0xd66de285cfc16cd0f1db8b16883af5f8d1d88410
0xb0b43081bae8781c22120d5a9d1cd14ae9a91607
BM9a3gAmpjKpgh9jmGiwafjFbCzGAjEaWogSSc335BSp
0xb6aee3d9b3ffd0e8edaff38d82e94efcebc5d1f82e9f459531c3196f017f5a2c
0x600e6fe06d6b29d0411258f4d17eb03fbdac2806
0x4861c074eaf91faf914cadfb8af6f0fb8208cb11
0x5f9ce5b754ffe87740ea07c6579250fa74af23be
CQLJCBqv4cnomwSGWJc4uivTqR93dgLF9zodHn6u5At9
0x637116edbe0ee6f90da09fb16410366b2267b3f1
0xba0f5dd97bd9febd3bdae3b436e307b7156fb9c8
AgDPVQX3FknSkUpCpgYL9FT5ppNN1uEWNppDkYWB1Nso
0x9362e95fbcbcf0f53ac5beac6f8468528f0bd1c0
3oXDEWYh4umc9LBYmmGVSUmRPCMHpSaf5HsyXGy6WzEz
0xecfc21db1ca16df8b61b7e86b4c876540511e716
0x9913e51274235e071967beb71a2236a13f597a78
0xc367477c2a8c79e457d90270e22c721082311e47f54e29973588cd339a275750
0xd1d648a7732f8bf19e4754c4010ed28a4d71e476
0x6cba242a95bc95de2d26a8aabee9e54e2b3a4657
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0xfcfbf8bec5d046bb8f543c83f5db5d322b62fcc0
0xb088c3ad5e23f87b803c58a456f7bffb31cc1018
0x57d761c3cbae64b5364a851f1bd4b55bc98cecd2
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0xc8c0d994e9c7776651b95acb01cc78afcb1060bf
0x8ec348aa16d745ab810ff8fb2a350c9ab6bca54c
0x0eb9a7b36e0bdba8fd9dd0fb2bba39625ebbbe40
0x1dfc5e3c691d6723743263d174f586d7e75bc893
terra1vs2p2kg8kphq7zepx77g8q92xdly6w6k2wklla
0xacf402e9b45cddf69778b1de8f68533dcad1a4d3
0x47f22ae47be3237b5161fe3f8362a52db4086ca1
0xc4a357f937672c447682a11bae788f6b8a18c5e6
0x5e5f3f573f23bd77c7947d810a93c65e23c8f166
0x285bc2970e54d53ea2fbe4b0e1bba2677a205742
0x6c602d086b89834ae44773a01201d8d2a2c724ba
0x527908f9bcff298a84a22ad7cb3564b0d878e8ae
BtXcYsb16Dcsi6JE5zagoCYNzZpHPBDYTnizWhnWqm9A
0x84dd05060d20f2fc5b17e0f231e4e7ad08441bcbeca8b036a2e7e017d5daef1c
0xc0d9397597ab85dfa4f82f3a69e2eefd246a6d6c
0xafd7229854df48034ecbf5f4db9da8b0074f5dad
0x00ce2cc9f206ad8133632212fe8a504ad65c304d
HBkZfFT1utfKkQPwquTt6jxK3NiLiJbe6MrRHVQoNwjf
0x68b7e843d2df64fa8a80395f4cbbc13c6dde3561
0x6a593b33e3c7575f3f9a9e2f9296754631cea390
0x4b017a945eef1b4a14841e0e9be4eded7cc67054
0x67fb984cea10bac6e04a1024271c62281af62d80
0x183184708bd111e1eea5a78a747bd8f7cdd7f957
0xd41c53807ce5a9d21aef2f2822354cc7d9e9ebc3
0x8abc2230ec7789d93445cf8a01d012f4cdb71c36
0x9740ab6e2a619b591d5d2e0fdf01ee667a5b405b
0x1590df1f895571aaacd42b68d7208959ad65eed1ba1865f45d339edaca12136c
0xe57c654f3ab2677e2599bcc6ab668a08b478b02e
0xdbddcdf222c3344e55d0cd3a0461c1bed29d1b00
0xf60ab3ecbe658a023b752d193cdcfdd234ac69b3
0xd2F7Bb62504FBe33e3038bf2fefAEaB42bad88B3
0x4c94614a8e219e88029471558c000b80a4b09f36
0x7fcd3d4e2d8a26ae77cbb3cfbd433dc3020a34be
0x8c0d73b9ae8b6f47d90c7a6c4e4e2998567de113
0x53b80a8c810eeadb766f6866debd01c05e3a7ec9
0x1d7ea2c7e464b5db9d7ec87f01c8e09a1905dc36
0x0c76380828e29fec312ce8cce6d25390a7fc03d1
0x76844ec7c127fd3689d83f63651a92887d850aa5
0x12ef8d415e1169665e074d5b6b65e68fe41bc67d
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0xb1fa5b2e1fd6adaa5142b423716a1f3a70ffb142
0xef42a911ca28df1055914eeafe3aebfb6c29ef13
0xDe58dD01040B8e1C686145D002B9C390db829EaB
0x21135f8f429b1d911ddcc632386d5df8ff27f1b3
0x0e48493e7c75d65906dad957ccff5f6ecc531c05
0x67424bb924da5451dab541db55974bf752103f31
2ESsoLAaxGzhFY3Fh6AaxVQPi5YBeAzvr64UcWJkXCCd
CXRwptH1bKgo6qKtVkVipH28Fe2dE5pY7Rd9jzga5EZZ
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0x117724ec81e0dae807aee926b1fb29062240ef46
0xbed3a20d29f2af79bf4f5b3c7f34e94548418ee1
0x015442db46488116388a4cf39b0e32c2411751ab
0x44dbcdfe6c7f9443b85d0cd167a4dc8022294c13
0x9417b4c7d3a89f086551364b8ababb8eb40b2426
0x8e1c7cfa5f91384a8b571a79eb63d533891707ef
0xe4a8dfca175cdca4ae370f5b7aaff24bd1c9c8ef
0x2e5fe63e5d49c26998cf4bfa9b64de1cf9ae7ef2
0x7d5b401c887117a567f2f334976e34fdda73176e
0xa9a3018f2a807efca8abc70226c53ff09364e9c2
0x98e048cb12fb91ac90d67105fcd94e2444017f58
0x5bcafb388197945f356b78e696257bff626e461a
0x49c2be2949b228d9cae1f27f9050d19e5edadde7
0x2c3043d0ae68a57342c0c15836c41449a2434012
0x66459cccbe1318f8eeb7c8b73e31ac200b5ef3e68cbd3df464a1a8e665d7c71d
0x654ccc508186540e30669e10b9f68c7495d57c9b
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0x4f6420e54191389555d33c0850e8ec66dccbcd45
0xea886cdf03bc6db3a629c1b229d01daa191baa4f
0x3e1fe4d83614c5e9de94abee1662aba643feb4d1
5yY452VgCnrVP5jJLZJuz5vpuX2fsWuAaKbvSt8sVTG4
0xb2b0999834b062ec425462bad311cfec987df904
0x30bd407dc6e561adc1e374828b7ae2b97d090bbc
0x52832e4e13bf9164d8e60778bfb81cf30ecd6bac
0xee21ec068fdc79a5785aa0e6d0e29bbc4b511a44
5RHWJhBoRPywy1vDBKdF1JrgEzEjpcX5yXDtAt8QJebx
0x9d27fcb22824239f26915e4b56752613b89cb743
infer from tx hash
0x8f97c8d8c08648f8539dd21a16895dd5622d2645
0x2e11ba7e203711f2ef0e668ac98203b5c5f35a9d
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0xccfd1ff80d176f1b98f7e40ed71a5b13d70daf69
0x807bb22b9e07df0c897e8949bad34754bdf4b857dab1a380232280e0cfac33d1
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0x656654b1178b1af2e1638857876480cb8014b494
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0xee895cff7933d3f201de6b69ee40eb2edaa07697
0x49b144d329b096d2772533f936b5fda6ddf8dbdb
0xe17162277128ca50be54f5d1980d6014901631f2
0x0e48f8dfe82997de5618738c4d79b7a873bfd2ed
terra1ph8rhrua297gruz2p8ztu9n6vlk7emdmrnxlmx
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terra1a9kut050ghawle3vs05kesl5a2tyvxhdc2d05a
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0x468ca71166d4fa9a00edda08defd08885cae0384
0x1546c295324c5d77dd798bb7cf6a855b3f89edfe
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Here is the full code exported sybil list:

import matplotlib.pyplot as plt
import pandas as pd
import networkx as nx
cross_chain_sybil_addresses = pd.read_csv("cross_chain_sybil_addresses.csv") # new node
num_likely_sybil_addresses = cross_chain_sybil_addresses.shape[0] # new node
num_real_sybil_addresses = set() # new code
sybil_file = open("real_sybil_addresses.txt","w") #new code
cross_chain_transactions = pd.read_csv("cross_chain_transactions.csv")
G = nx.from_pandas_edgelist(cross_chain_transactions, 'source_user_address', 'destination_user_address')
connected_components = list(nx.connected_components(G))
connected_components = sorted(connected_components, key=len, reverse=True)
cols = 4
rows = (len(connected_components) + cols - 1) // cols
plt.figure(figsize=(cols * 4, rows * 3))
num_real_sybil_addresses_filter_20 = set()
for i, component in enumerate(connected_components):
    num_real_sybil_addresses.update(component) # added
    plt.subplot(rows, cols, i + 1)
    subgraph = G.subgraph(component)
    pos = nx.spring_layout(subgraph, k=0.15)
    color_map = []
    size_map = []
    for node in subgraph:
        # if len of subgraph <20, ignore
        if len(subgraph) > 20:
            sybil_file.write(node + "\n")
            num_real_sybil_addresses_filter_20.add(node)
        color_map.append('blue')
        size_map.append(50)
    nx.draw(subgraph, pos, with_labels=False, node_color=color_map, node_size=size_map, edge_color='grey')
    plt.title(f'Cluster {i+1} (Size: {len(component)})')
plt.tight_layout()
plt.show()

print("number of sybil addresses:", len(num_real_sybil_addresses)) # new code
print("number of sybil addresses (filtered cluster size < 20):", len(num_real_sybil_addresses_filter_20)) # new code
print("innocent addresses:", num_likely_sybil_addresses-len(num_real_sybil_addresses)) # new code