output.txt

003d6de1fd9804577f303e91f61d42dcea23e25108fdc016e0265e85bf07f41d 9a18d88b77feb119 1860
ac4bb8c708f5bb9a49a89be7e4aa221ae1f5d2109600812b145fd56de503347c 279ecbc016a8560c 1601
975c60c5181a8aebba667a1562f53b7a37c57ece60551f831af142d2b8d9f9d8 c2f2637845007e75 1692
1a6349ae83c6467dba4bdd4bde1c07c3907bcdafce8f2d9ec05a12b570d9eb58 cfcbafeffef345b9 1467
9d1258125e2e897002ee29ad099eb4e23b8da76d43c170ffa41dc399efa0d6c9 6e7b2f48617f8564 1229
9dec29e55a6eb0a284effa0e3bbc0efef91872da05310a1088503fed176ed888 b89486bc09c38c61 1920
52d0eb0efd064e70cb1ae9cbec5174b00f90c7e58ba89a3659cec562f7f71be5 141e9b7161860144 1064
710930217c19cfa75a82a21e16a52ee3165589a5c24abc3a23df03b5839e579a c69553508ed7fbe0 1111
e52b65a686ca851e8255864f944658680fd3d5a837bfb14e67b2c00caf888571 5c022b73e6ce4363 1944
4269a319a7ec7a19ff1c1824aa56b5526c55568b02f244e56fcba39089379628 814e95a34fbf3859 1609
32ad52c4328fdde92a9e9c28e33cab31ae674d2913e9725ba3bc03d31b9ac33f 5b4676f36646823f 1727
6f821a46038c479a25c46b6f2abec58f305b69f304fd9852367ab6e0e8b1a2f0 3bcb4476d3d48f1d 1429
e5331cddbf6c02db02fd8164c693e882d76be30a9338e22dad20e75045fc1217 534cbac864c76bcd 1263
ffb7b5cad153e42f4e1dbe2d0ad85c2ce4aee27688143bc4b85396a45cb579dd 470b1293bb2e3f4a 1333
ecc4d824ca24459566abe12aa28c5b44da8a12ea8a2c9130488795d10d33ab3a 7c90cf120edf3d25 1164
c329bf179639ce2ac936ffcb5baca18f75a9631047cf21f94def5998f070b226 d9575efb8a893cdf 1137
b05f2c3f6f22c57e0d4c7135c574b87b4410e50576d1da1aa83a553111c9f1b9 2a7bed46b51f20a6 1299
638485d8a57a36fb94d597cd49611f584cd83b844391174639a710c517eb663f 03f207868f81a9e5 1328
b914f879b5d905956034b833b0c1ea282d21ef6a12385ac75e2fca545ce374ae 33eeaff25eac1679 1151
5b30e6a805e2b805167607866dbad7830a649f931c0cc92b88722286e693398a 844654fa5afb16d7 1610
b47182bfe00d32b6b3c433fe400297aad484f24b08353b72350b85ea1f8800d7 9761d78297b1baf5 1600
963c4540b30a4d28a0d065d9f22ded2d856ccb9982d00c56bbb0422498a66857 ac5d36ffc019700f 1605
e5fe683fbf850f04b6ca7c458effd0efe4bf2a7a827f2782a44a49d1bc5d0da1 1d28afebe03aef2e 1108
af274dbe7b325517c264e57c63e5519e26b8a51858a0e4ac95a790cf11be78a8 c58ec693eb20076d 1723
cf171d82ae37908a5bf4ca5c5bc0e003201c080a35fd454885049c94c0479bba 88c8381ced5be61f 1987
0b2b2d70c9633ef8a3dd87b620b9a9a1a45a6d8c93ca61719dc29c74baac5ee5 83225ccf237b35ae 1797
46224708022b7042d88d51c93a3d29cea326d08a0784b63409c5d2a04140f18a bbac0dff81e02b45 1404
86fe6c06b6c81596865f4afd7c62c78e7382e312fcea70626cc1aef717c76647 7db9cfff410f3eae 1221
cd108682257de5e4b7ad02614e3dd59d1e8537d270fd62e668f318dc454354ed fad5e1ac4e7ef42b 1471
c9e84de0abaa7eae2d641b564d2a87aa53724cf6326a81eec0dbdf3a5a210a24 97d5030340aa0af9 1280
1cb6d2e8eba3146b479d9045434c57300f1d28d0e6f92f610d145060cb0821e4 1f9b501945078454 1887
ce0a09d890289782cc7fe5fcca16cd8f96b87b2d0d52a78e219db330e4b12c4c ff99cb405e9b31c2 1333
1d4b7cddbf4dc286b3ab8f950dcb23ff5c52e8871f751b4cf2cbd338373392ba 5b32f8fd136a66d3 1808
af3701a47520923ed485b8717a4caf29894a7bdc07a3f5f2d13af8d1dda809cd c50a434b8c657747 1395
f05bce1fd256966b426537853044814050177e72c95029b6a10dcf3d0f00749d e261850c9084d3cc 1584
d2e2da144deeb583a83d60c524f74bdfe18a072ab6d263b030399d1bf7a8ef84 59cc66cc1183e4c2 1961
642f3e3be31b999e77ccd51d54ab483799cd00fab9d11977b6c14cd2c8825d62 3fb78efe3e9572cf 1497
9a20c5e0ef3fb1f582bb3ad0633b489d48627e5ad92f02d29d8d4ffd02fd4fba c526454ab05c1064 1321
8ef9177436eaf2db703d15f1dfdd834c4f8f68f31a6817748b0bcb53bf3152d0 659c72485acddf83 1151
be681c4e87a5291019d9b2f8d07d91fa9f40e110313d4fae4d4d924de4c8c879 f9fa295f11575f25 1650
f08d7626860c49cb97b61e4764ca6fa9cb737700c0d7f1e2fe4e7257e1910912 023ef4e0b7f1bf33 1985
15e2021cad2aa994c8a4380803b50e6f7f37a4f7d4317b04abf2a275aa47194b db1e452ebf755c8c 1267
d5c6c44d36fac670e3829cf14228e2e06d5f9592e988986f5199590075c22fed cc69a96ed4c6b534 1989
63eb2660a0f801ce48c8415bb95133b6b11f8775d75ac381a580a40ce9bf5a10 801b81ad3b44ba45 1350
69b4dd6630fc9d8ac56a6044b5d072c343a1b4af01b32ced7123938f3f225e73 3cb57c6f41d451b2 1377
0347a7c3b9f0c8b36014eaa6a224fbc67fe52f046aa4ef8799ef0afddd50fab7 3750255337c726b7 1143
76d590fbb7eaeb000cc24f9d1b235edade99e2d35c0fc3975e0a3328dbe8eb43 15ac9295ce01e67f 1284

script.py

f=open("flag.txt","rb")
flag=f.read()
f.close()

import hashlib
import random
import os
f=open("output.txt","w")

for password in flag:
    iterations = random.randint(1000,2000)
    temp = b""
    salt = os.urandom(8)
    for _ in range(iterations):
        temp = hashlib.sha256(temp + bytes([password]) + salt).digest()
    f.write("{} {} {}\n".format(temp.hex(), salt.hex(), iterations))
f.close()