Case:GBC-17/124

The Greedy Brothers Corp. has approached you, young wannabe, to solve a pretty nasty issue. Many machines have been infected by a mysterious ransomware. The IT department from the Greedy Brothers Corp. have found a dropper in some of the machines but no track of the actual malware… except, of course, the ransom notes all over the place.

The company has paid to release one of the machines and captured all the network traffic during the process in the hope to get some further information on this attack. The IT guys did they best to clean up the traffic from the other infected machines around. They have also changed all IP addresses in the dump for privacy reasons… at least that is what they said…

However, the technical guys at GBC believe that the ransomware actually does some more nasty things.

Your task is to get a sample of the malware and analyse it to figure out how to take it over… What would you do whenever you get control of the malware?.. :astonished:

Hints

This is not intended to be an easy challenge but I’m pretty bad assigning levels… so I may be wrong and it could be a very easy one. For that reason, I will not provide any hint. You can ask for hints whenever you feel you got stuck. I will publish them on the comments using the spoiler tag.

Fine some hints then:

Hint 1. You can extract the TCP streams using wireshark Follow TCP stream function
Hint 2. The malware sample is encoded in the first stream. It is a XOR encoded ELF binary… those ELF headers may help
Hint 3. You will have to reverse the dropper to extract the payloads that contains the other flags. The key is calculated with some data from the server and some data from the client

Hope this may help

Rules

This challenge has multiple flags. Get’m all!!!

Post the flags on the comments using the spoiler tag.

Notes

This challenge does not contain any malware. There is no risk of getting your files crypted or to cause any other harm to your computer. All malware functions are simulated and completely harmless… they just print the flags to the console. However it contains network code. To avoid any unintentional damage please, in case you want to run the program use a controlled environment.

Also note that this is the first time I try something like this so I hope it all works fine (haven’t spend as much time as I would like on it). I haven’t had a chance to test it on different OS configuration so, please, let me know if you are experiencing any issue.

Finally, note that I haven’t solve it myself :scream: … I think it is doable tho. :sweat_smile:

Your Case files

Grab your case input files with

cat text_below | base64 -d | tar xz

and get started!..

H4sIAKPBb1kAA+28B1STS/AoHvVevdcCIiIqUrwoqAhIRxBQUUFpKk2lRDpIiUJCh3ivIk1ARbqA
vdEEpJeoCEiR0EECRGkBQhEQAoQk//1CooErl987/3feOe88R8lmZmd2Z2dmZ3e/bCImbmHmYiWq
dlRVVEIOLiEpLQ773w4HAMjJyEClhJzMAdaSCTAJKQkpKTnpA7JSErADEhJyoBCQ+d+vyr8B5YI0
cxYQgFlZOpr9F99y9f+Xgti//L8IF7tsYXb5/18fkINlpaWX8L+0pPQBOYb/peQOyED+l5aTlIUJ
HPjfM8T/hv/H/c8f/Mf8m1VD7X+66zcVyVFXXUqxa+reGGKxcc3pY78dcbwccOZ5iKrRhgvBr0/A
GysFdM+sznvhOHy1s/7h84DNj8yT7j80sE1JubXP8LfSqQDe15eetz2U53qS/Q+tyKDm76Hwnnay
8qhXIplGmxjzUB5JRk3NDmcRwg8dwhs3rMpBfXSI+zSLz44Rq6C5efeMT4hRStli2noIbZqzGB3r
1VaS5agZXdTTWU59cYqRUMNuW9M6n4o/CRTqP6/KClPd8EUzHzSfErP5VTCOLTqRVtrTJYIPMFWy
OAf+ffAzGc5CnPv4ScFHe0c0Yg1vFg+9TxNqIhKG27R7jz0YlznZG/v4rJZXBNnNY2fNTNbDgy3C
TeK2H1wP2rhgH2dWNb98YG5xdizo8TuuYf25C1HcFqVkXMq523o7IlOnPx1BdcwiFbNSR9b5zPLV
5Th8mP7S7ZmIrPhax/ny0olmuHRXdZqJ+5cPX7oLNsh3PfftSJ2b+9Qxu1skws/ukJ5zuWmwsMUH
++P+Q/9Y7stUaEtXuxN0LXztNVySu0OUSjmvV55tubXjp/SSBi41TX7kyeezJwY8yjgP9DS59h0s
eOThpL1P+Zae+/2iILfCLTpvbv05bLB+onCviQS6P2yoefSDYIEP9XVGpn9YnLy3sX5OpngKDR4F
51BRGgzwVXPj8Zy6H+5/T6b/rpmVMFzZ86jzP5460dLW7vo5f5ak3asl1P/56ATbkGvdk8nuMvbE
y3O38t8WhXVktA2UqMElv9S/dCoK5yT5SanwTN3vun2Hu1djTD1NVKE+M7zoXEedhAf1VsYmwkiW
+8iNQj2kpgHioJARtiPj9JV3D1purtMX7z9jcClBvNxDNkcKa1x3SLXJ6kFX9OOujJ0xz3O7bvmb
Bnsl5L2yTch713R5SqdhohqpKOvySQYRn0PIwIlUxW1/pBTXpzeSsB89ZeniwF5BYvfoHRQlrreL
NTn/qdH4E2YgKqJg97ADcTI2VrfqWpTs00hybwtOMF3D3L0YPV3f1+jsP+fp1zuSEvlpDisKeOJc
Ql5YeTQ0GZUNb8ex95DxGqIkOC76QjYf99SVAm6jNlxR7v4sbzkV2qRGQ/dUdZiissrI9NysX293
1XbdqndXPu0e7fMuipsof/Rqzty3SDz7gsr+NbwZ1qemZ6ZlRIe7W6SfRYaPE7X2bXc7JemU0zi2
37FgbjxzZhiFNDb9OGpodl4erU9SQjX9zZPGozKV8kxY22M6jHKCQpz1XeNU6NrTMynVdS+7Ks2r
GcWjW36lfO5eQU9a/NcZ8T54blW+WnH+WJ4KtlMZR3yGvzQ9naJKqk4zkkf0iuLwaYfwaHe8as7e
LLTGJlxOA9p5DaXctRbts4ZCQNbiH12j8ir244uvUdNca2mP2GZt3GrRp9ZQNvB+Q6deoxZsWtU2
wI9/OxuFrKW9YJsNG7pPu8E2y99/n1bJNksavY+2NMc9heunwnHORdwj+PGe2YRc6ieJVXjEjoMe
neHhxDiDm9GFXwcqk7akbfe60oAQhmfYNj0f90gq047BII/NXMOG5eba6U6WBsc91Y7L/BZk5+Ir
5TjoEyJI5FZS8jjVIBWeVoieCRg/XvUsxNHT2vtskvPgu17BSzyJ0c4npx06EeJi8EZp2RmHtsdk
Ay1RDPrjXYlgA8NQtO3k+9mZEMPQ84VzGS/cZLTjX3/+VNHrIhuS6ImrfxV/ZtIlIUYWZdbdOvu7
SFkUPs9zPNqS3D1iIpM9RZVpEDtf+CJ/zggR7+JU8rRzFT5O36OZvXOra829bT7stp6TQxOYsVOo
sEmpWVf7taSewkLcBUr7w0I7X0WfL76Fop3hfoRyuZpBjOE5uahRojNJtyBSmDi3E1/8mV1n+EzC
TcfKNAzVL7J7x8hAkVG8/7sxbN9ohChJqxjd8jQFm3DxcBJVyWiSH/8SHWZAWZMziH+ijx41oKTg
1VQvTOb4prLZN2Hsk6hHUmZJ9k3oE0lUH6NJdJE+2iqJmnRhchTIuSdRiy5Mhl1qgtfNFts1YXSS
qEZGkznKVLxTE8YkiUq8MInAvaTZpMwa4Q3Dsgdpx5KoikaTboD1lT76qT7NJWU2x66JZp5ETU3B
akzGeLEpTqaSss/j3EduOjYPFQ20PkbPrkC3iTeMz+Szv3TbWiudPNhBSE1tJ/v1VaLS/KaCvn5a
M8ovN9SLSjPZ1kb2n+pOVxJ6XULuu0YwJ9fXdsq9Hr05vO6oV2It9byD4fWpzgeU4daaG1OmD3xU
HdCSr0WOerk72Bz1wtZSPR34edq8rk/db1WWfF2l6qXuQFo/3KFK/tw6sqWtwG/qaivVx3Z0exuu
dGqwtnP36yoLsspRRZ62ND7+tjC/qZorNAsy/4Mi3XZaq6/wa7ebZH4cesOwTjklbDhKlawLVT+t
FQsGZKmjXv65tJPk+4Kd0ZNxRi0f2+QpV9FFChPXXHwLPvol8tW/HiHlxQQXX1D2mR7lTnZIbf8W
ZdkugamI9BHOEwstmMVX2qQQuRPWlbzEFFa9asOa5hPaZwu68vi8vwQY9o2QZq/ieOC+6N5HKqEt
dW8MiG6U8eKKWULNQMYIlh/92olW/iwMTfnnCT/mMjzX4CnpnD66upxQ8ynVaq5sbppHdlRX9v6L
sQxHm5S4Se00blNTt/Zy9Ac2uLf5TIZ48cbUKLMmlWJ7clZIxKT4CG9qzOUQn5eeg00mxZ13k9Ht
ubFhkbtc4DO2vWok4arTFmX5efydUpiMwuzxtzmhPK4j9wJNaBkjuajM7TcSSOP5Bc0mmCp7vM5g
ctykn6kUfLurdWLUHVRq4hWP8U+P4cOcszQ9Re9u34orcVLuNMv2d4k1rVIzDr0j2Gofc1NJrJFp
4WeEfw11IL3ElE8W0Z4nF+9h9tyHcjP4DuopydP1nKRrmuVQu/yUi5f4TbKP7cenBc28xartTs5o
b1JSRf4a6sDL3pAIm5qUNK3QloGSSzVzDuMO9ybDqyziC9CRjfyKlFqfUG/Bm6OTvVqjJrnpGvca
Sk92qNqoxHkr2+DaOKme7a/OSz29LuE/QskjpKWux6HH31s6ZXHfUyQNYHwvHW54kInm67ug6NyM
8LJqEXKh1GmZUXS9rlu42Ljz5Xg3q3i7+rj2lggL4ciUp/lp1z0Ko3ckhuXsSLSk3XEsJuX0RHci
+zqv8D2Y8qnq3lFio0MTTeYZFJ6QH7h+AF+VXe02cvALIQb7ekdLaN+V6bZX1HeRFNzeEXzNlSK8
n4uHSTQu4opTJj+7zJow7UuTQUQE1dfX+KlVzL1EYnfrgEZioqJ8tVuH242Ugv72vGvlfokkbJYD
uf2gopCMy8mPqMlMg6apXGLKO+X1xV8zdosSd00oWBE9lCejD0Sdx2mNNjjzNk6dS3iNVrGI68m8
XOzllKtMPeQzIFWiPTym2Vqmha22b8ghO61De359ofRu0k1rpBY+QnNHfSwuH852GuxuYI8v2tdo
Iivm5dVQnYMwcX0zRLgzNSJK5NtMGmgR3jzVPavrpXRlZKe4DyG6p3Pr7WHUbNJJfLsN4mQqLU7Z
94GnZxpaCEuI928q/jb7SheOVjLmx5OrX0V1o85n97Ypyh2kEmpittTopBUkhpCRmvJsujfiFTCD
0+5Opnj3WlmuL9QCUW/uXMU5pybiB/ehRM3mRBnHC+Mu3aU4baV+XdXYS2HaZWWE0j747vIh3bG2
GcrToag7xIflmDtYIYrrZa0MamFl3pin6XQ44Y6Hj0RMy3QbX1zXmEKmkKkccBsG5+zj6EvONctM
JJdem3DyUPawtrrjqftOsc8lcYfroIQoIUXSiVhh2aH2DqXg5jR812x0bt+IKoUzSwcT880EQ8nv
Uaa9stb6cn5M3dug2sHDAKmTTX6f5xJV3u0xcs/+Uph7aktEWopiarnGJGIXqRzBt55UnjhKtsjo
SHo3Okf2yamS45FL4Ai1c2/mn8rsUUId7v5YUUgynqO4EJQlzAgrKqR2yw20VOSTb7i1Fyq+a6dt
6ksA09mOXwj/oI8i8U1J9lsvylBGaNSPsq/KRtbJ2+3zq6jRPzH2Oo7tHqd1h4MmK3w2hVKUwnbj
ErYXczaPfHPNVSmOHXJB9cqFzxiGevti4kq8+VL2VRM+9ygpeLu6J7wnuH3xIDV9m3CatHlX1CbO
N4EQJta09pUWqcwaZIREFGCpavWdZCJ3TVCLL6o3LXxMVfxcHr7vVfyRih0qnjvPpz3BOI9KFett
m1JxQIRFFOE1sj85Y/VaRjBk2aKuQ31sbNjuAt/TkYdGrY03JZpkIv5oMZ70qyAFlLkgFIRrutWC
3xUVduf3dOJadyTgpU5cR7HbeksPVZW4+Xy1HfeyK27GuBkT+x9eIL1u3I4/S6w5Z983KVXT6YwQ
057qznZPvtM97k57bKCBz6uQmYzo677hc/lyRgdCh58vUeILgh/Da8yVSrnM69s7soIdnVXYooXW
qiHyFfj78Oc/GTEZSxh17h8idmUOZPRQRYOxD7gNR0w9iPWrzNHOYTSB86O35EdauF3l0zopJRPf
+iNtzrZGJb2T2TSF6PXyksu6V2Km2vLRk/ryQFok0S7qgPXlHZyJEmb40cDRZz2dh56L8RWodRep
eHdvW9Xli4/Cj7o088k5dBbb37FHaptQE5I6yGkiPEazxUbcCdiGCSyB2+JjpP0UPvwKf3ts4Rrl
l2yjhQ9JF1VNvNlDL3mwa7eR/VS7SKX5xJaaC26TN1z61p8z8vId5oIfHFjj85LPZ3ywcnDAB2xb
P/Nr0ghHuIjUuZby5o8zX1+0rYNrZyt2B8iYrO3GJilNDh1cZapxaT25MbyB3d2MNJDjkPu5FuWS
4DNYEpvPn5byogDlOBmriG7IeUW1j5lzax0MJhk3bPZtsZvxsW8N4fcuHiyyJ693J6n5rDYm54y/
nTBEO1Y/jNPe4a3TcLeCPUVx+HFMC6/y1w6iziGXMiLBkPZ18sEz5ZnK6L3BUWEpaPuPpSUEPNKc
MqcS+jzf5mPuSVqu56B+SJJa6Pm57rbwYso1vogNSWXNWPjBrlb+/fyKTmNB19YUymQWOvfFwfd5
JyoTY9m1ky/wEPklvhYlNpoeQqMw+Yp38vmLD/m+KxTGyE9ml5eF5dEGS1YXS3W/P9NS7GUeNlkx
/dSmXHOuYpzDJzYyNCbj8kzjLDsPNvfil6busmTH7Qcv1r2UmCSmDSte7GsIF27HFBbOkUUwsl+K
nz1df256LlekL035yni8IVzZ7ExDbg+p4wy3yid36kk8tWQgvHPmS69gSypRyu4tmTsS6w62nGto
yK5Xv+uEJc7VNlWMZsnt8D3eUJbesCU1H1nZ9sQitVBuhlfhBZzazD7Li3vQk0ulZl8T6kX7X0Bb
VinaNtaVmBbLnA89mZpDKhnlqpDJhzujm4SxaZcTTcL3r9ldhdiNfXamYny6R/G0x/hInth5No3I
JDaTczOZj9FNNlsMlLkjdZRPz/hSR6388ChX2oxiRIHK+BuqopILIQFv6kqoeaAyp4VskCgxVU5v
GpUYTsOf+ySH8R21rJG73pbEJ49IVKHIFNBU3Wi5c492r4mfrpEp979iVvRVrlQj3GctOnMvxvNt
jVRUcQbRqYG91Q0bPJqyZ85pe/O6crMWy0Qdk1hTRFjMhCtu8kXk5DtZzG15aY+yyKxUNxeNlNOE
7PccFQTEZkQa3NNL5wZ+Z7iFR5XFcVxSnmhWkckdiuL0Kz9RDImrmXxGI+WrB75nYvjlluw5X+Pp
OZyRCs18fCZJR6l2hPCl9aaWMv8tbxVeTmVa2T/xNuI+PlG82NzhKnLycCaW28AHbe06+4+yFD88
R2N0UBHbRojTGa0nD/7uMwjqWpXt4Z1W5ZYUF8MOXmwDNlyh5I5CCo266HHKG9q9W/2eOgM3Ww3N
TwyJOEqZI57bf3CSe1x0s9WNXSSGg0fXRmoSVPMMrj2ffXxbehX1WRjHzx7MvEFH3erPt0m7E5gq
ISjrckShDmUiJpRvvTt/5c/Y49XUkUpPkQ/tXTSRlEv76lDHUCaayJRb/Wt/xo3k21k32VgfmGpv
8ZBoCEq5HvOH9gYPRX7GXOtx/VhLPmBCBwamloAS0Vh6rGVtYOrPmA8TODaZYM4q1E02Rfd7+gJF
LFHLqE25MHCspcX8IbEW+ZCYnB/df2FnHdfPmPsPHX1IfMYmXtVoKXnmykP7p+iovlv9Fyadd9Zl
eRwn7d75EfXNiui84RPvoCxJUzj/RpAan6mk4NRkNAe5F3liiCcvmiMugmPdkhaJhgYZEphKeLLJ
RC1ej5vTRGTXf5j7AdAXD/SOAmKZzhJ1MiL/offeJ5wmLcBuLg3AgM3LGFBHPL5/9oQmUkk4ol9h
9rGxJvLjKeTvP2NXCLvVP/uxpEpSJkZT715/7ADeylEdmeL54lZ/W+FD8p1bfQqeIBRUXcsz5eZt
orIvnoNsXR3Ae6cmoChdzPq9wWNJ8zjL9z8dJw29ZZMJYm98f76yUH4zGAK+GqiefMzwZ8z3J/4I
TK1a4B8MCJ2XKtybTOIMkfBN9cZqYbk7W2512L3pSwBOPz32on5bOqL0Zmum7rb0qJZG8C5Idlu6
Fq2h/I+f9fAZo6GO9MkGdsdJ1E0mgzIkL7o/NuI/oryD1Ta9TVC8mLPES4Dc4PYMxQ2t7VIR/fn7
9bwxIE4UJQSngsEcnURJCGYfPbH/Z23TJwU6QhvpIyeU77sTuOmpEIiXzE0mK37CfbVGcFf+CK5g
v254xbHSwNQYqonpusBUffHNLJZJrpGxdNv+Ks8GTJrzz899KrA8MeQFVJkAqsQNmp9w8Lt5bkmz
rwGqULxAxEhBvlpGk4TGBZqwA03Q+3bly6b1F+96nBfKdzApdWdBw9WPSrrAn0dLdU/ZvHcK2JZu
AwKlPMGYW8pcZIlwoU+LIuAWMnDPN1BqLeee8U6rmFhNoX3ayI/fRsdbIPeUH31oPxE0onE0lth/
G9HD6p4dcAXBqWvRHF4XQJmSH81htFNwy8/apk+5x2Be8lg8tHcEBm2vWE6TvgWa5ECa+LNocka4
idU98jkgUFRAjieACJZrAYG7dlv6z9qmz+fRPWA+mwHvyIDSYLl8iBrN0NQS4uQSzreedTsPJk+S
L1wd6Wha53NS3YVX6ZLBAu88BQrQbm5LT9Syeh+q8gSa0B8Cls4VZ0E0yQG9J8DahE+2/zDShgFr
Ux/USNC29GsNYDQEEGsG/xH2YSAeirdJmbe8rgjg9TkLtCk/M2z13jp0TwwH+QzILHdrAnwKllHk
wpVjFWCKgfjOywZ9NstYkkZaTE8MFT+RMpcD5tQ9uy2dU7sCvEv+j7DXB9x4oDfUBqJvewaZJFoZ
4LiWK0w8nqPgoPX7hHs1AfEmy0QsBbJJjsWJodE2y/emprott4Z9Lb22patq2Lz33SFlbg8lqeWM
IgIZhVPKHI2TFZwqM1aduuUqZS4cVR3gowSoOqes3vv6nDapDki5tfQChLIGirgBRTKBc1paZCzZ
iQ3AJCFgkAXvb7YOgpAbMq4O+Fj5H/49DJogAf8OgjxqcQkshoKhtuKCU1/AGExAy4FQD8slE4wo
WCJIYNXUxEGpZ3sGxTMnioNsDGQdQdN/vrjZGnjNGWxLRP5j9t0APfIA7gZoLITtGWr8nWAsZYCK
K7vZ2g0CLadxmWkjB5JaB5gtfFEgzwovl17xz1iSWieU1JLAGvtRJF/8T7vURyBxtdxKGczKs1Go
k9E7dF8HRJ8OMOrfYDAiq5ebwGsiawIoAtbvUx3iOLx0U3d6HS3Yli4M4rxDGYQ58A9iuUglRADm
rVLmiZfiOMgGJqrZfa+mJM39AZViWR1A0QQJrWP5hAZmKUjEIiBOQxUPttzi9bYFcaoL4nRko5R5
GMgApgflQbRnSpovaafO06CrEuBcS8i5xO0ZWUq4qoCiPOQJh/PVAUpC8RyKz5aZu/m5UPID0YR+
/THAR6TlllKmGtAjDNiDogfaUADh4wTasLb6j0mnCqIJDYzW6whWlcSzoJGkjput3GAa+SpYv2dT
t3qvPeuw3ISBGgkDQcYJ7bTB+ok9CO20oQnjBlIiBQSZy3JxmqoWWR3QsVnKnB8BPONqojpVtQZE
e0H57d2gFZBb1KAckxS8LZ3rZ1a9WpMCPdpiS/IZmDTLwmKUbJpj3PyKdaVoExMrYVQvkBoEgX+c
QrelY+zCORS/3DVWde2/AXwfBjZiBS7VAcNRlQHxc5+YE/vCdcyZ/etVsCk0/kRo4F/xJaO0RZ2K
bkt/BkWOvPV72ayPIMO1nAErEejG2uKEAx44J2W5rQu0ny94D3QD29HR3mU2/2NqkVaXJGOey9fJ
NGDshXblG4iK/NguJHx1Td2piCxhbOmCQJAWgBmuDCbW3mfNy0wsGxAzw5ukzKOyKwIos/fzQyXN
06DcbQTC6K94Di/7ZcLI5JyCYLY9yLLJjlByVNuWTooCE03J+v2Uts17atFykZwBjCULFqEWaDl8
QdwjOBUD9O8FlK96J4YadV2WiR48tDfiAWm/SUjQtes+pAAC5IoOQaAAqKpJWSpumFmrNgds81xB
f3XZUEZE3eUosMDf2ZbuBUxYAlRBY5dJLErQKvWX9XtqBrCAYh4woBcIjRSQJvyfAmPOGi8zhyaH
oJRcfrNVDVr9rmj4b0snQCMQASPQBCP4uFxiA8m5Q1Eof0pdON/peXBgKtd/JGnfNMT5p0LhgUGc
JlKUAl9JEEqUhz+OKrOVb/NuDNVuiPN6ymmiWoqGIkEHRMKuCA7FYZfljm+zIKbJZcdaroGYVkld
RhPl5hyW5cIN2kuoiP8I6uLxDvo8/agEGnvQhQC+HAGJlR1y94dlLCJySjh/hJ/ThF1zdz7VyHGZ
czhKFc+y3ROFjgUaLOuW8McKGUt/J7ADju/fvo/6CKRpK5AzEsDsv7LM3OgwVqjLMgK7TgGw6/Rd
To04391A5YDA1I6zgakbspbZLmfBMSxaU/TAmfOCMcsmdSaT1ZEWwGyuYdvSI0C69lguNXVrCOfz
beU0IekI5xcnAjee/4+Tsto3kR/HzRYdf3Dc9F/HYj1MlP0Hto5X86v+Dl2w9N2P5iiA7xOc4nnW
tExuEo7URirJC+WPxGkj46OWUYQwyaoIr+pD+yYsixuftceyKFII3Di0U0LQNRGUa5YziB84d3uB
XF0ENvyGiZHLKEJrT2MNbKCDPjvXj2OmjpIM2Dfkm7lgkQ/tn+dmg3jCAosYAstkL7f/gBYNfzDB
gsDJbs0yAYLCPqr8cfBGEaDz1EWWg3cjBuwJVeUcRmK0kRu4SIovQRo0AxbBgrQrp5O2TNrk0wOh
baiJ7BCM7/c0Wu65GPE5S6x+vAFiZA2La9Ji41hccwg62H0BHjkEck/6cqepCGCRIouHxJEXx1oQ
8QnLuAbjpseS/UY2gZPdFlbXkI1ZF9J8kJfVwA61A5Qhy8UIEUyaqUCwpoP5Jte3zHpupRbxYz0n
pgKvDFJZjrp2PODY8ICH8QytCqwvfOC053YSHGHIN5eLkUpgER9w6JaN72+LXsYeKgovWUMVSn0k
FnvQcGEuLIrEUMGGjAzmbDzY1tGeLxMhvg9BkJ6RqEM903tIlLFZ7vmmL4XlMRGKBKIVhfgRrUhV
R1bPRAOPUMD2NxSkMxxxmcOU8F1tJO9GThOVeG0kRW2ZUOWtw7BYpArKIu9YPHODxGqQR5BneKXM
VSIqAyg1McuFSBpYqJWEhPIjQTpBYJd5jKcL1jA+Hk4TN7Cq+55b5kmoSSOr1iRoLV3HMsEwEuIs
KWf6MrAaFsS1DtieYLyslzsnWAGTv4zuz/dRqJsMXe7pMHGBG7GQG3VYkk7Ts16WpIPfA846B6zB
7gmcV8yW0cMnB8TTFRBPBSCeSMrwZUzSgT7MMtUNoMegBSzrUhpVgMUk7p+vgh1fOdiDOYLQdutZ
5lFDAnCgDliocY85TWjnl1FkWOn+fqlcG+i51rDHEFEYZL/OBrDC42neIBZHmnrvqXpqQM+W75e2
0B4dFPPvifq9gFuUtM87+6FVUZA3+VOT6LAOAQfdfxNzq8whVYm71Yi7VaOLVesI4vwqDprnlItf
PzGVxav0B0ZOOc1Mzc4RImamyuacaUcpc5K6rxO9Nvua4jIoShPD3VHZ+wt6cAV9USYTVfphcs9m
+T/mrJtQlse059iSETtaMlQMe1vgUQjuUELSjGtYeUM5BYMbm+mGD72Z81BSxBw6iNmRCDe9C6+p
FsfEwJWj4Z0JcKoYxqFSnJYAT/0o7uaVYZWn5euh5+tx0dfD1dfD2fegMqZwD+ZeDNx3omIqDPdy
blK+fyaFMJB8w5CN72tZWZLYudy+jBU8acMSl7vZPeGXcRMWRP6E8+jaMnvyjldUf7Wcb3IGbIU0
ne5BHn6NSPsTWfsNbybDX1u3cWm1hG6d+BD7oJfYosmXNV1U+XdwFPwsWssC/2Jdi2Zxz9uyYt0t
yTGHvg5GYQ+6XmmI6CUahr5IRA9H6PIVT71cN+LrWZnupnLm4NfQiKgNhc/sevS1WrS35X39FJzm
cD409/HeVFKWRHBCvLyHmQX+yrrEUa+6uSbBZ2Lnzsd7y16q9tHyKaeaW/W+OV5VrnOOhhubBOL3
nEmxkcNyJop2g++e3cEaaMW8yn3h/HmOq6mbWyaER8qm9K7lUE9S35x143WvKEO2Qq/e97MHtR6J
2c3p9r5RTNyumPV1QicqdB+c9r5Ud3tijLxETHlPacUUd/KA4lzxY6qJlqCQtIlXUylJbio+93PM
FYstqTMYZ8rwXcwaV1w9ydR35nAV6sxr6sjxYYugcpqYCtWN/XHCKt4QlVT3uba56Sxa1tgaAnkt
NuyLRtxEVeQUG61iulNLFHvn3XTcRDeiJoz/9Guq2PHhzOAy2iEVKmFmzIcWPvbM30vCY2pzGXdC
Thh3CD7N9vwBCfTNtd3rsN2TM2M2PVHkPlLRgeKvxveaG8f3i90UP4Rfo1RX33nudVFXt9sV3arM
16B2bmjMvdhEelQpOFJOOutQ3LrhdxMzL+emd2oNYzEXXanRW8q4O9eEKRdPztqOTnNN0doejvmR
qgDHO/Kem7iC+DF2OS4TzAzh0kxQpOsXJVzdTKKptO9h37mrc9M8kU9a8Km2M1UPx1SqVPzRU5ZU
qnEddfof+OYQcW+0u+tkc7fpJepEbD0J3z0zFkYob9n8ETM9R3lsOzdmHwvZ4jHNkowW24428Z1D
zYylzQnj0PvTdRqaRwX9RjVwRe6oAiJ3jlxPWvC76XekF7y04G7a7US0GdT7ClrpWBoBa2Lm/YX8
fgw0L66RWA7YUF8N6qZaMK/2Y/YXUUQpVfXd6/CEypa+HFriTXyO7dy8JtwqKsGETFJGZLHoBDYM
vVuleNpTnA+/G0O5Fjy7rltnz6A4la/YkeiDeJKcZzUnHJWlUozywrqW4b/mRI7BsXjMqtgLCU9K
sYk+iq1U8ujrm/yvCr9NaXx6NDBVmYJfc4GExc0m6Ezisycp8YOJmN6ieyMY+yqybAe6PJJkm0+r
ER0tR9I6jDq9Bqhe2YXYeoqKHcmLyqel0jFOPUnCF/QW3RnBIKrIf3VgLhK9BJowl6smlDpobyIJ
rvm0t6LYbiSNYpSQOUA9bpSQOED1zznE3UAJzjk0Wk9hd/QIfUJGZ/fO3B2hHS/AlA/VffsQSTiZ
j65oJhx7SSMYjdCQtLTs9pYBKjxnYF0LSUzLBmPcjjFaQ8GVSTiSiLpaX4q974+PPZxwVi/GFPaT
JjInKKWYHLRYM03fymMiQ3RiXZXdGCG7fEZxRgEu/rJhXOnB8MzsyTXkiGAirXC8ae6KSKGcogKb
6Gidvwji45Ocgoxr3bqdR+LdupyvxFEu8BBfF2k397aId6YNd7vkDaOcvesxPs7eCVVwmp+NzWgr
vtU6FD58n9Zo5YjBrRG0QOOIX/15G2f6Mi5voBSOU+Zm/QgTpO5Zo8REP/wjvsH7NY5V7woGCRVe
wkUdXhA9xw/v77uJjFPmb0aeRp9fQ0lD1WJw9ZGUNr3XcC9xRzTFQqFf5dU1ahqyFn1hDaVEsT+x
6HVvLldi2ijqFXXuyXAiiVBCFaN49L58qIAeul9T5u6drey/jtQCj8w2Trw2TrEYpxSoNPvGV1uY
UTEv29MOFnXE89u/0J0z7U8cxeyleeeK4OERoxg3fQ0azwy1Mx1N9Mq7TPlWgciZ9lRUhNu8rYxU
yDkUqQKfoY72EqN6W/hjR1WGYiKpuhMY8fj2t1QqzmKYn8bBm4zWWkNhP9TsXByEXzeh+JxGZhss
5CfjitNonW9niSWuRkVha0mlplU4OMa7s7qYMy4y9CUG/dGqe7TQ9dLMPS3VmpkvTzOxiWNHUI4N
nOHiMbTp98Hc4vz6BdMxg4GklO2KfdSyMxYj7pcsKXNNTZMkp1gFu7eVFdxyji/5Ci42RIRvOSTr
WH9P+ISm6TeHyev+tHMHr7yVitKJxViPt0628NdT4ssG2woM9MMKEE36nCM46Se+bwdvZ4Vg4WcL
Jkc1mkDm3CaTsZe64aXnUAn2Jk9Mo+MMuZEzysRjNPHlMzHvS8ccuwfPcPqm243fLU0SF8PIxYx2
aJRvSMzs6/oyET6L9yWIwPOtG6L8fW10Ub4qa1X8MX/r65x77dn24WBVeahjm/S3b8TmBJSvqqQ1
EZ//Uty0XfplsV1DVE3BVG/aPTkbiocN1p3yuo2nhU1xNFYrIsslIcG9tFBLV9zXXXeOp236eJUw
+0xfkk0iLcTwpVdyYl2OUYNUtzDWazxJtXiy+SBB1mFQ6WuHPbtI4kSZHTw2c3JrwrfRtJuJhZdn
7DidvDydPaa9Pt2zUdM89/qp83SJ/Ymn9ilbz9A+PxUpE7t07yy1djBwciIO4/n9crVXMiY9w5XK
2TDIwyMunjgRZ4nZrtYs7uk5fhchix4oHMV4+w4XtEzxTR/JgJPSYmTHhOOemk6Ga4ULtVO+NXs4
OhQ+9H1bOZUtd6l61Vjm2HGyzEwlOSpMnaSknOdLneAsf5oTY7wOu4U3vClcuF1fmhTZLZ9RfGUy
oM0rTig7xi2pWxL1ZbzHW0Wi904XasTduvpuJOqOvXTe1Ahc30e/u3JveJ9cKI/LaH2VmpMJLWBV
rGz8RK9lR7pbWnLxZ2qv7h17s7lGXKSBTiFtxl0pAo+T9q3/58lxgqip4JXQXjO24EgdHkXCixeG
yq3yVuOqIuU6Tt4PxystCwzbaQ7UWA/rtka/kCj+zaFV92xOhNrPDvdobEnZHqG8Je6GsTQhvqJ7
xNf1ffRxYOTLtMwKn0xk9V2/qmfNz0iIer/ghO0ST90TEd7kuTb7zh0XFJ+lz87x4FOdSLEXcq2r
wRGLd/Syj1xKguyT/kPGhfJ5Ytxck6LnFMyqu5DwaUM/7vV76E8yxjD7qr+N5hsCkr6K6FzSuz6u
jqjr6DPys3E5zG001dDvQVfTE5ERXhHqY61SRHynWaniKcN2YYPCJMfSEF4D4/P5XcXpXXOyXJiO
VrPSHFAnblCYqVVqX9P/qqtGLSo5F53sroYLogG07omIQVlQTs6L6XicYrK79kBQh3lpGRD1svbn
SttvMnb13Fh2NpfK0Kuue/VcBeVBE216c528vAbeT5GlqyxK+YEkBet2biy+nos9iIuQneJeyr/d
1Q7T5b9bpNiUdx/bzIlZ+Dr4mP/jQdENn8QImw3bRV7I9w7r2Ihp7jNuNS3Nb6Ld1huS7ML6BSVW
a7qTav0lTd59eNXcb4mcHr1L60Kbtcs8yZm2FPrUpSS+JTu6QpOi0maeY9n+ZlLxbqv5iLgZv/j5
bBS/SttErZqUSX5vTrTWCX9aPpWbtq77RkRnV5IZ/+iFfBR/Tsq0SkdXmaWmo+TT7CJRPjfyzi+V
uR7WZsFEtMnMI2UvH2rG9+83WdrjfOfmsk1Kmsb6BphfbiL5Jun1WurcqSynPDydN65GRBeRHaMO
RevxSyBaZCOwfzStiyCGV/LCP+aQAlwSTKQJHCcn/esNXZI9TzyfjnvcxXXST6QtaToL51UWREvq
COI6WWo2EJTIq9eSopOjSxwsrn02yuFTdeMmv3Iq6WTUen6lBy+tLSnHBWvm2vy4Q3coF+OuCoYO
tgxXYhM7UuAZZ89Hl6ntdnOY9TfbTqKp1e302hNTWRjqmjw3zCvJ+GbT0LB/GW9BsW1534JvNvnl
aNRMJF5JqMLFEHh5P7ocPTv29+N3q4b1Cy1mMealLm0pW/s8dPPNqbX8n1Lwt9VcyXN2Vjbs/HJX
pnNfOtnAL9AwYpi/fD0bOSihI7Lfho4Isx+0r91bUqld7P3tes9vkwkH7Kq/vnTivAE/+AD7hPNE
yI7W4YcaxPIQfuUv0ztR17z4C6yyGvzDii3fPdHv8BQWHlln4ys79HbuTnnPVEnX7eL0fnV8OrAv
X2OdhEduT14c9cwhIY2G8p76a9k7wh+6GJq4NAgfbWo5hc00vEdIdu37lPnIo3hTX56j0L7U3OH0
Bjm/oHV8IrE2WHlsUf69hiy3fPPx01XfMA9mB1SJfJcUXo9E6VAmC3oRfMg34UWD8CFqBUpuQ+5z
Yig+2q12DC8/NOWS6kOthaPybDu01hgjj8xw5jSzewwKr+H9yj/BpvC5qC6sM3u0epY7cQjVTjXb
8ZW2+8uLcWw99kVJt8vgjWzsi9tF4YTG0mGlZ75U7YqgIpwPd7Ze+xNKkPjd5Om+8gddhKgnid6C
XsL6o+ux9fyhTkbp5QVPiqV5ZaX02rPUHnSFcxvUzBnihak7mm0JxNZtDzBfnwxI7RbJmVGQnahY
l0arTsFovMQ0vm1Jl1oboYPOaTwjPzQrTxAM6ps7e1appY7H6htoMJTXVfV5bpVKW8o0eoLYKndq
pLU1rkQ6YfA88oMzbbDAmxTqOoTHi06eUnEwOKSn1KCY7U0SVTT4dmpUrvcNsLLHE8JBxZpMNcWJ
5H7bBJ1io0Fia6vR0VIu9hyjMdpuePlqjOH4w0IJvgHT42r5XbPYaqUnjyzG8x6bO45zPcsuSjtY
HhLPuU8ML+6j2ZewKezkdGxDznBL+ld9wg0MHt3tlpSgSJQL87YOG+XzmMtD+/RIdRfjEIMkfHyG
R/eXJh1j0rl65eHrhGK3rr3sKHgb4m37388OnZl8ksRX1PWhJ5MYNmdliz7UzU3C8DVTRv7mR3+d
tqtKVRxzY3+fblN4ZQhz417ca5ovTvXZ3GDF4zJu5R2b29TQjnOE0D9kylPJ7wPLTeIb++vrZE1H
bHkww7Y8WbyO3cmh2WO1tCnfezRMUQjtWbzU35cGT3W7H3Y9sLOuEtambnVso62fupIYyqLujPEl
jpd7Nu7esdnx2imZtoF7QQHqXCUPmy4MVj5UM8o6vod3ZclzvW2H3xpUHs4SEOOyulLiOFL09sDo
FYy9rpI29t7lGqVdhSRfGo1URBohj1/RmTqyPcuz3CzFLfXlzpCL6iVs9bZ/S7se3r5P3eLA49sw
O1uBDSLqJfz1e65KnxLYsPuxxIp6+T/s5mB2545uf3qrzO3Chg4vLXJ6wbTbw5buYbi2Iq3iFJJ2
qS3sJm/d/R3oPpcryX24N+w3bipnujg42Owvzhmr3lSakJR9O68yhWCcpbhxH0qy9dh4z53cs4GH
X0y4CY2t3sv/lNvFBtPxd8ClMELDO3exKwf+EjuQY/0t/9JKdTfR5IKCqdb2ycdvbC8bplVx8Iag
b/p2syuPrMvu2lVvEofvMJHKdDc9/sBP5lH/ndvaEX6pBdkPTGLHu+M5KWN8SlV52WeO8XKZRB+6
fnLkTM0TB1v+vetp6+vWfNtP2Lgt5FlPXsD6RLttA37nHQgefFf7FfN0tEW5OOUE/jKuqVDnrDTf
GfFhL+H1e7x+jRmPVfWbRnJ6iLGaaaXrXyII1b9l5tmw5X+FcLfi8t+05733e5Rz3GJQKRu7v6vh
7y4X2Z6GtvrYMzUvMiUkhP1P9dx1udBVJUa4/BvivKT4qfqsjNbKqm9Dt98FdJ7Wtj6fUROv4G+Z
Jy4nzFGEOvglvuDq+XEhzZB3eq+4n9aUNlJ83PUkHjh7ml3YJ7ZRfEee90NkTA9yQNIfHc2n3HbO
eqrzfk/NdGnf8RfBiPw8y6iDuYWyWgPNp/e5HdSbhRVF5Rb2eZHfCYR7hQSRwtpNHO4WbB4u8BgT
FvzqFUd8ciqn4Ql7Rruaq6xiYNDZ+I0EHatr5MeVChxyRAFzjM3WG2Zb0bf+tol4MvQp7Nu7/ZJ7
MhO3xlIM0gMfc1vf7gzf+inMyNmVUy49MKHR5kHknqecLtHveUpOVsRUx2sIkndeOhCqYNgU3ooL
0peWcSp7v9HkgU+nXr7V8E3f8q982H3lqyUqlCrTRQlahsnrVPz74SpJO3isSiuPWqwdO3STMGN/
LO1Gjc9drqQd9u3/BL5lf/6e+3yoo+Xqp8Ung+5kFt7VFxRpuf/G9Xw630DIpTsvHxS+SR1Y2a1U
z9UZHmk2GOaQmBOA3ReE3fLB5hlX5g773e8v7xtjPxtadOlj4Z6TcQrvw6MGIrmIf3k/uKeZYx1p
4bhL+FnewcCvTb2/7SmPzPS504wMWFNK2PXBZiXORnFsRgkL3j87fFHkTaOqQ+LjL7xd2mMHucZP
BmSvlJw+ZhSTf20u89E9gsu+scC1Zx6IRJ2j1eU7KM7kjq/X4Xy2whmjYrPVX/FkL25LjatDfmHP
MWufG0a5uokmipWP7Dvl9/DGeu/Pv6ROuSl4mxzZaN3MPm0/3tn/kmfSSD8nc6z4qIdFZtdkdVu5
jFbeNCUwOWmV/vGYrEkZ8oQu+/DzoNQ8ZW2UfGZzutDRT/bs1p3BkXOxXTtMdievkSlrkIm9uEEw
/8a25C9qLbaNRiWmiBD4xZDzyJArr8os88t8zlkomDwST+Y42uK+0l7zsX3hSvvqFiOL2M6WiD9N
unnOWew2Uf3N/nrRc2LQlWHtx/bId9uLkSHKr25wmXSndrakvujczguSzJ/20/UyFyaV+YVelSHz
yxyLXEqjNfpqZcQG4kVO7Hgzk/uMpGQL/qa3Je98Zv/hYsjW/LINRaMT28THlcMe2ldfl5kb2E6+
GFKM3CB+zmKfyaPNzbf6fRLXWISMkCadtNaMIV4cGsvoutCHkWz8oj3pxJfnaGNznJ93h1PMly3b
pIstXfTPaU3vNg6GAnf+f9kEe/sZeezIGcvTBfaiIs17V9Gs7bcfI56Rzgg0ejF8TvSMpvB23AGj
MuXHVoW3cZOnV/81qNWuduVQ6W7Prt9jXG6et1+tLJy0eX++4d7XqaiUzVs9XFO2bbz/aEwnDT99
LWzdRRHrO/pr+zqSNo+o5HTZIde3F079UXaeU9T95bn+/VrB61/wBZH6P23BYFbTeOXSJPJunxyp
26IjguyMjyzeUNfhlV3KAZKSJTmv7KbUMMfRQ+JdHq5q33TyeKVAUqJs5HAK9irUCJDcd7thS8sm
tKHRDtwfZqu3NFx31GscOzu1N2PTit+fHj6WazUg4f9b9i3uc9fFDriJXZmJQklJyIQOT0iOzXCW
2uzqmVA8dh3XJfHxem7AqZ7XpVnvozq2lHPcs4DfdTn59JF7TGh7fKTZn709KyfaS413HnzxpW9X
ydkozs68kmBticntOJs9Yy/uTw9tmBm6SC485W0w4T11w7sLxR49u5UszXHr7bPYD+apt/0+qnx9
bn60NGmvcZ6eRVyTfv34R1T9+GTTpDlW9uQZo4Ie97j9rS2mZ212GQtqiOy5MyS6NcH0ttupQUln
rfjbJbGfKsKU2ZTt5h6/hb849DnnlMfnR4UiqlI3RNinH3azyXGdDOX+LS3n/JPqTDvEejv78JNB
VHuMD3rvhgGc7ODD1owDnx982uOC1+fgqRLfveaRRUqzhUnJgfUl7udXYfcUHY2Rwh+SnDh+1lpZ
Qnri06vAiRc6hsfaxb6kPu4XUsgINGt01tTY3BAoptkV8unxftPqeLUcjY36fz/iM/gU3s9TuW8o
toLb77eB4hNfLj2R7njc3aReWJ55quyFpnPYHQnKpd4IF66UgFR3gwr54Bg/63H9XUfOH8KuPYa4
VkP557RBRatmcdAu9ZO7iz/cGfOxvqgc92qD1DZxK8cPlWx7XuWebu4ujPEWPFUrZnpREfn3GB9h
Klw18eKgZ7ZOxlxZdVyXFOd4nuhdfcnZW6UST3fr/ZVjOfcVrS9pnS5euWJXg5dHTE2hc+rZmqo/
Qz+rau6/mKDIn1FYXn0tLvxUnpjwiHFXw52xXTjhGWrMfaXNiWs/2HDhhM+nWPTVTLy46r32t+Zy
icm/QLVB3P2OzYnBAokqgubPsq6uV66bNeTPaH4iFUpw4RgLDDpdFWxsrvemh10vrh07GyDtpjf2
xIPtr5ALEa+czqStlSrw57WIcv89GtfUha/fPDn8+O+63hzpS+J1p0qjD4QUGf4DN/6Uku60+5/N
k9HeIaoJ1o13xnV9G2rxtcqx+l3u4/mu1qoPfUyl2racPrRi+FmNyIm08IrsxrnH1ICZNyOH6qOf
vrgshLtB/qiKEwv7ABJLyz9yx2PvF8b1j6YJDLtjzr3W2setLasSHHrGUYZI6uN8PZyy13Xa58y5
dIstnXICPfGI5M7DQ795OQ+V732BdyqncqYoyFJ1Q7fyzuxKeLNp9LpAdfJH0YmOC/9s/XKrbLu6
IE996yq76KPb06HN0JkWD/cxj/gDQ3amKds8TDaU3ltVOrcx6OAf9e689fIvc8qM1QW317ey27UP
q2/qIirtGd2hzL5HLQTTGt6ywkvoeRdF71xLwYN3nzR6uINMgytkv2wu2PLYIqbnZqjoPTmhRIcq
IVLZ4GhHIQfhmZtGY793mWu8jjpma1Adf9CVvZu7b7t9mOGO4USo6/q8NSHqclkYxSVK/0Z8arrp
/Ysi9WrOClnEMCoy3I2Xt0DztVSCA/G113Wl6FTN4/eaZo5qtRSe/tt8xeSDresebCWrSigc2W6/
M4TzVpmzusXBx+kr6j/8I3206sOM2sFBvIwbG+WUF0Io23Rt9NE+g4haV5tCddWRibQP2ebDKCHB
CfsqwSM7Plnf3nUUF/pE+M+xrHMmRnevK+1W6HSaCpsmpURMX3j5MfFAV+ilb9skw8hGGzi4aIdc
nUSKXTdAU/LA+m6a+WedlS2fEkVXn1XheoTdgrqWS1sxTHWoeyekcAa358LDaG0HG3SZkEjnx0N5
iQaDq3cOcr/GqtBWtM1sFUeVCPzsw7P/N+Dfv/9j6Yy4fNnK+X9jH//9+09SkrKS0j9+/0dWFnYA
vJWR+PX7P/8nAH1c88TKFT9uUayEKcMg7M2fh+n4YQZ9618/ZA7D5GF/glc+GA9sNcB/Z+E7DDu8
oJxmNM0smV/rWQX+fgN/8owPruVXHl5Q7mDwMcsVLOXC6ziHF5R6m2ALShhM4LscpOtGvnnqRr6L
C8pahh6fVy6UW8mQ28OQ28PgZ5a1DMVqF43vN8afHqM9Pca4mOUxBt8xFn4ITvciLaH33zbO4982
Hl5QHmLwHVokdwbIrYb9z4HRPOwso7+l7PKVMS5myfSDuIOduay0uIOlqIOdE8pd1F1eVlRWWswF
ISZJ12kjg1dNW5/Oz7TjVobOm2HzMQDVrxZ9JXrrU+ktkcbJ9PPifrU2FoqbIf4tsB9+Y3RMtyZE
PzgezAyNnwIC/G36CV15CbroEnSbJehbl6DHLkGPXoK+aQk6agn68SXoV5egn1qC/gDGjPCFcH4J
fq8l6HxL0HcvQXdagn5nCbreEvR1S9Avgj8O2DbYZ4GLdPz71whBvFpA4SkLc0FY2FshYbZIhJMD
/dUFBtY7Z4QzzALh5GRlgYTB4WBRsLCHW9jaw63N7BxgLkhnBysnmLOVmSXMzskKCTeztHSGOZs5
WcIczRwcEBaAwxK0AXPxcEFaOcKs3ZztkFYwCweEixUMcRmIwuGQAlC7zki4o5mdE8wJibAFLTtd
drZzQloDBhtHhBODAQ5T0zx5VBUuKSb9/Z2kGPR7iCvBv1X014X/YD+lQdNmJX3qMOf7Zju7DdDM
e8Wgobbb/QnJ5jPqp/nn7QbNuQ0sdmXaE8q9rJf/tjLoa2A/cgMEAix01rtJe1joq1joB1jov7HQ
5VnorHn/MAudNe+ps9DXsNBPs9BZv1p6joX+Jwv9Igud9Y6rLQud9Sb6ZRb6eha6Owud1Z5XWehs
LPQAFjo7C/0WC30jCz2ahc76PfL7LHTWefKchc7JQn/FQt/MQs9lobNeu1O/NvSHevDvWdwCMHW/
N8jf+7vpxPd/lMAWAE3mIWCh7X4MXtn5D4N3EG4LVQ18pgHYHQvhUOgM1NLxOxAOhczAGzoeBOFQ
qAy8ouP/QDgUIgP36bgnhEOhMXCLjjtDOBQSA1fp+CUIh9QfuEzHzSEcCoGBi3T8AoRDrh84TcfP
Qjjk8oHDdPwUhEOuHjhAx49COOTiAQE6rgDhkGsHNtJxSQiHXDoAo+P7IBxy5cBXKoT/BeEb6eOn
49shnIM+fjq+CcI30cdPx9dCOCd9/HR8JYRvpo8f4BLDJwPrTNUDu9Sv9Xw9rXey/M0b9sMw9fK3
p+lF+doNh2H9wkDumzU7P9hs5BKAQ/XVRaH5rH5tmk09sNdtpUlJLt2nwJHfTDBQFe0zO/9Vuv8/
s/C/nV4F+NUD36m/HVRRX1GrXjeNAsL588L1QFg9sGxevoshf1Xp7y0CIM1w6APJfl+giEnZ706A
tGKiBIoH9WtK0ZwC0JXx7yps1scIgN0H4KfVmgx4ARF2ftiACyihNnTV/x46B9wfWJRMptHUA7Pu
g8JSPfi33Xsg+cDj/RJv+suAZQL93oOaYPprYHn/BAUi5UIywb93coGwBURfCtTEO/XgDdWAEuw3
32QNqNGDakJCculNQOT83+jNQ3XyFHojtxmNHGQ0EhLy+Tv3PGPifzRyjQIRc+eVW03X98N3fYMj
oPcStYF+heSlmzjM2kQJfXzVdD7oVT0a0s2Erptf7XfyvAmIJxlyJfOlukUtQ8riA4MSrb73rXow
XVJ9xVvWUVXM0Wh7SwJb55uaZ/d7g0qZ5wEM0XOQffzu0wdC73Sjesj8SAL9WkFR7vcGvEL+JhoE
098DBZidhaxf+S/ailr27APz1Ji3g3wraq99XrW3dp6hlckAk/gasOIanWnFPK8ifUze95jNBJv0
W6pL7aBHCkhZtyDa3++haDIyKfmfBtc1et1K5B4QAgmcUAi8zecFtcVQDsPc2AjNN2CAXGjyD3wE
RPr0mK+F0lr/wTlmcF4rgl5hHpsxjyExSMd5sUjAzbCg+rU3INHSA+0y3X+N4NW6nD6glTBMLjiX
9RNmASnQr34+Vmq/h1H+xu+xcp3uJNBTMtSTNdQTPefuh3oKiSj8LqMe2AzY78zRnTUfkVAlmNdZ
QJI+Sfv9Z1km1kuA5NNAM8VHwPAwH79zjc3+e5xNUO/9s8xxQl8NYLRPdxhRLTj+Db3PN8yoLGRG
5RuWqPzAjMr336Py0QwzKt/Qe6RHGOrVdzVXzX4PynmTQjH5mS5el48DahVD+w5g5nnlX89AtmQw
lPejADqfoBbFkOYMawxZ3zpieCSQdsTgZODUEf2TgU1H9DRDdj9nEwApNEQUKnU19/ZK4NTfUlZB
K2h/1jQQ9xtG8rHzX6evl5qBg5qBU8dAG7TN7erXSlaoHySiBqGF1cjkiPERkyOmR+Al1tZiTP6J
EsZazFh6VzB2W+p6eqfFJcQOCEgeOCCgo7FW18rZ1cpZQUDX1swS4UavlRKTlBUw0j2uqn/2+DGT
taoIJ6SVE1IU6XHZSkEAaeWOFLdFOjooCljYmjm7WCGV9PVOiMp/Z9O0crJB2ioIiEqsXXtaW425
3qsd1xMQF2D0LrFW38XKWfSIDRBQOOoAttTadubmDlYuAjJiUpIC7jq6ApKAaS3spJOrmYOdpYAT
ytHcylkAYS1w2czZzNEKaeXsspaxYVdgbs8VYGfBPlxgt6WAuQcStGXtjHAUAJvytTBxpONlcTEz
+o5bgYkJ7HYREBJwdhRg1sJW8KxSlIfN7xsdSDQaZMlq4AjorJEH/JkMyi1gAjSC0hhk5jmoHpTH
VvzYG63wPAtb4b5xBc/6NX/cWrFmI0SHDqSXQTv7WPY/P+eHwXYx+D9P0WgXoZnItvEE29ZT7Ovc
/rgKU9muuE9qF/2JC8QHPSP4HehlArn2CNvGGytVN6xWv7Zq1emVoLX59iE+B/CnAAL22GI+uR98
UFt3wd85sDBYQYSjbBvDVh5n2xq66jibQMhvx9n23Pxdne3AjdXqbPLX1mixHXZgkz/CduAI256j
bAJH2bYC/qNsf9DH1Q6t7xQoDf2CX/ALfsEv+AW/4Bf8gl/wC/5vhKtrD9PLi4ySCSsWlcxn3PJ/
zPMxn22/Z5/HtzFw5ueIPAyc+ex9O6Pcyqjfsah+kkqDPsKDRa+cb4/57L//t3mc+cz/DaOe+Yy+
jFEyn81vZZSLvzL+/bMHxvNt5hmG+Tk189zG/AyBm1EeXn14AX0jA2fqzbyzwPwsgdk/OD7Sx/P5
93l+GgNn2vMrA09l1P+fAubn2IvBleHXG4wyklE+ZZTZjLKMUTYzyj5GOcUoVzPuEWxhlEKMUpZR
nmCUBozSmlG6MsobjDKSUT5llNmMcjG0MuLvNOPzcjVVVQWBPfrmKCckSkBaTF5MWlQSRcckfCWk
xQ5Ii0ntnafDYGIuHo5IM3NQIp3nS1vmOzsnpJXzZZiYEwJpJXbk6ElRpJkNA7NxQomZo+wcLEXt
LGF0zNbMxRYmZunhBNqbL5HO8zWuVs4udginBQgc1DlbOZhBjIx3lx2QUJd24BV6DgMTswYIqENY
miHNYGJWtnBr6OkI3NbS+Qc2LwE3c3Y285iXYL6/ZOFMV8PM0c4CdI1A0l/me5lv0dzFBSZmgXB0
tHJC/tSs/8sAzXcotpnz6sf9jnl88Yf3i3/HQxC28LPDH/cn5nGBRfy/LcIlFskjGfJIBmHXMvLQ
c5IpMCeZ8sw8FL1InpmXFv9crTZsPgcx5d8w5N8wCHsYJZTHVrDIM/OFAWw+RzHlmXmvn6EoM88x
YbH9TGHzOYYpz8wrqQyFxRbpv3JRaQ+bz1lMnJm3PjMEmHlusf5M8ITN25Qpz8yTG1cvlF9sP+b4
ry+SZ+bdwwx5Zp6G5Nf/RD4UxnJ3BPZjnZJnMDLXHyYs9n/gIvmLDPmLDPmLiwy+cVF5d5E88z7V
G8bCsHLRZZ2NC1H6PRJWeea6+p7xofCfi/gX638ftnD+1TLkaxnygov0X+y/54vkf9xDmscPLeJf
3H/2IvkDHIcZ5Ty+a5n+38DmPxdnrtM/7iX9nH8xXg2b//ycKc/cZ2z9H8q3MvRnygsw5AX+h/Id
sHnfMeV/3Bubx5n3xZj+Zcoz46BvUf/MexnT/P/dP7MkLpJn7nOYifPNMvLfFsm/Yci/EVjQzL/k
mTDLoDHlmfdHPjMEF8f7Ynkao//Fd0CZ8ot/yWnx/pQDtvB+CRPW7WT0tyj+FudP1thlhei98+Ue
hsLQJ3/Q3bHF+efPJfp/IzFfNi6q/NnvWC0FqwA3cz1dSF/5fZ1cSF/1ff1bSP/t+7q2kP779/Vq
IX3193VoIX3NT/etq4BVPv+U/uf39WAhfe33PL+Qvu57/l5IX/89Ly+kb/iebxfS2b7n0YV09u/5
cSF94/e8t5DO8T2fLaRv+p6nFtI5v+efhfTN3/PKQjrXT/flq2BbvueBhXTuH/N7AX3r93m7kP7j
ftxC+uJVcT4uf4ONLf4BWvqau+In+kPnvpXA/l8X2V+MQRdYdK48wqAvPm8a0tvfButn6MmcH9b0
9/+2ZwijnauL2omh8//bL7lLjGup8b6j13HATm9a2H4t7Od2gC3RTjv9ddO/9B+mt/Nvv08x+Bfr
/+cKiP/ffudfAXGD+GfEMzOHia/4+X1PDTr933FisALKoVthtYvuUcJX/PzepfMKKKdv/VdcedD5
/z3v0Eu0E74EPW0JeskS9OYl6INL0CFDQeNtXTTeP1f+nH/HEnTxJehHlqBfXDlvt8X2d1yC//rK
n/sxHNJ/5VbYh0Xt3KeP60d+Y+7T0lbOx8lpBt2KQccs0W8DXc9tsK+L2m9ltLM43+IZ/Ivjgcjg
h+6lQXCMEaBfGfQUxvw1YdCnl9Bn7aol7iOv+rl9JJfgV1uCbrRq3p6L9bdZov2/l2jnLp3+7/Ur
dgn+5FU/v7/8Zgl+mIUz0gWJsrYWs4DB4adUz8I1T+rqweEwSytnKxs7F6SVMxzpCLdwQDhZucB+
QoLDLRFwGweEuZkD3BKJcHaBm6HcYRYIx8sOVkgrSzHZg3JSP2eC/3jGALdyQjp7wOafSliiHB09
gAgLBv/xaILByviWEV3rE2ePaB2HH9c+BtSeHwPz/QIxSxj82HntI1onVRfW0K9Mw+BqmjpHj2jC
dU6c0D2uB9c7clTzOJx57drCBUXXFgY/qacF/2EaPS1VyAp6ZuYOVjDoUQijMVsk8jL96Qr9Hvfh
w6x3sOFW9Gcm8+3NXwxfxLDo9viPWmnG/fCF/PQr6AtJ83fCF9KYN8UXCwNNna2uzF9nX1hHv36+
kATdYF+s7aKL6YurWczy/er7Yp4FV9eBiAsCbmvmZAmMCj+pAyos7ZzgKBcrS1Z/0J9vzd+eX2xg
yNUMr8LNXVwY7+m35m2ABo4uNvQrPotsRr/Dv6ipU67wswxfqzqYubiAgGfcIlrIaP0zR8NBeDBD
EYqbn0YN/Rr/IkH60KBvByzyIP07Aoto9LtNC2m/4Bf8gl/wC37BL/gFv+AX/IJf8At+wS/4Bb/g
F/yCX/ALfsEv+AW/4Bf8gl/wC37B/yr8fyt6zqMAoAAA
11 Likes

aww man couldn’t you have waited another month to drop this so I could have finished my exams peacefully?!
Still need to solve the keygen one.
Looking forward to it and it’s on my list!
Keep waiting for my solution :smiley: :stuck_out_tongue:

1 Like

I’m having a good feeling about this… :stuck_out_tongue: I love this style of challenge, real life style challenges!

1 Like

took some time but I found 1 flag

CTF{Sl33py H0loW}

1 Like

Congrats! @Leeky we have got the first flag!!! :trophy:

I guess some of you could use some help with this challenge. There you go.

Below you can find the STAN .srep file for the dropper. I think you should be able to extract the malware from the wireshark capture using the information it contains.

Run the following command to get the .srep file:

cat data_below | base64 -d | gunzip > dropper.srep

Then open the binary with STAN and load the .srep file with case.load path_to/dropper.srep

You can get STAN from https://github.com/0x00pf/STAN (you will need the latest version)or just process the text file yourself ;).

The data:
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