Thireus' Bl0g

This article is a collaborative work between 3 authors. This is our look back on 2012′s most famous public password leaks.

Authors: m3g9tr0n, Thireus, CrackTheHash | Copy Editor: Thireus.

Nowadays, different hacking communities around the World publish their leaks on various online paste Web Services like Pastebin, Paste2.org, and others. The most usual target’s vulnerability is SQL Injection. These leaks contain elements like usernames, passwords, addresses, zip codes, telephone numbers and even paypal accounts or credit card nubers. In a small amount of them, passwords are in plain text which makes hackers’ job very easy.

In this article, we gathered a big amount of public published leaks with main purpose to check the strength of users’ passwords and password policy which is applied for each service. Some well known leaks, included in our article, are LinkedIN, Stratfor, Gamigo, NVidia, Adobe and eHarmony. We are going to present our cracking techniques and tools which we used to crack passwords from these leaks. And as a gift gave to our readers, you will find attached to the end of this article a wordlist containing all cracked passwords from these leaks.

CRACKING METHODOLOGIES AND TOOLS… (m3g9tr0n)

The tools we used to accomplish our cracking process are John the Ripper and Hashcat-suite. In other words, we took advantage of both CPU and GPU.

When dealing with password cracking the most important thing is to know as many elements as possible about your target. For the case of Stratfor we had all the appropriate elements needed for effective password cracking. These are usernames, first name, last name and e-mails. Many users use their e-mail or username (or part of) as password or keyword. Knowing these information really speeds the cracking process as it is more effective to create a wordlist based on these information for our first cracking step. On the other side,  LinkedIN and other well known leaks contained only hashes… that makes the cracking process more difficult and time consuming. But, with good rules and techniques some interesting results can be achieved. For better documentation, we are going to analyze each case separately by showing the techniques and custom rules.

Stratfor Case

Regarding Stratfor, we had all the appropriate elements needed for effective password cracking. The first action was to separate names, usernames, e-mails and encrypted passwords to different files. In a first attempt we used John the Ripper’s –single attack which is a cracking attack purely based on usernames associated to hashes (Hashcat-suite does not provide such an attack). The hashfile must have this kind of format for the attack to be effective:

John@yahoo.com:90560000032a57c389f686bd4eeccd4a
Kate@hotmail.com:d4c202003a0a66496df5c043ec1eaaac

• John the Ripper command for –single attack against MD5:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ ./john --format=raw-md5 --single --pot=stratfor.pot Stratfor-hashes.txt

This kind of attack was able to crack many passwords. When I (m3g9tr0n) am trying to crack passwords, my first reaction is to apply effective rules against effective wordlists. As far as John the Ripper is concerned, I always try Single, Extra, Jumbo and rules presented in my first article plus some rules generated by Bartavelle. Regarding Hashcat-suite our favourite rules are best64.rule, best80.rule, passwordpro.rule, T0XlC.rule and d3ad0ne.rule.

• A typical example of a wordlist attack with John the Ripper is:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ ./john --format=raw-md5 --wordlist=list.txt --pot=stratfor.pot --rules:Single Stratfor-hashes.txt

• A typical example of a wordlist attack with oclHashcat-plus (GPU based) is:

m3g9tr0n@linux:~/oclHashcat-plus0.09/$ ./oclHashcat-plus64.bin -m 0 hashfile.txt list.txt -r rules/best80.rule -o hashfile-crack.txt --remove

During our cracking processes against Stratfor, we observed that many passwords contained the word “stratfor”. Based on this observation, we considered to generate our own rule that appends or prepends this keyword at the begining and at the end of each word of a given wordlist. The following code is an example of rule created for John the Ripper in the john.conf file.

[List.Rules:stratfor]
A0"[Ss][tT+][rR][aA@][tT+][fF][oO0][rR]"
Az"[Ss][tT+][rR][aA@][tT+][fF][oO0][rR]"

After cracking a big amount of passwords, we generated a custom charset with John the Ripper.

• A typical example to generate your own charset file with John the Ripper:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ ./john --make-charset=stratfor.chr --pot=stratfor.pot

• And the associated incremental rule in john.conf file:

[Incremental:stratfor]
File = $JOHN/stratfor.chr
MinLen = 10
MaxLen = 31
CharCount = 95

The charset file can be used to conduct Brute Force attack with John the Ripper based on Markov model.

• A typical example of Brute Force attack with Markov model in John the Ripper is:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ ./john --format=raw-md5 --incremental=stratfor --pot=stratfor.pot hashfile.txt

We left John the Ripper to run for a large amount of time. Many passwords were cracked, but the most important was that a large amount of these recovered passwords using this method were 8 characters mixed upper, lower and numbers. Thus, we understood that Stratfor had a policy of generating either default or recovered passwords with this policy for their users. Our first thought was to use pwgen utility in order to produce random passwords based on this policy.

• A typical example of pwgen to generate 8 characters mixed upper, lower and numbers:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ pwgen -c -n -s -1 8 5
Ch1NiIzz
YrN5SSXL
8CdcCJGG
5YBIxBTt
rmIW8ipN

Of course in our case we should generate more passwords and pipe pwgen’s output to John the Ripper or Hashcat-Suite. But this kind of attack is too slow. For that reason we should take advantage of GPU. We applied Brute Force attack via oclHashcat-plus.

• A typical example of Brute Force attack with oclHashcat-plus:

m3g9tr0n@linux:~/oclHashcat-plus0.09/$ ./oclHashcat-plus64.bin -a 3 -1 ?l?d?u hashfile.txt ?1?1?1?1?1?1?1?1 -o hashfile-crack.txt --remove

This kind of attack took 2 days and 17 hours to complete with an ATI 5770 but it was only able to crack 48% of passwords.

• Some examples of cracked passwords generated from Stratfor’s policy are:

dd39ebf25b0892803c0edfdedfcf137a:4QnvJQKQ
0adff76e3b3c2130fcb8d9cf476f947a:4Kjduu8J
61b4f425867841330cec762d96df157b:4sFqqEnY
ffee030ed8d97ad550e50b011d95b47b:2xdjVx7G
728d78a787d7279cb0a007f5f68d817c:2DJsL9jE
00ca874d657b3fcdddbb96121667ca7c:33g3UWcA
73b87959e3d1ba6c97037f6ddb5be87c:3TSfVw9M
9a4f0f28125c03323951283409c8187d:37nfZS6p
01dfda585ff13b24ab1d276bfd58227d:2K2HHfKC
7a4f94112cd50422740035dd80f52a7d:2s6KkegZ
99ee4023fc71693006af30dbb25f477d:4f9ySQxR
e46c4ccb9323566dbeb1a33967c94a7d:2SfXBWb7
99aba8d7e69649332ac64e813a664b7d:4pZ7ZmjJ
e5f706829a937c3fa5e430c81e926f7d:3YnxoEfy
ffff9c930660fae4c9e9ace85a96a27d:2JTSA88Y
0d7103e46a1c0f44df5c096b6e2ae17d:2ATb8ApH

eHarmony Case

Regarding eHarmony it seems that the website had a policy to covert all users’ passwords to UpperCase. For example, if you had inserted, as a registered user, the password “p@$$w0rd”, eHarmony’s system would have converted it to “P@$$W0RD”.

The first thought that came up to my mind was to write a simple rule for John the Ripper to convert all my wordlists to uppercase characters:

[List.Rules:eharmony]
u

Then, I applied this Rule to John the Ripper and a large amount of passwords were cracked very fast:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ cat ../Wordlists/* | ./john --format=raw-md5 --pipe --pot=eharmony.pot --rules:eharmony hashfile.txt

Due to the fact that my wordlists do not contain only uppercase letters, numbers and symbols it was a waste of time to apply other rules against eHarmony hashes. So I decided to convert the most effective wordlists to uppercase characters, using the above mentioned rule, and apply some specific rules:

• Convert a wordlist to uppercase with John the Ripper:

m3g9tr0n@linux:~/JohnTheRipper-OMP/run/$ cat ../Wordlists/* | ./john --pipe --rules:eharmony --stdout > ../Wordlists/UpperList.txt

Then, I used the –wordlist attack with John the Ripper using the following rules (it is a sample you can add more):

$[1]$[2]$[3]
^[S]
$[T]$[E]$[R]
^[P]
$[M]$[A]$[N]
^[M]
^[B]
^[C]
^[A]
^[A]^[P]
^[T]
$[I]$[N]$[G]
^[A]^[M]
^[S]^[A]^[P]
$[P]$[B]$[B]
$[R]$[T]$[Y]
^[D]
$[E]$[R]$[S]
^[H]
$[P]$[E]$[R]
^[F]
$[G]$[E]$[R]
^[G]
$[K]$[E]$[R]
^[K]
$[S]$[O]$[N]
^[R]
^[L]
$[I]$[N]$[E]
^[P]^[H]^[P]
$[I]$[O]$[N]
^[J]
$[V]$[E]$[R]
^[W]
$[E]$[S]$[T]
^[H]^[P]
$[D]$[E]$[R]
^[N]
$[K]$[E]$[Y]
^[H]^[C]
$[O]$[N]$[E]
^[E]
$[A]$[S]$[S]
^[E]^[W]^[Q]
^[A]^[S]
$[T]$[O]$[N]
^[E]^[D]
$[D]$[O]$[G]
^[W]^[Q]

Of course, you can always generate your own rules or modify existing custom rules contained in the john.conf file. In addition to this, Hashcat’s Suite rules can be used. One simple rule is to use the keyword “EHARMONY” at the beggining or at the end of each word:

[List.Rules:eharmony]
A0"[E][H][A][R][M][O][N][Y]"
Az"[E][H][A][R][M][O][N][Y]"

For people who do not own strong hardware and adequate disk space, Hashcat-suite contains a powerfull parameter which has to do with combination. In other words, you can combine each word of your first wordlist with the other.

• Thus, I generated some wordlists via crunch, such as the following one of 4 ualpha-numeric characters:

m3g9tr0n@linux:~/crunch3.1/$ ./crunch 4 4 -f charset.lst ualpha-numeric -o 4-list.txt

• And used combination attacks with oclHashcat-plus:

m3g9tr0n@linux:~/oclHashcat-plus0.09/$ ./oclHashcat-plus64.bin -a 1 hashlist.txt ../crunch3.1/4-list.txt ../crunch3.1/4-list.txt -o hashfile-crack.txt --remove

Methodology for Other Leaks

Regarding other leaks such as Nvidia, Gamigo, Adobe, Project Whitefox, LinkedIN and various unknown leaks collected from Pastebin, the tools and methodoly are the same. The only difference is that in each situation we have to create custom rules that refer to the name of the platform/website or by guessing some keywords.

•  John the Ripper Rules for Nvidia:

[List.Rules:nvidia]
A0"[Nn][Vv][iI1][Dd][iI1][aA@]"
Az"[Nn][Vv][iI1][Dd][iI1][aA@]"

• John the Ripper Rules for Adobe:

[List.Rules:adobe]
A0"[Aa@][Dd][oO0][bB][eE]"
Az"[Aa@][Dd][oO0][bB][eE]"

You can also create similar rules for Hashact-Suite.

Another effective technique is fingerprint attack. This is attack is focused on using cracked passwords against remaining hashes.

• To isolate cracked passwords from .pot files (John the Ripper or Hashcat-suite) use:

cut -d: -f2- john.pot | sort | uniq > list.txt

• In Hashcat-suite to isolate MD5 cracked passwords (from output with the -o option), use:

cut -b34- crack-file.txt | sort | uniq > list.txt

Then you can try all the rules mentioned above. From my own experience this technique has always great results.

ADVANCED PASSWORD CRACKING FOR HUNGRY PASSWORD CRACKERS… (Thireus)

During your cracking sessions you may certainly have noticed that most of the passwords used by users are always made of “keywords”. This can easily be noticed when dealing with big leaks such as LinkedIn, Gamigo or Stratfor. These keywords are interesting for us, as they are used by users consciously or unconsciously in their passwords. Fortunately for us, lot of users use the same keywords and if you want to go further in your cracking process the main idea will be to use these keywords as roots for generating new passwords. In this article section I (Thireus) will introduce you a new cracking technique based on this idea. But first of all let me explain what those keywords are exactly and why they can be so useful…

About “Keywords”…

Basically keywords can be described as passwords or part of passwords that appear as intelligible or used by multiple users. Let’s focus on the following example:

Il0v3soph
il0v3sam
k4r3nl0v3sk4t3
l0v3s3at
l0v3s3x
Myl0v3s

These passwords have the keyword “l0v3s” in common, which can be found at the begining, at the end or in the middle of the password. A common mistake would be to think that re-using these passwords with various rules will make more “l0v3s” based passwords appear, which is false because most of the rules you use will never extract the “l0v3s” pattern only, but combine or tranform each of these passwords… And yet, you keep thinking that there should be more words containing this keyword… and you are right!

As explained in this section’s introduction, keywords are not just words, they are part of passwords that are intelligible or repeated among multiple users’ passwords. Here are some example of keywords:

inked
_123
assword
!)!

Keywords can be anything intelligible or not. The most important think about keywords is that they are not random, ideally generated by humans AND have a high probability to appear in other passwords. And of course keywords can be part of other keywords, for example:

inked –> Linked, linked, winked, inkedIN, etc.

Another nice property of keywords is that they are independant of the password size. And a weak password (understand easily crackable with BruteForce/Rules/Wordlists) can contain a specific keyword, that you can use to crack other strong passwords. Let’s see for example how the following passwords have been cracked:

a6fee417cdc11a71ac5da0ebb9cd20acb93d2959:M00linkedin13
ebf1570c045011b27706a28eb4c857a5b994cf47:0linkedin1-us2

M00linkedin13 –> Was cracked because it contains the keyword “linkedin13” which is part of more than 40 other linkedin passwords and is also a weak linkedin password. M00linkedin13 = 3chars + keyword
0linkedin1-us2 –> Was cracked because it contains the keyword “0linkedin1” which is part of “M00linkedin13” and 1 other linkedin password. 0linkedin1-us2 = keyword + 4chars

The padding technique – CTH_WordExtractor

So the main idea that can cross your mind would be to manually analyse your cracked passwords and look for good keywords, to finally write rules based on those few keywords… But what if there are so many keywords that you can’t even complete all this work manually? The answer is to have a keyword extractor based on your results, and CTH_WordExtractor.sh (from my “Crack That Hash” project) is the script I have created for this purpose!

You can get the script here: CTH_WordExtractor.sh

This script helps you to extract all potential keywords directly from your current pot file. Basically what this script does is:

1. Read all passwords and use a padded window which padding and size vary from X to Y as defined by the user.
2. Sort extracted words by size and for each word count its redundancy in all passwords.
3. Ask the user to select a range of redundancy to select only good words. In other words to select real “keywords”.
4. Generate keyword wordlists from X chars to Y chars to be used by the user.

In the case of LinkedIN passwords, a 4-6chars keyword wordlist would contain the following keywords (this is just a little sample):

inke
inked
link
Link
linke
Linke
linked
Linked

This wordlist will be used to append and prepend characters using BruteForce and Mask attack (which is the most effective). As you can see, most of these keywords are part of other keywords… and you can think this is actually very bad in term of performances… but it is not … let’s see why.

Let’s take the example of the “inke” keyword…

BruteForce + Mask attack with ?l will generate 26 possibilities per keyword:

inke –> ?l + inke = 26 possibilities

But ONLY 1 will cause a repeated password which is “linke“.

The next step of the process will be to use BruteForce + Mask attack with ?l?l which will generate 26^2=676 possibilities per keyword:

inke –> ?l?l + inke = 676 possibilities

But ONLY 26 will cause repeated passwords which are those that have been generated by ?l + “linke“.

etc.

And for sure, we have been able to recover all passwords containing the keyword inke, including unexpected passwords such as:

$dynamic_26$00000cd9fb6fe9d200144077861d4dc70c7d4798:reinke
$dynamic_26$00000efc970e5f2edc1bf34fea284e930b677c19:twinke
etc.

The Proper Way to Use Generated Keyword Wordlists

First of all, this technique becomes more effective and useful when you reach your limits with other classic cracking techniques. Meaning that if you want to have a very good keyword wordlist you need a very big pot file.

Then, this technique must be used with GPU BruteForcing + Mask attack or using combination attacks. Applying classic John the Ripper or Hashcat rules on the keyword wordlist will not be effective at all and will be very slow. In this article I will only take as example the GPU BruteForcing + Mask attack.

• First of all, we need to generate our keyword wordlists from 4 to 14 chars. Let’s do this for the john.pot of our LinkedIN cracked passords:

$ ./CTH_WordExtractor.sh 4 14

Other settings can be found in the CTH_WordExtractor.sh script such as padding limits.

• This is the list of wordlists generated:

$ ls CTH/
CTH_WORDLIST_FINAL_10-10.dic CTH_WORDLIST_FINAL_4-6.dic CTH_WORDLIST_FINAL_6-9.dic
CTH_WORDLIST_FINAL_10-11.dic CTH_WORDLIST_FINAL_4-7.dic CTH_WORDLIST_FINAL_7-10.dic
CTH_WORDLIST_FINAL_10-12.dic CTH_WORDLIST_FINAL_4-8.dic CTH_WORDLIST_FINAL_7-11.dic
CTH_WORDLIST_FINAL_10-13.dic CTH_WORDLIST_FINAL_4-9.dic CTH_WORDLIST_FINAL_7-12.dic
CTH_WORDLIST_FINAL_10-14.dic CTH_WORDLIST_FINAL_5-10.dic CTH_WORDLIST_FINAL_7-13.dic
CTH_WORDLIST_FINAL_11-11.dic CTH_WORDLIST_FINAL_5-11.dic CTH_WORDLIST_FINAL_7-14.dic
CTH_WORDLIST_FINAL_11-12.dic CTH_WORDLIST_FINAL_5-12.dic CTH_WORDLIST_FINAL_7-7.dic
CTH_WORDLIST_FINAL_11-13.dic CTH_WORDLIST_FINAL_5-13.dic CTH_WORDLIST_FINAL_7-8.dic
CTH_WORDLIST_FINAL_11-14.dic CTH_WORDLIST_FINAL_5-14.dic CTH_WORDLIST_FINAL_7-9.dic
CTH_WORDLIST_FINAL_12-12.dic CTH_WORDLIST_FINAL_5-5.dic CTH_WORDLIST_FINAL_8-10.dic
CTH_WORDLIST_FINAL_12-13.dic CTH_WORDLIST_FINAL_5-6.dic CTH_WORDLIST_FINAL_8-11.dic
CTH_WORDLIST_FINAL_12-14.dic CTH_WORDLIST_FINAL_5-7.dic CTH_WORDLIST_FINAL_8-12.dic
CTH_WORDLIST_FINAL_13-13.dic CTH_WORDLIST_FINAL_5-8.dic CTH_WORDLIST_FINAL_8-13.dic
CTH_WORDLIST_FINAL_13-14.dic CTH_WORDLIST_FINAL_5-9.dic CTH_WORDLIST_FINAL_8-14.dic
CTH_WORDLIST_FINAL_14-14.dic CTH_WORDLIST_FINAL_6-10.dic CTH_WORDLIST_FINAL_8-8.dic
CTH_WORDLIST_FINAL_4-10.dic CTH_WORDLIST_FINAL_6-11.dic CTH_WORDLIST_FINAL_8-9.dic
CTH_WORDLIST_FINAL_4-11.dic CTH_WORDLIST_FINAL_6-12.dic CTH_WORDLIST_FINAL_9-10.dic
CTH_WORDLIST_FINAL_4-12.dic CTH_WORDLIST_FINAL_6-13.dic CTH_WORDLIST_FINAL_9-11.dic
CTH_WORDLIST_FINAL_4-13.dic CTH_WORDLIST_FINAL_6-14.dic CTH_WORDLIST_FINAL_9-12.dic
CTH_WORDLIST_FINAL_4-14.dic CTH_WORDLIST_FINAL_6-6.dic CTH_WORDLIST_FINAL_9-13.dic
CTH_WORDLIST_FINAL_4-4.dic CTH_WORDLIST_FINAL_6-7.dic CTH_WORDLIST_FINAL_9-14.dic
CTH_WORDLIST_FINAL_4-5.dic CTH_WORDLIST_FINAL_6-8.dic CTH_WORDLIST_FINAL_9-9.dic

CTH_WORDLIST_FINAL_4-14.dic for example means WORDLIST from 4 to 14 chars.

• Then we can select a specific wordlist to be used by cudaHashcat-plus or oclHashcat-plus:

$ ./cudaHashcat-plus64.bin -m 100 -a 6 -1 ?a ../LEFT_LINKEDIN_CLEANED.txt ../CTH/CTH_WORDLIST_FINAL_4-11.dic ?1?1?1?1 --remove --gpu-temp-abort=110

In this example, CTH_WORDLIST_FINAL_4-11.dic has been choosen because oclHashcat-plus/cudaHashcat-plus has a limit of 15 chars for hash computation. Which means you will never be able to crack passwords that are more than 15 chars long… And that’s why if you use a mask attack of 4 chars to be bruteforced you must use a wordlist containing words limited to a size of 11 chars.

• This is an output sample:

499896a0a104c0be6d7e578f9257e56e2dd97b31:rottweiler3:!^
556cdfaabedd4a90c23627782ab7eb7a4d709565:LinkedInMakes$
e5386e1f0de44840a987c4d0840accbe2573511f:NetworkingLuv!
08e7c2d275a68e1519c8b0842c68601b7ba6274a:19linkedin_68!
359e2430b1e4352f1577575b7ca1ae6866131820:linkedinmym99!
8e6139a4503dd34297e32df7ea4cedc4275d3a85:linkedin15c00!
df0fdf12590705e9c3ef6edb6f59323e3de6a70b:linkedinl1ng0!
79984358590405280bca6e43d331465bdb586746:linkedin81*&1$
49cd314ab02e393171bcf1bf13099f55495b2c2e:Linkedin12kay#
7813dc98e26938e83f4475c32bbd07a3fb81b473:linkedin69TJK]
cc307a7d9e40b00c0100bc049c397b817aa0a274:linkedin12914??
33f13bb3b861c0e5fc82b10fba7857107e079884:steelwindows@77
3dd28c9d9cc4f646c254d6b4570e8bc6268b020b:artdirector@nsa
44bdcefe2a698925c57d80712763245d07326704:yaslinkedin@yas
8aa482c9989df0def8756e545457ebf206da9895:Linkedin151$cdu
56267a448f53e5d6095844152310d12e52b710aa:thundercats@83a
a5949feca9f34d7042aaffe537db0e2d298c572f:linkedin13713@@
fab9ae4accf0b5766489c7760f4ee52582940d3c:missinglink=wwd
1d92639e0279840b8d00a2d7793c291838664c6c:my-linkedin-pwd
a1bac77b4fe610ec13300d246ad882a68f0fedda:Interactive@ln1
90ba89bfa42002d8e6fb4fe3728bcbcd6605b49c:Inspiration.SSN
[s]tatus [p]ause [r]esume [b]ypass [q]uit => s
Status.......: Running
Input.Base...: File (../CTH/CTH_WORDLIST_FINAL_4-11.dic)
Input.Mod....: Mask (?1?1?1?1)
Hash.Target..: File (../LEFT_LINKEDIN_CLEANED.txt)
Hash.Type....: SHA1
Time.Running.: 1 day, 7 hours
Time.Left....: 3 hours, 59 mins
Time.Util....: 112529717.4ms/0.0ms Real/CPU, 0.0% idle
Speed........: 35724.6k c/s Real, 36175.5k c/s GPU
Recovered....: 292/1086109 Digests, 0/1 Salts
Progress.....: 4020080601574/4533053083750 (88.68%)
Rejected.....: 0/4020080601574 (0.00%)
HWMon.GPU.#1.: -1% Util, 82c Temp, -1% Fan

And as we can see some interesting keywords have been selected, such as “rottweiler“, “Networking“, “Interactive“, “artdirector“, “Inspiration“, and of course keywords containing the word “linkedin“.
You can also notice that I’m not using a very powerful GPU , but a laptop with a “NVIDIA NVS 3100m” chip. So you can imagine how powerful this method can be with a better GPU!

To conclude on my new technique, I would say that it was very successful. I’ve been able to recover more than 1 million passwords after having exhausted all the classic techniques I usually use, and that in just 13 days with a NVidia GTX 480 and an AMD HD6870. This 1 million result was mainly against Gamigo, eHarmony and Stratfor and after an initial achievment of about 80% recovered passwords. And one thing to consider is that to go further in the cracking process and have an optimized cracking methodology, I prefered merging multiple MD5 leaks into one big MD5 leak and use this technique against the merged pot file to generate my keywords. As explained before, you will find this technique more useful in the case of very big leaks and very big pot files.

Please consider my CTH_WordExtractor.sh script as a Xmas gift. I would love to receive feedbacks about your results with it. Of course, if you have ideas to ameliorate this script or this technique do not hesitate to contact me.

METHODOLOGY TO GENERATE EFFECTIVE WORDLISTS… (CrackTheHash)

The main purpose of most of the classic cracking techniques are to guess the most common patterns in users’ passwords. Those techniques are either dealing with rules or wordlists, but in any case for them to be the most effective possible they need good candidate passwords as root of the technique process. But how can you find those good candidate passwords? The purpose of this part will be to explain a technique to find fresh new candidates from various sources such as Pastebin or Twitter.

First of all, to understand what brought me (CrackTheHash) on this methodology field, you need to know something about my hardware resources. They are very limited! I just own a dual-opteron with 2GB RAM. And for this reason, I do not want to exhaust my CPU for cracking hashes that everyone can easily recover. So I decided to focus my research on finding sources of good candicate passwords to be used for cracking techniques.

In order to know what we are looking for, let’s write some principles that will rule our research. Those principles are based on the password characteristics for them to match at best the requirements of good candidates. And they are the following:

1. Password candidates must be up to date.
2. Password candidates must be representative of what people may use.
3. Password candidates must be multilingual (passwords in Russian, Chinese, Greek, Farsi, etc.).
4. Password candidates must be available in large quantity.

There are multiple sources on the Internet where you can find a large amount of data containing password candidates, but only a few will fill those requirements. For the needs of this article we will focus only on two platforms and sources of good password candidates, Pastebin and Twitter.

Pastebin

Pastebin is probably the first Web location where you can find lot of fresh leaks and various user data. What is very interesting in most of the leaks we can find on Pastebin is that they often include real passwords in plaintext. So, monitoring Pastebin is quite interesting and useful to get fresh new candidate passwords. On top of that, there are several resources on the Internet, that will help you to monitor and download the latest Pastebin leaks. Portals like Leakedin, @Pastebindorks or @PastebinLeaks or projects like pastemon and pasteminer are good examples of sources and tools you can use.

Unfortunately, in order to generate effective wordlists you have to create some further scripting because the data does not come very well parsed. The first step and ordinary solution to parse the Pastebin data is to generate a wordlist using the space or tab character as separator and replace it with a line break. This way may lead to miss some interesting cadidates as in some leaks or cracking results. Most of the time you will find lines containing “username assword”, “username | password” or even worse, direct sqlmap output, etc. So you have to be clever and find the best way to parse those leaks to create useul wordlists.

In any case, Pastebin can help us to build useful wordlists, because everyday new leaks are uploaded. The produced wordlists are not that amazing in term of quantity, but usually their content is valuable.

Twitter

Nowadays people tend to use sentences or combination of words for their passwords. They have been advised to do this as it is considered to be a strong and easy to remember way to create passwords. So I decided to use one of the the best sentence generator ever… Twitter! Indeed, everyday people generate tweets with fresh content and in this case our password candidates are just what people are saying.

The most important things about Twitter are that this social platform generates a lot of public and fresh data, is international and tweets are short enough to be parsed individually! On top of that, wordlists generated via Twitter can continuously feed John the Ripper.

So the first step is to grab live Twitter’ content. In order to achieve this, Twitter provides a live-feed query that gives you a full json of tweets with all the data you need. The only elements that are required to perform this query are a valid Twitter username and password:

curl --user <username>:<password> https://stream.twitter.com/1.1/statuses/sample.json

To get only the tweet content you have to parse it a bit. First we may need the ‘-m’ argument of curl to timeout just in case of network trouble and then grep the data received with the keyword \”text\”.

curl -m 10 --user <username>:<password> https://stream.twitter.com/1.1/statuses/sample.json | grep \"text\"

Once received, the result must be parsed because it comes with Unicode escaped characters. Something like the following script will do the trick:

import json, sys
for data in sys.stdin:
  jj=json.loads(data)
  twit=jj["text"]
   print twit.encode('utf-8')
print "done"

The above few lines of Python code can be directly used to generate candidate passwords, which means keeping the whole sentence of the tweet. Another approach is to use each word of the tweet as a candidate password. Furthermore, an interesting idea is to combine tweet words with others.

What we can do is generate combinations of 4 words. Best results are by combining with or without space separators.

Here is a small Python script I wrote to performe this task, the input file is “tweets.txt”:

import sys
def combinations(words, length):
    if length == 0:
        return []
    result = [[word] for word in words]
    while length > 1:
        new_result = []
        for combo in result:
            new_result.extend(combo + [word] for word in words)
        result = new_result[:]
        length -= 1
    return result
filein=open("tweets.txt","r")
linesin=filein.readlines()
for i in linesin:
  thisline=i.rstrip("\n").split(" ")
  for j in combinations(thisline,4):
    print '%s' % ''.join(map(str,j))
    print '%s' % ' '.join(map(str,j))
  for j in combinations(thisline,3):
    print '%s' % ''.join(map(str,j))
    print '%s' % ' '.join(map(str,j))
  for j in combinations(thisline,2):
    print '%s' % ''.join(map(str,j))
    print '%s' % ' '.join(map(str,j))
  for j in thisline[:]:
    print j

As far as size is concerned, 10 seconds of live Twitter feed will give you about 1.5 MB and about 600 tweets. This size can be reduced down to 50 KB when keeping only the parsed tweet contents. This combination script will give you around 50 Million candidate passwords to test.

Those two approaches, are not the most effective for cracking million passwords. But for sure, they will give you interesting results such as passwords considered as very strong that have even resisted to lots of GPUs’ on fire.

CONCLUSION

As you might expect, we are not professional password crackers. Password cracking is a hobby for us. Actually, our hardware resources are limited. And bruteforcing passwords is not the most time friendly way, unless you own many GPUs and strong hardware. For this reason, we are tryining to discover new and effective techniques to crack complex passwords.

But always keep in mind that any platforms, websites and online services are never entirely protected against hacking and data leaks. So we would like to give some advices in order to protect your passwords in case critical senarios such as LinkedIN leak happen:

• Never share passwords
• Never use the same password
• Always use strong passwords
• Do not use common words
• Change your passwords in a regular basis

We hope you enjoyed reading this article. Find attached at the end of this article our new wordlist as a late Xmas gift. And of course…

HAPPY NEW YEAR 2013!!!

ABOUT THE WORDLIST

M3G_THI_CTH_WORDLIST_CLEANED.zip
M3G_THI_CTH_WORDLIST_CLEANED.zip
Version: 1.0

~16M Words

LinkedIN
Gamigo
Adobe
Blizzard
eHarmony
Geissens
NVidia
Stratfor
Project Whitefox
Various leaks collected from Pastebin

Author:	Collaborative_Work
Category:	wordlist_M3G_THI_CTH
Date:	December 31, 2012

75.8 MB
1429 Downloads
Details...

Leaks

LinkedIN
Gamigo
Adobe
Blizzard
eHarmony
Geissens
NVidia
Stratfor
Project Whitefox
Various leaks collected from Pastebin

Some Results

LinkedIN*:
        Loaded 6458020 password hashes SHA-1 LinkedIn
        Remaining 1078419 password hashes
LinkedIN**: (CLEANED NO DUPS)
        Loaded 5787239 password hashes SHA-1 LinkedIn
        Remaining 880786 password hashes
Gamigo:
        Loaded 7004341 password hashes MD5
        Remaining 1019934 password hashes
Adobe:
        Loaded 630 password hashes MD5
        Remaining 95 password hashes
Blizzard:
        Loaded 15932 password hashes MD5
        Remaining 4967 password hashes
eHarmony:
        Loaded 1513805 password hashes MD5
        Remaining 134345 password hashes
Geissens:
        Loaded 32502 password hashes MD5
        Remaining 4180 password hashes
NVidia:
        Loaded 791 password hashes MD5
        Remaining 354 password hashes
Stratfor:
        Loaded 822666 password hashes MD5
        Remaining 58694 password hashes

*, ** The initial LinkedIN hashlist contains 00000ed and non-00000ed SHA1 hashes. A lot of 00000ed hashes still have their duplicate non-00000ed hash in the list. For instance, if you crack the initial LinkedIN hashes with our wordlist you will find 473148 duplicates between 00000ed and non-00000ed, and if you are using John the Ripper with –format:raw-sha1-linkedin you will need to run the process twice to write duplicates (either the 00000ed or non-00000ed version) in your POT file. If you have already considered duplicates as non-useful, then the right results to consider are the ones from the CLEANED version.

Some Pipal Analysis

• LinkedIN: M3G_THI_CTH_WORDLIST_LinkedIN_PIPAL.txt
• Gamigo: M3G_THI_CTH_WORDLIST_Gamigo_PIPAL.txt
• eHarmony: M3G_THI_CTH_WORDLIST_eHarmony_PIPAL.txt
• Stratfor: M3G_THI_CTH_WORDLIST_stratfor_PIPAL.txt

FINAL NOTICE

The wordlist provided in this article has been created using all the presented cracking techniques against public leaks only. Do not expect to find new passwords using the same leaks and techniques presented here.

As always it is up to the reader to use this wordlist to do password recovery. We do not take any responsibility if some of your passwords can be found in this wordlist or be recovered using our techniques. Be aware that the best way to protect you is always to change your passwords as often as possible.

Incoming search terms:

• rockyou yahoo wordlist (13)
• john the ripper dynamic conf (11)
• famous passwords (11)
• korelogic rules best80 (9)
• john the ripper mask (9)
• hashcat brute force example (8)
• john the ripper rules (7)
• john make charset (5)
• hashcat examples (5)
• m3g9tr0n_122Million_Passwords_WordLists (5)

Android and iOS devices are today a prime target for hackers, and for good reason, two of the main factors of the perfect attack are joined here while exceeding any of the attacker’s expectations.

This article is about the ironha1l Tool Suite I have created. This article is intended to provide an understandable explanation about the problematic I faced during my researches to develop ironha1l. You will also find in this article a lot of relevant information if you are a jailbreak beginner. Most of the information here are part of my own research but also comes from external sources. I tried to remain as reliable as possible. Feel free to comment my work!

Updates: (subscribe to my twitter to get notified)

• 10/10/2012 – ironha1l sources available!

Why attacking smartphones with smartphones?

The primary factor is the quality and relevance of the information available on iOS and Android devices. Smartphones like the iPhone and the Galaxy SIII to name but a few, have been created to assist a large majority of our daily activities. Consequently, they contain a huge amount of data about our life and habits such as address book, pictures, emails, text messages, GPS location data history and much more. We can also find web browser history and all cache, credentials and data of third party applications such as online banking applications.

The second factor is accessibility, and even more so, the huge amount of attack vectors. These operating systems are mainly used in mobile phone devices and provide many access points for potential attacks. First of all, the mobile aspect of these devices can be used for data injections via the baseband, meaning injections via 3G/Edge/GSM protocols with text messages and voice calls. And even further, some attacks can be made on SIM cards, for example spoofing adapters are mainly used today for unlocking. More wireless access points such as WiFi, Bluetooth, IrDa, NFC constitute a large part of the vector attack panel and can be used to remote access the targeted device. Finally, some last attack vectors such as USB, serial port, SD card reader, audio port, touchscreen and camera provide to the attacker some physical and potentially vulnerable access points.

About the PoC scenario and its limitations

The main idea I had about a smartphone security related scenario was to show the two aspects described in this foreword. The revolutionary aspect of these mobile operating systems in term of functionality versus their incredible weakness in term of security. Therefore, the attacker is equipped with an Android 4.0 (Ice Cream Sandwich) smartphone in the aim to access sensitive data of an iOS unjailbroken device such as iPhone 3GS/4, iPod Touch 3G/4G or iPad according to the limera1n exploit limitations. The iOS version does not matter here, as the limera1n exploit does not depend of the operating system version running on the device. The attacker has only one constraint, which is to use the USB port of both devices to inject data and proceed to the data theft from Android. The aim of the attacker is to get the maximum amount of sensitive data available on the iOS device, such as pictures, emails, contacts, etc. The attack must be fast and discreet (a few minutes), and must be cancellable at any time. The use of an Android smartphone as been preferred for these reasons, due to its discretion, offensive functionalities and performances.

In this article, the term iDevice is used to refer to any iOS devices vulnerable to the limera1n exploit.

Bypassing iOS security

Before going further it is important to enumerate some of the main security features available on iOS. The same goes for the architecture security features, particularly the boot process and partitioning system.

iOS partitioning system and its biggest security feature

All iDevices have the particularity to contains a 8GB to 64GB flash memory split in two distinct partitions. The first one in read only contains the operating system iOS while the other one is dedicated to user data and have read and write permissions. The data partition contains user documents, applications, pictures, and other various user files. This flash memory is hardware encrypted using an AES-256 crypto-processor soldered on the iDevice motherboard right on the path between flash memory and RAM. Meaning that anything that comes from the flash memory to go in RAM is decrypted and anything that comes from RAM to go in the flash memory is encrypted. Nothing can transit without being encrypted/decrypted by the crypto-processor, thus you cannot manually extract the flash memory and read data from it, because anything is encrypted with AES-256.

UID (Unique ID) and GID (Group ID) are two keys soldered inside the crypto-processor and used to encrypt or decrypt. These keys are only accessible by the crypto-processor itself, they cannot be software requested or dumped (hypothetically a covert channel attack could do the trick). The UID key is unique for any iDevice and is not registered in Apple Databases (but we do not have proof for that), the GID key is the same for iDevices of the same class, meaning for example that any iPhone 3GS will have  the same GID key but each one will have a unique UID key. On top of that, these keys can be combined in addition to the passcode key (derived from the user passcode to unlock the iDevice) or any other external key, which creates various protection classes.

These protection classes are then used to encrypt some user data on top of the already hardware encrypted flash memory. Thus, if you successfully access the data partition, some files will remain encrypted with either the passcode key or other external keys combined to the UID key, which is the case for emails for example. So once the iDevice is locked, files are completely secured due to the missing passcode key. And brute-forcing the passcode key can only be achieved on the iDevice, because the decrypt function is called inside the crypto-processor which combine the given key with the UID key.

According to iOS Hacker’s Handbook it takes about 18 minutes in the worst case scenario to bruteforce a 4 digits passcode (iOS default scheme), which is even worse if the user change his passcode for a alphanumerical passcode (in that case it can take years to bruteforce ). There are no time limitations in case you bruteforce the passcode directly by calling the decrypt function of the crypto-processor. But in the case you attempt to manually bruteforce the passcode directly from the iOS unlock screen you will face these limitations that exponentially increase when a wrong passcode is entered.

Fortunately for attackers, only a few amount of data files are encrypted using the passcode key. Most of the files remain unencrypted, and some protections I talked about here were implemented during the iOS development. Meaning that old iOS versions are less protected that the newest ones, unfortunately the ones I presented here are all integrated to iOS 5.

Various exploits but only one goal

There are three categories of exploits on iOS, each one refers to a particular boot module of iOS.

• Bootrom (also called SecureROM by Apple) Exploits
• iBoot Exploits
• Userland Exploits

Bootrom exploits are the most powerful, because the bootrom is the first piece of code executed on the iDevice boot process. This bootrom is read only and cannot be updated nor modified, it is soldered on the iDevice. Thus a bootrom vulnerability cannot be fixed by Apple on existing and already sold devices. Actually there are only one bootrom exploit, which is called limera1n and created by George Hotz. This bootrom vulnerability has not been patched by Apple until the next hardware revision with Apple A5 processors and upper (meaning iPhone 4S, iPad 2, etc.) only  iPhone 3GS/4, iPod Touch 3G/4G and iPad are vulnerable. The limera1n exploit breaks the signature check for any elements of the boot process, meaning you can boot with an alternated or custom boot chain. In addition to that a bootrom exploit can be used to decrypt Apple GID encrypted files contained in IPSW archives (used to restore or upgrade iDevices). IPSW contains iOS and various GID encrypted data, that can be decrypted using with such an exploit by calling the crypto-processor function that uses the GID key. Attackers can then patch and alter these decrypted files for their own purpose, inject and boot with these files by the use of limera1n.

iBoot is the boot process part that launches the iOS kernel. These kind of vulnerabilities can lead to an untethered Jailbreak. Finding a vulnerability at this level is as powerful as a bootrom vulnerability in term of functionalities. Unfortunately such a vulnerability can be quickly patched by Apple in a next iOS update. iBoot vulnerabilities are not used in ironha1l, but it is important to have in mind the entire boot process for the next part of this article.

Finally, userland vulnerabilities are at the top level of iOS at the same level of running iOS applications. Exploiting such a vulnerability is very hard, and only allows the attacker to access mobile (or root) privileges. But yet, the attacker needs first to get out of the sandboxed application where the vulnerability was exploited. I will not talk more about this kind of exploit here.

DFU mode

The DFU mode (Device Firmware Update) is a special mode in which the device loads a specific code from the bootrom. This mode is also available in the Nintendo DS for example, and is not Apple property. This executed code allows the device to accept boot elements from the sync port of the iDevice (I prefer calling it the USB port). This DFU mode is mainly used when the device is software bricked, and even if the classic restore mode of the iDevice is broken. The DFU mode once detected by iTunes will receive boot elements from it. Those elements are from the IPSW archive, and are composed of iBSS, iBEC, DeviceTree, KernelCache and Ramdisk. The ramdisk file is a container which contains a very basic version of iOS only used to flash the device with a new iOS version sent by iTunes. This ramdisk is a very good basis for an attacker to access the iDevice partitions.

To put your iDevice in DFU mode, you first need to connect it to a USB host device, to boot the iDevice while maintaining the HOME and POWER button pressed during 8 seconds, then release the POWER button only while maintaining the HOME button still pressed. After some seconds, the iDevice should be in DFU mode. This mode is visually indistinguishable, only the host device knows if the connected iDevice is in DFU mode as it receives a DFU notification.

In 2012, George Hotz publish his limera1n exploit which allows the use of unsigned boot elements on vulnerable iDevices. This vulnerability is a memory overflow in the bootrom. With such an exploit, jailbreakers are able to modify the iOS ramdisk used in DFU mode, for example by editing the /sbin/launchd binary which is used to launch other binaries and scripts at boot, such as mounting partitions in read and write mode. This launchd binary will be used for example to execute sshd on our ramdisk.

Tumbling down the rabbit hole

 This part is dedicated to the development and creation of the ironha1l and DFOwn tools. Those tools were created to inject and execute a custom ramdisk containing a SSH server on a targeted iDevice using an Android device.

USB reverse

As previously described, what is interesting for us is to exploit the bootrom vulnerability with limera1n in order to inject a custom boot chain on the targeted iDevice. Actually, jailbreak software such as Redsn0w can do that, but our aim here is quite different as we intend to do it with an Android phone, and there are no such existing tools for this operating system.

We have two choices. The first one is to modify existing and open source jailbreak tools and port them for Android platform. The second choice is to create a totally new tool optimized for Android. Given the aim to have a mastered full tool suite, and because I had time to learn and I love challenges, I decided to give a try to the second alternative. Consequently, USB reverse engineering in DFU mode was required to establish how data is sent to the iDevice and what are the USB transfer modes, headers and commands used for all the steps of the boot chain.

It is first necessary to create an USB debug environment or as I like to call it, a Man In The Middle USB. There are several ways to do this, either you can directly reverse each element of the boot chain to understand how the following elements must be injected, or you can modify the Mac OS or Windows USB driver to activate the USB debug mode, you can also use a sniffer device between attached to your USB cable, or you can even directly use a Windows Virtual Machine on your Linux system. In this last case the use of Wireshark running on Linux will do the trick, as it can sniff USB communications, especially in our case the USB communications between iTunes running in the Windows VM with our attached USB iDevice. This last method is quite buggy, but allows us to see the USB communication protocol quite easily.

The full description of the DFU mode is in the meantime available in the Universal Serial Bus Device Class Specification for Device Firmware Upgrade publication. With the help of this documentation and various USB headers and requests sniffed with Wireshark, the exact iTunes behavior for data transfer in DFU mode has been established. It is important to note that all communications are in clear text, same apply for commands sent to the iDevice which are associated to each part of the boot process. Another observation, is that header values are specific to Apple and not documented.

Development of libironha1l and ironha1l

The biggest step in this project was to create the ironha1l tool and its library libironha1l. This library provides functions to inject data to the iDevice in DFU mode and is strictly based on libusb. The principal advantage of this last point is the portability of libusb, and its compatibility with Android. ironha1l is the application that coordinates all the various injections of our custom boot chain, based on the DFU protocol reverse established previously. Meaning the injection of iBSS, iBEC, DeviceTree, KernelCache and Ramdisk modified files. These custom files can be extracted and automatically created using the iPhone-dataprotection tool suite of Sogeti.

The development of ironha1l and libironha1l lasted 3 months and contains more than 1000 lines of code. ironha1l and libironha1l are written in C. The ironha1l tool also comes with the limera1n exploit and payload (sources available on the Jailbreak community Wiki).

During the development, a number of difficulties came to light. The first one and the most difficult was the ramdisk size. During the first attempts to inject a custom ramdisk it has been established that a ramdisk size higher than ~10 MB could not be executed on the iDevice, unfortunately a ramdisk containing a SSH server cannot handle such a limited size. The solution was in fact, to alter a bit in the control transfer header specific to the ramdisk. This solution was established after many days by testing ramdom and various header values. Unfortunately it is still unknown why does this bit value bypass the size limitation.

Two more issues, this time related to limera1n, came to light during the libironha1l development. The first one is that limera1n consists of two elements, the source code and the payload. The limera1n payload is unfortunately not documented and is not open source, but it is easily extractable from Jailbreak tools such as RedSn0w. To extract this payload you need to apply the same reverse process as describer in the USB reverse part of this article. Meaning using Redsn0w in a Windows Virtual Machine on your Linux platform and dump all USB transmissions with Wireshark in Man In The Middle. Once the payload dumped, it has been tested and once again came another problem.

The limera1n exploit is based on the principal buffer overflow vulnerability of the bootrom, but unfortunately a USB control command must be sent to the iDevice during the injection process of the payload, so that the payload can be executed. If the control command reaches the iDevice too late, the payload is not executed. Le biggest problem is that generally the USB commands cannot overlap with one another with libusb, and this it is not possible to send the specific USB control command without interrupting the previous injection. The trick and the solution is to play with reception timeouts (acknowledgment replies), the payload is sent to the iDevice with a big timeout and due to its big size the iDevice will take a certain amount of time to deal with it. During this short amount of time (1 to 10 milliseconds), the control command that executes the payload must be sent, this time with a timeout lower than 10 milliseconds to match the iDevice process timing (ideally 1 millisecond).

Here is the prototype of ironha1l:

usage: ./ironha1l -h (help)
	[-v verbose_level{0,1,2,3}] [-d libusb_debug_level{0,1,2,3}]
	[-l limera1n_file] [-i iBSS_file] [-b iBEC_file]
	[-t DeviceTree_file] [-r Ramdisk_file] [-k KernelCache_file]
	[-c iBSS_command]
	[-z idBus]

Once our tool working and injecting correctly all elements of our customized boot chain, the iDevice boots on our ramdisk containing a SSH server. Meanwhile, on the client side we need to find a way to connect to this SSH server via USB. Fortunately this task is not a real big deal. The MobileDevice framework of Apple (included in iTunes for Windows) contains a daemon called usbmuxd. This daemon is typically what we need as it creates a TCP tunnel over USB to communicate with the iDevice services (in our case the ssh server). Of course, the usbmuxd used by Apple is not open source, but a bunch of great developers have created a usbmuxd version open source that comes with iproxy which is used for port forwarding. So combining usbmuxd with iproxy creates a local port that communicate through USB directly with the SSH socket in listening mode on the iDevice. It was not a big deal to port usbmuxd and iproxy for Android, as these tools are working under Linux. It should also be noted that this open source project is not maintained by Apple, thus each time a new device comes out usbmuxd must be updated by developers and apparently this is not an easy task. In our case our targeted iDevices are all working and supported by the latest version of usbmuxd.

The application portability is a very important aspect, it has been decided to directly integrate libusb and usbmuxd in ironha1l. Before compiling, a script downloads, patches and configures the latest libusb and usbmuxd version. The ironha1l tool suite is compatible with Linux, UNIX, BSD, Mac OS and Android.

Android port and GUI application DFOwn

Since the ICS version, Android supports USB host, which allows to connect to the USB port a USB device such as USB storage, mouse, etc. using a micro USB to USB host adaptor.

The first thing to do with Android was to root the OS, which is the easiest hacking task ever. The second step was to make sure DFU mode is well detected by Android. For this task either we can use lsusb ported on Android using Android SDK, or we can also use the devices tool contained in the ironha1l tool suite. The USB enumeration has been made on two devices, a SAMSUNG Galaxy SII and a SAMSUNG Galaxy Nexus, both under Android 4.0.3. It appeared that the Galaxy SII could not detect any iDevice in DFU mode (other modes were successfully detected). Even after multiple testings using different USB devices, even with a self-powered USB HUB the problem was still there and was not identified. Fortunately the Galaxy Nexus was working perfectly well under Android 4.0.3. Thus for the next part of the development this smartphone was used.

Android is a Linux based operating system, the compilation of ironha1l tool suite with Android SDK was almost instantaneous. There was only some very basic problems such as the creation of specific Makefiles for Android. The ironha1l binaries were thus sent to the Galaxy Nexus device to be used later with the JAVA GUI application DFOwn. The tools ironha1l, usbmuxd and iproxy have been manually tested and were working perfectly well. The custom boot chain was injected and the Android device can communicate with the SSH server of the Android loaded ramdisk. The iDevice partitions were accessible after mounting in read and/or write, it is thus possible to alter or download files from them directly from our Android smartphone. As described previously, some files such as email database are encrypted and cannot be decrypted without knowing the iDevice passcode, which is not the case for pictures and movies for example, or text message database and contact database.

The DFOwn JAVA application was created to provide a easy and fast way to use ironha1l and pwn (understand limera1n exploit + custom boot chain injection) the attached iDevice in DFU mode. DFOwn does not currently integrate a SFTP client which thus require the user to use its own SFTP client such as AndFTP to access the iDevice data.

Conclusion

DFOwn and ironha1l are Proof of Concept applications, their goal is to prove an attacker can gain access to your iDevice smartphone files easily with minimal hardware such as an Android smartphone. DFOwn takes about 1 minute to complete the ironha1l boot process on an iDevice. The application is fast, quiet and easy to use. Transfers can go up to 1.5 Mo/s depending of the iDevice and Android device used, which allows the attacker to get a large amount of files very quickly.

Sources Available HERE!

References

Incoming search terms:

• usbmuxd windows (18)
• openiBoot (15)
• android libusb (14)
• ironha1l (10)
• oclhashcat-plus brute force (9)
• libusb android (6)
• ios usbmuxd tutorial (4)
• limera1n explits verbose mode (3)
• mpirun john Crash recovery file is locked: (3)
• iphone 4 gsm dfu usb drivers portable (3)

This is the story about how I cracked 122 million* password hashes with John the Ripper and oclHashcat-plus.

Author: m3g9tr0n, Copy Editor: Thireus.

It was several months ago, when I (m3g9tr0n) saw a tweet from KoreLogic about a torrent file containing various password hash lists for a total of 146 million passwords. This very big amount of password hashes at first discouraged me, as I only own a classic computer configuration with an AMD Phenom II 4 cores at 3,2 Mhz in addition to an ATI/AMD 5770 graphics card. But at least, I really wanted to give them a try because the field of password cracking fascinates me.

The password cracking tools I used during this long trip were John the Ripper and oclHashcat-plus. This article is about cracking the provided MD5 hashes of KoreLogic only, but the same strategy was also applied to SHA1 hashes.

Updates:

• 08/29/2012 – New example in John the Ripper section: “Crack double MD5 hashes with the help of dict2hash.pl script”
• 08/29/2012 – New download! All in one sorted and cleaned version.

Dealing with hashes…

First of all the KoreLogic torrent file file must be decompressed, it contains a folder named “hashes”. Let’s see the content of this folder…

root@m3g9tr0n:~/hashes$ ls
longer_salts  raw-md5.hashes.txt  salted_with_md5  SHA1  vBulletin-v3.8.4

We will concentrate here on the raw-md5.hashes.txt list. This file is 4.3 GB and includes 139444502 lines according to wc utility.

root@m3g9tr0n:~/hashes$ wc -l raw-md5.hashes.txt 
139444502 raw-md5.hashes.txt

As you consider, both John the Ripper and oclHashcat-plus are not able to load this file because it is too big. For that reason, we need to split this file. Under Linux we have a nice utility called split that does this job very well.

root@m3g9tr0n:~$ split --help
Usage: split [OPTION]... [INPUT [PREFIX]]
Output fixed-size pieces of INPUT to PREFIXaa, PREFIXab, ...; default
size is 1000 lines, and default PREFIX is `x'.  With no INPUT, or when INPUT
is -, read standard input.

Mandatory arguments to long options are mandatory for short options too.
  -a, --suffix-length=N   use suffixes of length N (default 2)
  -b, --bytes=SIZE        put SIZE bytes per output file
  -C, --line-bytes=SIZE   put at most SIZE bytes of lines per output file
  -d, --numeric-suffixes  use numeric suffixes instead of alphabetic
  -l, --lines=NUMBER      put NUMBER lines per output file
      --verbose           print a diagnostic just before each
                            output file is opened
      --help     display this help and exit
      --version  output version information and exit

SIZE may be (or may be an integer optionally followed by) one of following:
KB 1000, K 1024, MB 1000*1000, M 1024*1024, and so on for G, T, P, E, Z, Y.

We can use the –lines=NUMBER parameter to split our raw-md5.hashes.txt file.

root@m3g9tr0n:~/hashes$ split -l 3000000 raw-md5.hashes.txt part

Note that we can also split the file based on the amount of MBs by taking into consideration that each MD5 hash is 32 bytes long.

Cracking Passwords with oclHashcat-plus

I started with oclHashcat-plus because it contains the –remove option, which enable remove of hash from hashfile once it is cracked and is really convenient. The only limitation oclHashcat-plus has, is the constraint on password length. In other words, it is only able to crack passwords up to 15 characters. The rules that I used for oclHashcat-plus are base64.rule, passwordspro.rule, T0XlC.rule and in some cases d3ad0ne.rule. There rules can be found directly from the oclHashcat-plus suite.

Bruteforce techniques were not my first choice. I used wordlists which I download from g0tm1lk’s blogspot. You will find on g0tmi1k’s article other external links for more wordlists. The biggest part of cracking process was done by using these wordlists with the rules mentioned above. Let’s see some examples…

Using a single rule:

./oclHashcat-plus64.bin -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic -r rules/best64.rule -o Ultimate_Crack/eNtr0pY_1 --remove

Using Rules’ combination:

./oclHashcat-plus64.bin -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic -r rules/best64.rule r rules/passwordspro.rule -o Ultimate_Crack/eNtr0pY_1 --remove

Bruteforce attack with mask (you can specify whatever charset you want):

./oclHashcat-plus64.bin -a 3 -1 ?l?d?u?s -m 0 ~/hashes/md5_1 ?1?1?1?1?1?1?1?1 -o Ultimate_Crack/eNtr0pY_1 --remove

Combination attack:

./oclHashcat-plus64.bin -a 1 -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic ~/Wordlists/list -o Ultimate_Crack/eNtr0pY_1 --remove

Combination attack with rules:

./oclHashcat-plus64.bin -a 1 -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic ~/Wordlists/list -r rules/passwordspro.rule -o Ultimate_Crack/eNtr0pY_1 --remove

Permutation attack:

./oclHashcat-plus64.bin -a 4 -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic -o Ultimate_Crack/eNtr0pY_1 --remove

Permutation attack with rules:

./oclHashcat-plus64.bin -a 4 -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic -r rules/best64.rule -o Ultimate_Crack/eNtr0pY_1 --remove

In some cases, I used the hybrid + mask attack technique:

./oclHashcat-plus64.bin -a 6 -1 ?l?d -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic ?1?1 -o Ultimate_Crack/eNtr0pY_1 --remove

Hybrid + mask attack with rules:

./oclHashcat-plus64.bin -a 6 -1 ?l?d -m 0 ~/hashes/md5_1 ~/Wordlists/d3ad0ne.dic ?1?1 -r rules/best64.rule -o Ultimate_Crack/eNtr0pY_1 --remove

At this point, I did not use these last two methods as they are very time consuming. I rather found a better one using KoreLogic’s Rules for John the Ripper by piping the output of John the Ripper to oclHashcat-plus. As I mentioned, oclHashcat-plus is able to crack passwords up to 15 characters. For that reason, I had to define everytime, via the –stdout option, the length of the produced word. If you own a very fast GPU you do not have to use the following example.

./john --wordlist=~/Wordlists/all.lst -rules:KoreLogicRulesPrependYears --stdout=10 | ./oclHashcat-plus64.bin -m 0 ~/hashes/md5_1 -o Ultimate_Crack/eNtr0pY_1 --remove

Of course you can use other prepend rules created from Korelogic, like KoreLogicRulesPrependNumNum, or even better create your own rules!

It was time to produce a wordlist from the cracked passwords and use it to crack the remaining hashes. From eNtr0pY_1, I removed the MD5 hashes with the following command.

cut -b34- eNtr0pY_1 > eNtr0pY_1.dic

By using the above produced wordlist, a big amount of MD5 hashes were cracked with the fingerprint attack. You can read more about this attack from Martin Bos @purehate and I guarantee that this technique is very successful!

Of course you can also use the binaries included into hashcat-utils and pipe the output of each util to oclHashcat-plus.

root@m3g9tr0n:~/oclHashcat-plus-0.08/hashcat-utils$ ls
combinator.bin  expander.bin  gate.bin  len.bin  mp32.bin  permute.bin  prepare.bin  req.bin  splitlen.bin

Cracking Passwords with John the Ripper

After testing all my wordlist collection and after several days, it was time to move to John the Ripper for cracking the rest of password hashes…

I used magnum-ripper compiled with OpenCL for ATI/AMD graphics card because I wanted to use the –format=raw-md5-opencl parameter. Compared to –format=raw-md5, it is way faster as it uses your CPU and GPU!

The Rules that were used with John the Ripper are wordlist, Single, NT, Extra, KoreLogicRulesAppendNumbers_and_Specials_Simple, KoreLogicRulesAppend6Num, KoreLogicRulesPrependAndAppendSpecial, KoreLogicRulesAppendNumNum_AddSpecialEverywhere, KoreLogicRulesAppendNumNumNum_AddSpecialEverywhere and KoreLogicRulesL33t.

Furthermore you can download these rules and add them to your john.conf file.

• http://paste2.org/p/2048517
• http://paste2.org/p/2048512

Let’s see now some examples with John the Ripper…

Using –rules=Single

./john --format=raw-md5-opencl --wordlist=../../Wordlists/all.lst --rules:Single ~/hashes/md5_1

The results of cracked hashes are stored in the john.pot file by default. You can examine its contents with cat, more, head and tail.

root@m3g9tr0n:~/Tools/Password_Cracking/magnum-jumbo-OpenCL/run$ tail -n 9 john.pot 
$MD5$0fad81e7a61b47d387dde893fcf8e88a:anacarolinagu
$MD5$0f82fc9a81f5db07eb9289767390fd2b:fabulousfoodsu
$MD5$0e22933267b2e7df062703c4e5842029:fabuloustravelu
$MD5$0d40086a54fefe993c9816d1441672ac:modularhomeu
$MD5$0ed8181fc4d18e260dd8e36633124bfd:greenshoppingu
$MD5$0d6e8da4017ec5c384ac5536087da44d:lawofattractionu
$MD5$0eb916d3c6a66a32cedd4acc6edb1dbb:hotreportu
$MD5$0e241f99b5c13d56686ec618ab54d5fa:flightsandholidaysu
$MD5$0f3c99478362aae389d2cbf716394269:stthomasmoresu

To produce a wordlist from the john.pot file, you can use the following command.

cut -d: -f 2- john.pot | sort -u > cracked.dic

The created wordlist can be used to crack more hashes when combined with the abovementioned rules.

When I was cracking MD5 hashes with oclHashcat-plus, I observed that some produced passwords were rejected. This is because oclHashcat-plus has a limitation about characters’ length. For that reason, I piped hashcat’s output to John the Ripper with the additional advantage of using hashcat rules with John the Ripper.

./hashcat-cli64.bin --stdout ~/Wordlists/d3ad0ne.dic -r rules/best64.rule | ./john --format=raw-md5-opencl --stdin ~/hashes/md5_1

After trying all the wordlists combined with the rules mentioned above, it was time to move to bruteforce attacks with John the Ripper. Unfortunately, John the Ripper does not use the mask attacks to produce passwords when implementing bruteforce attacks. We have to create our own charset based on cracked passwords contained in john.pot.

./john --make-charset=eNtr0pY.chr
Loaded 7948325 plaintexts
Generating charsets... 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 DONE
Generating cracking order... DONE
Successfully written charset file: eNtr0pY.chr (95 characters)

Many of you will wonder about “31 DONE”… This is just because I compiled John the Ripper with 31 characters’ length. By default, John the Ripper is compliled with 8 characters’ length, so it is best to change it by modifying the following lines of the header file params.h located in the scr folder of John the Ripper.

#define CHARSET_MIN                     ' '
#define CHARSET_MAX                     0x7E
#define CHARSET_SIZE                    (CHARSET_MAX - CHARSET_MIN + 1)
#define CHARSET_LENGTH                  8 //Change that to 31 or whatever you wish

At last you have to include your created charset to john.conf as given in this example:

# Incremental modes
[Incremental:eNtr0pY]
File = $JOHN/eNtr0pY.chr
MinLen = 0
MaxLen = 31
CharCount = 95

Now it is time to use bruteforce attacks with our own charstet!

./john --format=raw-md5-opencl --incremental=eNtr0pY ~/hashes/md5_1

If you look into john.conf you will see some bruteforce attack modes characterized as extrernals. These are Double, Strip, Keyboard (which uses neighbor combinations produced from keyboard characters), KnownForce, DateTime, Repeats, Sequence, Subsets and DumbForce for crazy password formats.

./john --format=raw-md5-opencl --external=DumbForce ~/hashes/md5_1

We would also like to crack double MD5 hashes with the help of dict2hash.pl script provided here.

perl dict2hash.pl < rockyou.txt | ./john --format=raw-md5-opencl --stdin ~/md5_1

Here you can see some samples of cracked md5s with John the Ripper:

• http://pastebin.com/uaGFXkBA
• http://pastebin.com/LT5Gda9L

Personally, I believe a password like “$MD5$0b26a0faf1344d6e772bf55628e10e29:n34=mn { .clipboard $me }” is impossible to crack with bruteforce attacks.

Note: All the abovementioned techniques can be used with oclHashcat-plus by defining -m 100 and with John the Ripper by defining –format=raw-sha1-opencl for SHA1 cracking with OpenCL!

Password Analysis

Finally, it worths to see an analysis using pipal (a password analyser) of a collected sample generated from cracking results.

root@m3g9tr0n:~/pipal$ ruby1.9.1 pipal.rb \
-o eNtr0pY_1 ~/Wordlists/Ultimate/Part1/eNtr0pY_5.dic
Total entries = 759103
Total unique entries = 758299

Top 10 passwords
niezgadniesz123 = 3 (0.0%)
ubqu = 3 (0.0%)
amonys = 3 (0.0%)
centralitie = 3 (0.0%)
bobydu = 3 (0.0%)
hanghuynh = 3 (0.0%)
hmadyousi = 3 (0.0%)
matthewperman = 3 (0.0%)
shadowninja2 = 3 (0.0%)
lhz4 = 3 (0.0%)

Top 10 base words
august = 219 (0.03%)
july = 205 (0.03%)
april = 199 (0.03%)
june = 195 (0.03%)
march = 165 (0.02%)
alex = 161 (0.02%)
love = 132 (0.02%)
chris = 130 (0.02%)
daniel = 128 (0.02%)
dragon = 122 (0.02%)

Password length (length ordered)
1 = 13 (0.0%)
2 = 103 (0.01%)
3 = 1332 (0.18%)
4 = 16781 (2.21%)
5 = 19831 (2.61%)
6 = 95800 (12.62%)
7 = 202414 (26.66%)
8 = 158562 (20.89%)
9 = 103855 (13.68%)
10 = 75652 (9.97%)
11 = 46023 (6.06%)
12 = 24997 (3.29%)
13 = 8423 (1.11%)
14 = 3772 (0.5%)
15 = 1560 (0.21%)

Password length (count ordered)
7 = 202414 (26.66%)
8 = 158562 (20.89%)
9 = 103855 (13.68%)
6 = 95800 (12.62%)
10 = 75652 (9.97%)
11 = 46023 (6.06%)
12 = 24997 (3.29%)
5 = 19831 (2.61%)
4 = 16781 (2.21%)
13 = 8423 (1.11%)
14 = 3772 (0.5%)
15 = 1560 (0.21%)
3 = 1332 (0.18%)
2 = 103 (0.01%)
1 = 13 (0.0%)

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00000000001111111
01234567890123456

One to six characters = 133854 (17.63%)
One to eight characters = 494828 (65.19%)
More than eight characters = 264275 (34.81%)

Only lowercase alpha = 154996 (20.42%)
Only uppercase alpha = 14072 (1.85%)
Only alpha = 169068 (22.27%)
Only numeric = 119581 (15.75%)

First capital last symbol = 6088 (0.8%)
First capital last number = 73611 (9.7%)

Months
january = 109 (0.01%)
february = 45 (0.01%)
march = 247 (0.03%)
april = 251 (0.03%)
may = 850 (0.11%)
june = 246 (0.03%)
july = 223 (0.03%)
august = 300 (0.04%)
september = 80 (0.01%)
october = 134 (0.02%)
november = 113 (0.01%)
december = 115 (0.02%)

Days
monday = 59 (0.01%)
tuesday = 20 (0.0%)
wednesday = 7 (0.0%)
thursday = 38 (0.01%)
friday = 46 (0.01%)
saturday = 7 (0.0%)
sunday = 70 (0.01%)

Months (Abreviated)
jan = 1482 (0.2%)
feb = 249 (0.03%)
mar = 8397 (1.11%)
apr = 692 (0.09%)
may = 850 (0.11%)
jun = 889 (0.12%)
jul = 1051 (0.14%)
aug = 785 (0.1%)
sept = 215 (0.03%)
oct = 512 (0.07%)
nov = 821 (0.11%)
dec = 874 (0.12%)

Days (Abreviated)
mon = 4319 (0.57%)
tues = 28 (0.0%)
wed = 217 (0.03%)
thurs = 44 (0.01%)
fri = 758 (0.1%)
sat = 769 (0.1%)
sun = 1018 (0.13%)

Includes years
1975 = 411 (0.05%)
1976 = 388 (0.05%)
1977 = 446 (0.06%)
1978 = 432 (0.06%)
1979 = 441 (0.06%)
1980 = 541 (0.07%)
1981 = 453 (0.06%)
1982 = 519 (0.07%)
1983 = 533 (0.07%)
1984 = 603 (0.08%)
1985 = 585 (0.08%)
1986 = 616 (0.08%)
1987 = 710 (0.09%)
1988 = 641 (0.08%)
1989 = 941 (0.12%)
1990 = 931 (0.12%)
1991 = 995 (0.13%)
1992 = 935 (0.12%)
1993 = 905 (0.12%)
1994 = 907 (0.12%)
1995 = 4021 (0.53%)
1996 = 858 (0.11%)
1997 = 486 (0.06%)
1998 = 443 (0.06%)
1999 = 416 (0.05%)
2000 = 1024 (0.13%)
2001 = 643 (0.08%)
2002 = 586 (0.08%)
2003 = 1132 (0.15%)
2004 = 1254 (0.17%)
2005 = 796 (0.1%)
2006 = 818 (0.11%)
2007 = 1442 (0.19%)
2008 = 1019 (0.13%)
2009 = 742 (0.1%)
2010 = 767 (0.1%)
2011 = 516 (0.07%)
2012 = 925 (0.12%)
2013 = 165 (0.02%)
2014 = 142 (0.02%)
2015 = 146 (0.02%)
2016 = 118 (0.02%)
2017 = 139 (0.02%)
2018 = 131 (0.02%)
2019 = 172 (0.02%)
2020 = 179 (0.02%)

Years (Top 10)
1995 = 4021 (0.53%)
2007 = 1442 (0.19%)
2004 = 1254 (0.17%)
2003 = 1132 (0.15%)
2000 = 1024 (0.13%)
2008 = 1019 (0.13%)
1991 = 995 (0.13%)
1989 = 941 (0.12%)
1992 = 935 (0.12%)
1990 = 931 (0.12%)

Colours
black = 485 (0.06%)
blue = 549 (0.07%)
brown = 184 (0.02%)
gray = 89 (0.01%)
green = 348 (0.05%)
orange = 125 (0.02%)
pink = 262 (0.03%)
purple = 73 (0.01%)
red = 2974 (0.39%)
white = 179 (0.02%)
yellow = 85 (0.01%)
violet = 63 (0.01%)
indigo = 22 (0.0%)

Single digit on the end = 92080 (12.13%)
Two digits on the end = 87587 (11.54%)
Three digits on the end = 103715 (13.66%)

Last number
0 = 45407 (5.98%)
1 = 64764 (8.53%)
2 = 52570 (6.93%)
3 = 52890 (6.97%)
4 = 43719 (5.76%)
5 = 55185 (7.27%)
6 = 42826 (5.64%)
7 = 46169 (6.08%)
8 = 42475 (5.6%)
9 = 44930 (5.92%)

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0123456789

Last digit
1 = 64764 (8.53%)
5 = 55185 (7.27%)
3 = 52890 (6.97%)
2 = 52570 (6.93%)
7 = 46169 (6.08%)
0 = 45407 (5.98%)
9 = 44930 (5.92%)
4 = 43719 (5.76%)
6 = 42826 (5.64%)
8 = 42475 (5.6%)

Last 2 digits (Top 10)
95 = 14675 (1.93%)
23 = 12192 (1.61%)
12 = 9230 (1.22%)
11 = 8214 (1.08%)
01 = 7606 (1.0%)
00 = 7131 (0.94%)
07 = 6295 (0.83%)
10 = 6182 (0.81%)
21 = 5881 (0.77%)
99 = 5868 (0.77%)

Last 3 digits (Top 10)
123 = 6857 (0.9%)
995 = 4122 (0.54%)
971 = 2916 (0.38%)
972 = 2850 (0.38%)
007 = 2514 (0.33%)
000 = 1868 (0.25%)
234 = 1725 (0.23%)
666 = 1465 (0.19%)
777 = 1389 (0.18%)
004 = 1347 (0.18%)

Last 4 digits (Top 10)
1995 = 3886 (0.51%)
1234 = 1379 (0.18%)
2007 = 1325 (0.17%)
2004 = 1121 (0.15%)
2003 = 1016 (0.13%)
2008 = 869 (0.11%)
2000 = 846 (0.11%)
1991 = 819 (0.11%)
2012 = 809 (0.11%)
1990 = 789 (0.1%)

Last 5 digits (Top 10)
12345 = 743 (0.1%)
23456 = 652 (0.09%)
54321 = 189 (0.02%)
23123 = 140 (0.02%)
56789 = 127 (0.02%)
34567 = 102 (0.01%)
11111 = 99 (0.01%)
45678 = 75 (0.01%)
00000 = 73 (0.01%)
88888 = 68 (0.01%)

US Area Codes
971 = Oregon:  Metropolitan Portland,
               Salem/Keizer area,
               incl Cricket Wireless (OR)
972 = Texas: Dallas Metro (TX)
234 = NE Ohio: Canton, Akron (OH)

Character sets
loweralphanum: 330937 (43.6%)
loweralpha: 154996 (20.42%)
numeric: 119581 (15.75%)
mixedalphanum: 41121 (5.42%)
upperalphanum: 41078 (5.41%)
mixedalpha: 28464 (3.75%)
upperalpha: 14072 (1.85%)
loweralphaspecial: 10222 (1.35%)
loweralphaspecialnum: 5735 (0.76%)
mixedalphaspecial: 4724 (0.62%)
upperalphaspecial: 2939 (0.39%)
mixedalphaspecialnum: 2247 (0.3%)
specialnum: 648 (0.09%)
upperalphaspecialnum: 374 (0.05%)
special: 47 (0.01%)

Character set ordering
stringdigit: 349534 (46.05%)
allstring: 197532 (26.02%)
alldigit: 119581 (15.75%)
digitstring: 28873 (3.8%)
othermask: 18649 (2.46%)
stringdigitstring: 14577 (1.92%)
stringspecial: 10441 (1.38%)
digitstringdigit: 9981 (1.31%)
stringspecialstring: 5469 (0.72%)
stringspecialdigit: 3075 (0.41%)
specialstring: 834 (0.11%)
specialstringspecial: 510 (0.07%)
allspecial: 47 (0.01%)

Hashcat masks (Top 10)
?d?d?d?d?d?d?d: 85053 (11.2%)
?l?l?l?l?l?l: 38400 (5.06%)
?l?l?l?l?l?l?l?l: 36217 (4.77%)
?l?l?l?l?l?l?l: 35468 (4.67%)
?l?l?l?l?l?l?d?d?d: 24051 (3.17%)
?l?l?l?l?l?l?d?d: 18591 (2.45%)
?l?l?l?l?l?d?d?d: 18047 (2.38%)
?d?d?d?d?d?d: 16048 (2.11%)
?l?l?l?l?l?l?l?l?l: 14236 (1.88%)
?l?l?l?l?d?d?d: 13802 (1.82%)

Conclusion

This was a very time consuming and hard job because I do not own the fastest card. The whole cracking process took about 5 months to accomplish because I had to finish my studies about CCNP certification. The lesson learned from this is that with a good and smart dictionary combined with handy rules either for hashcat or John the Ripper even strong passwords can be cracked. Based on the upon statement, admins should use a stronger hash algorithm (with salt) to store your passwords or even better from your side just change your passwords in a regular basis.

Thanks for reading.
You can find me on twitter, @m3g9tr0n.

Downloads

You can download the results of cracked hashes:

m3g9tr0n_122Million_Passwords_WordLists.zip
m3g9tr0n_122Million_Passwords_WordLists.zip
Version: 1.0

Results of KoreLogic cracked hashes.
More info about theses hashes: https://www.korelogic.com/InfoSecSouthwest2012_Ripe_Hashes.html

eNtr0pY_1.dic
eNtr0pY_2.dic
eNtr0pY_3.dic
eNtr0pY_4.dic
eNtr0pY_5.dic
eNtr0pY_6.dic
eNtr0pY_7.dic
eNtr0pY_8.dic
eNtr0pY_9.dic
eNtr0pY_10.dic
eNtr0pY_11.dic
eNtr0pY_12.dic
eNtr0pY_13.dic
eNtr0pY_14.dic
eNtr0pY_15.dic
eNtr0pY_16.dic
eNtr0pY_17.dic
eNtr0pY_18.dic
eNtr0pY_19.dic
eNtr0pY_20.dic
eNtr0pY_21.dic
eNtr0pY_22.dic
eNtr0pY_23.dic
eNtr0pY_24.dic
eNtr0pY_25.dic
eNtr0pY_26.dic
eNtr0pY_27.dic
eNtr0pY_28.dic
eNtr0pY_29.dic
eNtr0pY_30.dic
eNtr0pY_31.dic

Author:	m3g9tr0n
Category:	WordList_m3g9tr0n
Date:	August 28, 2012

721.9 MB
1914 Downloads
Details...

The provided KoreLogic torrent file contains various but unique password hashes. For that reason you may find duplicated passwords in these wordlists, as a single password can be hashed using various algorithmes! Meaning that 122 million unique hashes (MD5, SHA1, double MD5, etc.) were cracked and result in 83,6 million unique passwords.

You can download the “all in one” version, cleaned and sorted:

m3g9tr0n_Passwords_WordList_CLEANED.zip
m3g9tr0n_Passwords_WordList_CLEANED.zip
Version: 1.0

Results of KoreLogic cracked hashes.
This is a cleaned (duplicate removal) and sorted version.
More info about theses hashes: https://www.korelogic.com/InfoSecSouthwest2012_Ripe_Hashes.html

eNtr0pY_ALL_uniq.dic

Author:	m3g9tr0n
Category:	WordList_m3g9tr0n
Date:	August 29, 2012

270.2 MB
1871 Downloads
Details...

export LC_ALL='C' && cat * | sort | uniq > eNtr0pY_ALL_sort_uniq.dic

References

• hashcat Wiki [ http://hashcat.net/wiki/ ]
• John the Ripper Wiki [ http://openwall.info/wiki/john ]

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