Tool Name: PRINCE (PRobability INfinite Chained Elements)
Version Reviewed: 0.12
Author: Jens Steube, (Atom from Hashcat)
OS Supported: Linux, Mac, and Windows
Password Crackers Supported: It is a command line tool so it will work with any cracker that accepts input from stdin

Blog Change History:

1/4/2015: Fixed some terminology after talking to Atom
1/4/2015: Removed a part in the Algorithm Design section that talked about a bug that has since been fixed in version 0.13
1/4/2015: Added an additional test with PRINCE and JtR Incremental after a dictionary attack
1/4/2015: Added a section for using PRINCE with oclHashcat

Brief Description: 

PRINCE is a password guess generator and can be thought of as an advanced Combinator attack. Rather than taking as input two different dictionaries and then outputting all the possible two word combinations though, PRINCE only has one input dictionary and builds "chains" of combined words. These chains can have 1 to N words from the input dictionary concatenated together. So for example if it is outputting guesses of length four, it could generate them using combinations from the input dictionary such as:

4 letter word
2 letter word + 2 letter word
1 letter word + 3 letter word
1 letter word + 1 letter word + 2 letter word
1 letter word + 2 letter word + 1 letter word
1 letter word + 1 letter word + 1 letter word + 1 letter word
..... (You get the idea)

Algorithm Design:

As of this time the source-code of PRINCE has not been released. Therefore this description is based solely on At0m's Passwords14 presentation, talking to At0m himself on IRC as well as running experiments with various small dictionaries using the tool itself and manually looking at the output.

As stated in the description, PRINCE combines words from the input dictionary to produce password guesses. The first step is processing the input dictionary. Feeding it an input dictionary of:

a
a

resulted it in generating the following guesses:

a
a
aa
aa
aaa
aaa
aaaa
aaaa
...(output cut to save space)

Therefore, it's pretty obvious that the tool does not perform duplicate detection when loading a file

Finding #1: Make sure you remove duplicate words from your input dictionary *before* you run PRINCE

After PRINCE reads in the input dictionary it stores each word, (element), in a table consisting of all the words of the same length. PRINCE then constructs chains consisting of 1 to N different elements. Right now it appears that N is equal to eight, (confirmed when using the --elem-cnt-min option). It does this by setting up structures of the different tables and then filling them out. For example with the input dictionary:

a

It will generate the guesses:

a
aa
aaa
aaaa
aaaaa
aaaaaa
aaaaaaa
aaaaaaaa

It generates the following guesses

a
aa
bba
aabb
bbabb
bbbbbb
abbbbbb
BigInput
BigInputbb
bbBigInputbb
bb
aaa
...(output cut to save space)

Next up, according to the 35 slide of the Passwords14 talk it appears that Prince should be sorting these chains according to keyspace. This way it can output guesses from the chains with the smallest keyspace first. This can be useful so it will do things like append values on the end of dictionary words before it tries a full exhaustive brute force of all eight character passwords. While this appears to happen to a certain extent, something else is going on as well. For example with the input dictionary:

a
b
cc

It would output the following results:

Finding #2: PRINCE is not guaranteed to output all chains in increasing keyspace order, though it appears to at least make an attempt to do so

Additional Options:

Performance:

PRINCE was written by Atom so of course it is fast. If you are using a CPU cracker it shouldn't have a significant impact on your cracking session even if you are attacking a fast hash. For comparison sake, I ran it along with JtR's incremental mode on my MacBook Pro.

Prince:
run laki$ ../../../Tools/princeprocessor-0.12/pp64.app  < ../../../dictionaries/passwords_top10k.txt | ./john --format=raw-sha1-linkedin -stdin one_hash.txt
Loaded 1 password hash (Raw SHA-1 LinkedIn [128/128 SSE2 intrinsics 8x])
guesses: 0  time: 0:00:02:00  c/s: 1895K  trying: asdperkins6666 - bobperkins

JtR Incremental Mode:

run laki$ ./john -incremental=All -stdout | ./john --format=raw-sha1-linkedin -stdin one_hash.txt 

Loaded 1 password hash (Raw SHA-1 LinkedIn [128/128 SSE2 intrinsics 8x])

guesses: 0  time: 0:00:00:14  c/s: 2647K  trying: rbigmmi - rbigm65

Using PRINCE with OCLHashcat:

Below is a sample screen shot of me using PRINCE as input for OCLHashcat on my cracking box, (it has a single HD7970 GPU). Ignore the --force option as I had just installed an updated video card driver and was too lazy to revert back to my old one that OCLHashcat supports. I was also too lazy to boot into Linux since I was using Excel for this post and my cracking box also is my main computer...

What I wanted to point out was that for a fast hash, (such as unsalted SHA1 in this case), since PRINCE is not integrated into OCLHashcat it can't push guesses fast enough to the GPU to take full advantage of the GPU's cracking potential. In this case, the GPU is only at around 50% utilization. That is a longer way of saying that while you still totally make use of OCLHashcat when using PRINCE, it may be adventurous to also run dictionary based rules on the guesses PRINCE generates. Since those dictionary rules are applied on the GPU itself you can make a lot more guesses per second to take full advantage of your cracking hardware. This is also something Atom recommends and he helpfully included two different rulesets with the PRINCE tool itself.

Side note: PRINCE plows though the LinkedIn list pretty effectively. To get the screenshot above I had to run the cracking session twice since otherwise the screen would have been filled with cracked passwords.

Big Picture Analysis:

The main question of course is how does this tool fit into a cracking session? Atom talked about how he saw PRINCE as a way to automate password cracking. The closest analogy would be John the Ripper's default behavior where it will start with Single Crack Mode, (lots of rules applied to a very targeted wordlist), move on to Wordlist mode, (basic dictionary attack), and then try Incremental mode, (smart bruteforce). Likewise with PRINCE depending on how you structure your input dictionary it can act as a standard dictionary attack, (appending/prepending digits to input words for example), combinator attack, (duh), and pure brute force attack, (trying all eight character combos). It can even do a limited passpharse attack though it gets into "Correct Horse Battery Staple" keyspace issues then. For example, with the input dictionary of:

Correct
Horse
Battery
Staple

It will generate all four word combinations such as:

HorseHorseCorrectBattery
HorseHorseBatteryBattery
HorseCorrectCorrectHorse
HorseBatteryCorrectHorse
HorseCorrectBatteryHorse
HorseBatteryBatteryHorse
CorrectCorrectHorseHorse
BatteryCorrectHorseHorse
CorrectBatteryHorseHorse
BatteryBatteryHorseHorse

Experiment 2) PRINCE and Dictionary Attacks targeting the MySpace list

Experiment Setup:
This is the same as the previous test targeting the MySpace dataset, but this time using dictionary attacks. For JtR, I stuck with the default ruleset and the more advanced "Single" ruleset. I also ran a test using Hashcat and the ruleset Atom included along with PRINCE, (prince_generated.rule). For all the dictionary attacks, I used the RockYou top 100k dictionary to keep them comparable to the PRINCE attack.

Cracking Length: I gave each session up to 1 billion guesses, but the two JtR attacks were so short that I only displayed the first 100 million guesses on the graph so they wouldn't blend in with the Y-axis. The hashcat attack used a little over 700 million guesses which I annotated its final results on the graph. Side note, (and this merits another blog post), but Hashcat performs its cracking sessions using word order, vs JtR's rule order. I suspect this is to make hashcat faster when cracking passwords using GPUs. You can read about the difference in those two modes in one of my very first blog posts back in the day. What this means is that Hashcat's cracking sessions tend to be much less front loaded unless you take the time to run multiple cracking sessions using smaller mangling rulesets.

Commands used:
laki$ ../../../Tools/princeprocessor-0.12/pp64.app < ../../../dictionaries/Rockyou_top_100k.txt | python checkpass2.py -t ../../../Passwords/myspace.txt -m 1000000000

laki$ ../../../John/john-1.7.9-jumbo-7/run/john -wordlist=../../../dictionaries/Rockyou_top_100k.txt -rules=wordlist -stdout | python checkpass2.py -t ../../../Passwords/myspace.txt -m 1000000000

laki$ ../../../John/john-1.7.9-jumbo-7/run/john -wordlist=../../../dictionaries/Rockyou_top_100k.txt -rules=single -stdout | python checkpass2.py -t ../../../Passwords/myspace.txt -m 1000000000

Experiment Results:

As you can see, all of the dictionary attacks performed drastically better than the PRINCE over the length of their cracking sessions. That's to be expected since their rulesets were crafted by hand while PRINCE generates its rules automatically on the fly. I'd also like to point out that once the normal dictionary attacks are done, PRINCE keeps on running. That's another way of saying that PRINCE still has a role to play in a password cracking session even if standard dictionary attacks initially outperform it. All this test points out is if you are going to be running a shorter cracking session you would be much better off running a normal dictionary based attack instead of PRINCE. This does lead to my next question and test though. After you run a normal dictionary attack, how does PRINCE do in comparison to a Markov brute force based attack?

Cracking Length: 10 billion guesses

Commands used:
laki$ ../../../John/john-1.8.0-jumbo-1/run/john -wordlist= ../../../dictionaries/Rockyou_full_ordered.txt -rules=single -stdout | python checkpass2.py -t ../../../Passwords/myspace.txt -u uncracked_myspace.txt

laki$ ../../../Tools/princeprocessor-0.13/pp64.app < ../../../dictionaries/Rockyou_full_ordered.txt | python checkpass2.py -t ../../../Passwords/uncracked_myspace.txt -m 10000000000 -c 23865

laki$ ../../../Tools/princeprocessor-0.13/pp64.app < ../../../dictionaries/Rockyou_top_100k.txt | python checkpass2.py -t ../../../Passwords/uncracked_myspace.txt -m 10000000000 -c 23865

laki$ ../../../John/john-1.8.0-jumbo-1/run/john -incremental=UTF8 -stdout | python checkpass2.py -t ../../../Passwords/uncracked_myspace.txt -m 10000000000 -c 23865

Experiment Results:

I'll guiltily admit before running this test I hadn't been that impressed with PRICE. That's because I had been running it with the top 100k RockYou dictionary. As you can see, with the smaller dictionary it performed horribly. When I ran the new test with the full RockYou dictionary though, PRINCE did significantly better than an Incremental brute force attack. Yes, cracking 1.5% more of the total set might not seem like much, but it will take Incremental mode a *long* time to catch up to that. Long story short though, PRINCE's effectiveness is extremly dependend on the input dictionary you use for it.

Like most surprising test results, this opens up more questions then it solves. For example, what exactly is going on with PRINCE to make it so much more effective with the new dictionary. My current hypothesis is that it is emulating a longer dictionary attack, but I need to run some more tests to figure out if that's the case or not. Regardless, these results show that PRINCE appears to be a very useful tool to have in your toolbox if you use the right input dictionary for it.

Current Open Questions:

Other References:

• PRINCE Tutorial by Atom
• Grab the latest version of PRINCE