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GPU Password Cracking GPU Password Cracking – Building a Better Methodology written by Karl Fosaaen | March 15, 2014 In an attempt to speed up our password cracking process, we have run a number of tests to better match our guesses with the passwords that are being used by our clients. This is by no means a definitive cracking methodology, as it will probably change next month, but here’s a look at what worked for us on a recent cracking test. For a little background, these hashes were pulled from a domain controller in the last six months. The DC still had some hashes stored in the older LanManager (LM) format in addition to NTLM. The password cracking process is also helped by using any cleartext passwords, recovered during the penetration test, as a dictionary. For this sample, there were: 1000 total hashes (159 LM/NTLM, 841 NTLM-Only) 828 unique hashes 172 accounts with duplicate* passwords (*shared with one or more accounts) Since LM hashes are weaker, we cracked those first. Initial attacks cracked all of the LM/NTLM hashes, giving us a nice head start (130/828 unique hashes or 15.7% cracked) and a good list to feed back into our other attacks. The General Methodology: 1. Use the dictionary and rules (Three minutes*) – Remaining Unique Hashes 698 Our dictionary file will typically catch the simple passwords. Our dictionary includes previously cracked passwords and most dictionary-word-based passwords will be in here. Add in a couple of simple rules (d3ad0ne, passwordspro, etc.) and this will catch a few of the “smarter” users with passwords like “1qaz2wsx%“. As for the starting rules, we’re currently using a mix of the default oclHashcat rules and some of the rules from KoreLogic’s 2010 rule list – http://contest-2010.korelogic.com/rules-hashcat.html For our sample set of data, the dictionary attack (with a couple of rules) caught 372 of the remaining 698 hashes (53%). 2. Start with the masking attacks (Fifteen minutes*) – Remaining Unique Hashes 326 Using mask attacks allows us to match common password patterns. Based on the passwords that we’ve cracked in the past, we identified the fifty most common password patterns (that our clients use). Here’s a handy perl script for identifying those patterns – http://pastebin.com/Sybzwf3K. Due to the excessive time that some of these masks take, we’ve trimmed our list down to forty-three masks. The masks are based on the types of characters being used for the password They follow the following format: ?u?l?l?l?l?d?d?d This is equivalent to (1 Uppercase Character) (4 Lowercase Characters) (3 Decimals). Or a more practical example would be “Netsp199” For more information on masking attacks with oclHashcat – http://hashcat.net/wiki/doku.php?id=mask_attack Our top forty-three masks take about fifteen minutes to run through and caught (29/326) 8% of the remaining uncracked hashes from this sample. 3. Go back to the dictionary, this time with more ammunition (10 minutes*) – Remaining Unique Hashes 297 After we’ve cracked some of the passwords, we will want to funnel those results back into our mangling attacks. It’s not hard for us to guess !123Acme123!“ ” after we’ve already cracked “Acme123“. Just use the cracked passwords as your dictionary and repeat your rule-based attacks. This is also a good point to combine rules. oclHashcat allows you to combine rule sets to do a multi-vector mangle on your dictionary words. I’ve had pretty good luck with combining the best64 and the d3ad0neV3 rules, but your mileage may vary. Using this technique, we were able to crack four of the remaining 297 (1.3%) uncracked hashes. 4. Double your dictionary, double your fun? (20-35 minutes) At this point, we’re hitting our limits and need to start getting creative. Along with our hefty primary dictionary, we have a number of shorter dictionaries that are small enough that we can combine them to catch repeaters (e.g. P@ssword“ P@ssword“). The attack is pretty simple: a word from the first dictionary will be appended with a word from the second. This style of attack is known as the combinator attack and is run with the -a 1 mode of oclHashcat. Additional rules can be applied to each dictionary to catch some common word delineation patterns (“Super-Secret!“). The example here would append a dash to the first word and an exclamation mark to the second. To be honest, this did not work for this sample set. Normally, we catch a few with this and it can be really handy in some scenarios, but that was not the case here. At this point, we will (typically) be about an hour into our cracking process. From the uniqued sample set, we were able to crack 530 of the 828 hashes (64%) within one hour. From the complete set (including duplicates), we were able to crack 701 of the 1,000 total hashes (70.1%). 5. When all else fails, brute force Take a look at the company password policy. Seven characters is the minimum? That will take us about forty minutes to go through. Eight characters? A little over two and a half days. What does that mean for our cracking? It means we can easily go after any remaining seven character passwords (where applicable). For those with eight character minimums (or higher), it doesn’t hurt for us to run a brute-force overnight on the hashes. Anything we get out of the brute-force can always be pulled back in to the wordlist for the rule-based attacks. Given that a fair amount of our cracking happens during a well-defined project timeframe, we’ve found that it’s best for us to limit the cracking efforts to about twenty-four hours. This prevents us from wasting too much time on a single hash and it frees up our cracking rig for our other projects. If we really need to crack a hash, we’ll extend this time limit out, but a day is usually enough to catch most of the hashes we’re trying to crack. With the overall efforts that we put in here (~24 hours), we ended up cracking 757 of the 1,000 hashes (75.7%). As luck would have it, I wrote up all of the stats for this blog and immediately proceeded to crack two new sets of NTLM hashes. One was close to 800 hashes (90% cracked) and another had over 5000 hashes (84% cracked). So your results will vary based on the hashes you’re trying to crack. *All times are based on our current setup (Four 7950 cards running OclHashcat 1.01) One final item to note. This is not the definitive password cracking methodology. This will probably change for us in the next year, month, week… People are always changing up how they create passwords and that will continue to influence the way we attack their hashes. I’ll try to remember to come back to this next year and update with any changes. Did we miss any key cracking strategies? Let me know in the comments..
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