Saturday, October 30, 2010

CCS Paper Part #2: Password Entropy

Round Peg, Square Hole
This is part #2 in a (mumble, cough, mumble) part serious of posts discussing the results published in the paper I co-authored on the effectiveness of passwords security metrics. Part #1 can be found here.

I received a lot of insightful comments on the paper since my last post, (one of the benefits of having a slow update schedule), and one thing that stands out is people really like the idea of password entropy. Here’s a good example:
“As to entropy, I think it would actually be a good measure of password complexity, but unfortunately there's no way to compute it directly. We would need a password database comparable in size (or preferably much larger than) the entire password space in order to be able to do that. Since we can't possibly have that (there are not that many passwords in the world), we can't compute the entropy - we can only try to estimate it in various ways (likely poor)”
First of all I want to thank everyone for their input and support as I really appreciate it. This is one of the few cases though where I’m going to have to disagree with most of you. In fact, as conceited as it sounds, my main takeaway has been that I've done a poor job of making my argument, (or I’m wrong which is always a possibility). So the end result is another post on the exciting topic of password entropy ;)

When I first started writing this post, I began with a long description on the history of Shannon Entropy, how it’s used, and what it measures. I then proceeded to delete what I had written since it was really long, boring, and quite honestly not that helpful. All you need to know is:
  1. Claude Shannon was a smart dude.
  2. No seriously, he was amazing; He literally wrote the first book on modern code-breaking techniques.
  3. Shannon entropy is a very powerful tool used to measure information entropy/ information leakage.
  4. Another way of describing Shannon entropy is that it attempts to quantify how much information is unknown about a random variable.
  5. It’s been effectively used for many different tasks; from proving one time pads secure, to estimating the limits of data compression.
  6. Despite the similar sounding names, information entropy and guessing entropy are not the same thing.
  7. Yes, I’m actually saying that knowing how random a variable is doesn’t tell you how likely it is for someone to guess it in N number of guesses, (with the exception of the boundary cases where the variable is always known – aka the coin is always heads- or when the variable has an even distribution – aka a perfectly fair coin flip).
Ok, I’ll add one more completely unnecessary side note about Shannon Entropy. Ask a crypto guy, (or gal), if the Shannon entropy of a message encrypted with a truly random and properly applied one time pad is equal to the size of the key. If they say “yes”, point and laugh at them. The entropy is equal to that of original message silly!

Hey, do you know how hard it is to make an entropy related joke? I’m trying here…

Anyways, to calculate the entropy of a variable you need to have a fairly accurate estimate of the underlying probabilities of each possible outcome. For example a trick coin may land heads 70% of the time, and tails the other 30%. The resulting Shannon entropy is just a summation of the probability of each event multiplied by the log2 of its probability, (and then multiplied by -1 to make it a positive value). Aka:

So the Shannon entropy of the above trick coin would be -(.7 x log2(.7) + .3 x log2(.3)) which is equal to 0.8812 bits. A completely fair coin flip’s entropy would be equal to 1.0. In addition, the total entropy of different independent variables is additive. This means the entropy of flipping the trick coin and then the fair coin would be .8812 + 1.0 = 1.8812 bits worth of entropy.

I probably should have put a disclaimer above to say that you can live a perfectly happy life without understanding how entropy is calculated.

The problem is that while the Shannon entropy of a system is determined using the probability of the different outcomes, the final entropy measurement does not tell you about the underlying probability distribution. People try to pretend it does though, which is where they get into trouble. Here is a picture, (and a gratuitous South Park reference), that I used in my CCS presentation to describe NIST’s approach to using Shannon entropy in the SP800-63 document:

Basically they take a Shannon entropy value, assume the underlying probability distribution is even, and go from there. Why this is an issue is that when it comes to human generated passwords, the underlying probability distribution is most assuredly not evenly distributed. People really like picking “password1”, but there is always that one joker out there that picks a password like “WN%)vA0pnwe**”. That’s what I’m trying to say when I show this graph:

The problem is not that the Shannon value is wrong. It’s that an even probability distribution is assumed. To put it another way, unless you can figure out a method to model the success of a realistic password cracking session using just a straight line, you’re in trouble.

Let me make this point in another way. A lot of people get hung up on the fact that calculating the underlying probability distribution of a password set is a hard problem. So I want to take a step back and show you this holds true even if that is not the case.

For an experiment, I went ahead and designed a variable that has 100 possible values that occur at various probabilities, (thanks Excel). This means I know exactly what the underlying probability distribution is. This also means I’m able to calculate the exact Shannon entropy as well. The below graph shows the expected guessing success rate against one such variable compared to the expected guessing success generated by assuming the underlying Shannon entropy had an even distribution.

Now tell me again what useful information the Shannon entropy value tells the defender about the resistance of this variable to a guessing attack? What’s worse is the graph below that shows 3 different probability distributions that have approximately the same entropy, (I didn’t feel like playing around with Excel for a couple of extra hours to generate the EXACT same entropy; This is a blog and not a research paper after-all).

These three variables have very different resistance to cracking attacks, even though their entropy values are essentially the same. If I want to get really fancy, I can even design the variables in such a way that the variable with a higher Shannon entropy value is actually MORE vulnerable to a shorter cracking session.

This all comes back to my original point that the Shannon entropy doesn’t provide “actionable” information to a defender when it comes to selecting a password policy. Even if you were able to perfectly calculate the Shannon entropy of a password set, the resulting value still wouldn’t tell you how secure you were against a password cracking session. What you really want to know as a defender is the underlying probably distribution of those passwords instead. That's something I've been working on, but I’ll leave my group’s attempts to calculate that for another post, (hint, most password cracking rule-sets attempt to model the underlying probability distribution because they want to crack passwords as quickly as possible).

Thursday, October 7, 2010

New Paper on Password Security Metrics

I'm in Chicago at the ACM CCS conference, and the paper I presented there: "Testing Metrics for Password Creation Policies by Attacking Large Sets of Revealed Passwords", is now available online.
Since I had the paper and presentation approved through my company's public release office I was given permission to blog about this subject while the larger issue of my blog is still going through the proper channels. Because of that I'm going to limit my next couple of posts to this subject rather than talking about the CCS conference as a whole, but let me quickly point you to the amazing paper "The Security of Modern Password Expiration: An Algorithmic Framework and Empirical Analysis", written by Yinqian Zhang, Fabian Monrose and Michael Reiter. In short, they managed to obtain a great dataset, their techniques were innovative and sound, and there's some really good analysis on how effective password expiration policies really are, (spoiler: forcing users to change their password every six months isn't very useful).

I'd like to first start by acknowledging the other authors who contributed to the "Testing Password Creation Metrics..." paper.
  • Dr. Sudhir Aggarwal - Florida State University: My major professor, who spent I don't know how many hours helping walk me through the subtle intricacies of information entropy.
  • Michael Collins - Redjack LLC: Another data driven researcher, and much cooler than me since he uses GNUPlot instead of Excel ;)
  • Henry Stern - Cisco IronPort: He was the driving force behind getting this paper written. It was over lunch at the Microsoft Digital Crime Consortium, (it's a conference to combat cybercrime, and not a group of people from Microsoft looking to commit digital crime like the name implies...), that the framework for this paper was laid out.
As for the contents of the paper, I'm planning on breaking the discussion about it down into several different posts, with this post here being more of an overview.

When writing this paper, we really had two main goals:
  1. How does the NIST model of password entropy as defined in SP800-63 hold up when exposed to real password datasets and realistic attacks?
  2. How much security is actually provided by typical password creation policies, (aka minimum length, character requirements, blacklists)?
Based on our results, we then looked at the direction we would like password creation policies move to in the future. This ended up with us suggesting how to turn our probabilistic password cracker around, and instead use it as part of a password creation strategy that allows people to create passwords however they like, as long as the probability of the resulting password remains low.

Of all that, I feel our analysis of the NIST password entropy model is actually the most important part of the paper. I know it sounds like an esoteric inside baseball subject, but the use of NIST's password entropy model has a widespread impact on all of us. This is because it provides the theoretical underpinning for most password creation policies out there. Don't take my word for how widespread the use of it is. Check out the Wikipedia article on password strength, (or better yet, read the discussion page) for yourself.

Our findings were that the NIST model of password entropy does not match up with real world password usage or password cracking attacks. If that wasn't controversial enough, we then made the even more substantial claim that the current use of Shannon Entropy to model the security provided by human generated passwords at best provides no actionable information to the defender. At worse, it leads to a defender having an overly optimistic view of the security provided by their password creation policies while at the same time resulting in overly burdensome requirements for the end users.

Getting in front of a room full of crypto experts and telling them that Shannon Entropy wasn't useful to evaluate the security of password creation policies and "We REALLY need to STOP using it", was a bit of a gut clenching moment. That's because the idea of information entropy is fairly central to the evaluation of most cryptographic algorithms. I would have never done it except for the fact that we have a lot of data backing this assertion up. The reason we are making the broader point is because it's tempting to dismiss the flaws in the NIST model by saying that NIST just estimated the entropy of human generated passwords wrong. For example, if you juggle the constants around or perhaps look at word entropy vs character entropy, things will work out. Our point though is not that you can't come up with a fairly accurate Shannon entropy model of human generated passwords. You most assuredly can. It's just that it's not apparent how such a model can provide "actionable information". In addition, the way we currently use Shannon Entropy in evaluating password security policies is fundamentally flawed.

This subject really does require another blog post, but before I head back to Boston I wanted to leave you with one of the graphs from our paper that demonstrates what I'm talking about:

The above graph shows cracking sessions run against passwords that met different minimum length password creation requirements, (aka must be at least seven characters long). The NIST estimated cracking speed is based on the calculated NIST entropy of passwords created under a seven character minimum password creation policy. You may notice that it overestimates the security of the creation policy over shorter cracking sessions, but at the same time doesn't model longer cracking sessions either. This is what I keep on saying that it doesn't provide "actionable intelligence", (third time and counting). When we say "password entropy" what we really want to know is the Guessing Entropy of a policy. Unfortunately, as a community, we keep using Shannon entropy instead. Guessing entropy and Shannon entropy are two very different concepts, but unfortunately there doesn't exist a very good way of calculating the guessing entropy, while calculating the Shannon entropy of a set of text is well documented. This is part of the reason why people keep trying to use Shannon entropy instead.

So I guess I should end this post by saying, if any of this sounds interesting please read the paper ;)

Friday, September 10, 2010

Quick Status Update

This is just a quick post to let you know that I for once have a valid excuse for not updating this blog in a timely manner. I actually found a job! Thanks to everyone who offered help, recommendations and encouragements. The only catch is that right now it's being decided if I have to run my posts through our public release office or not. Don't worry, this blog is not going away regardless of the decision.. It might just gain a few unwilling readers ;)

As to my new company, I'm going to keep that a bit of an open secret. This blog reflects my personal views. I certainly don't speak for them, and I plan on avoiding any topics that have to do with my day job, (Don't worry, I'm not doing any password cracking there).

Once again thanks, and I'll resume posting once I get the OK and can update this blog while complying with company policies. I just want to make sure I handle this situation the right way.

Wednesday, July 28, 2010

Defcon Crack Me if You Can Competition

I'd be remiss if I didn't spend a little time talking about the "Crack Me if you Can" competition at Defcon. It's really been amazing the amount of interest that this contest is drumming up. People are excited; it seems like everyone is refining their mangling rules, putting together new wordlists, and finishing up various password cracking tools. The impact that this is having on the password cracking community as a whole is hard to overstate. Needless to say, I'm a fan of that, and I have a ton of respect for Minga and the folks at KoreLogic for putting this together.

I'll be participating, though I certainly don't plan on winning. What I'm really looking forward to though is the chance to meet with everyone else and learn what other people are doing. I'm hoping this turns into an event like the lockpicking village with the contest being almost besides the point. Of course I might be saying that because I'm going to get creamed as well...

Since I've had a few people ask me about the competition itself, here's my two cents. My biggest concern is that the passwords we will be cracking aren't real. This isn't a criticism. There's no way you could run this competition with real corporate passwords, (well, legally that is...). It's just something to keep in mind. What will be interesting though is applying the techniques learned from the winner, (part of the rules are that you have to disclose your cracking techniques), to other datasets as they become available. That's why I have this blog. I might not be the best password cracker out there, but I can certainly run other people's attacks and plot the results on Excel ;)

If I had to hazard a guess, here's some predictions of mine about the contest:

1) Most passwords will be based on relatively common dictionary words. Way more so than you would find normally.

2) Most of the cracking will center around applying the correct mangling rules. Yes there will be the 'Dictionary123' words, but I expect most 'high score' passwords will have less common rules such as 'xD1ct1onaryx'.

3)There will probably be some LANMAN passwords, so bring your rainbow tables.

4) I expect there to be so many NTLM passwords that rainbow tables for them won't be cost effective.

5) I'll be interested to see if they have any 'exotic' password hashes. WinRAR, TrueCrypt, etc.

6) It'll be a ton of fun ;)

I'll see you guys there.

Saturday, July 3, 2010

Protecting Physical Documents

The above picture is of my Subaru Baja. Sometime last night, someone broke my back window, and stole almost everything from my car, (they apparently did not like my music; btw Punk is not dead). Normally this would be a costly annoyance, but in this case I'm in the process of moving and finding a new job. While most of my stuff is sitting safely in a storage locker, I still had several boxes of various items stored in my back seat, including unfortunately my "to-go" bag.

My to-go bag contained all of my important possessions that I planned on grabbing if my house was in the process of burning down. For example, it contained two thumb drives with backups of all of my work plus assorted other documents. I'm not worried about them though since I used TrueCrypt. Good luck cracking those, which BTW, is the reason I love TrueCrypt. What I am concerned about though is that my social security card, my passport, my birth certificate, my extra banking checks, and a whole lot of other important paper documents were also taken. Visualize everything you don't want to be stolen and you have a pretty good idea of what was in that bag.

Of course the next question is "Why the Hell did you leave such important documents in your car?" My response is, laziness and a poor threat model. I needed several of those documents when getting a job. A drivers license by itself doesn't cut it, as you need at least two other forms of ID. Rather than grab those two documents out of my bag and leave the rest in the storage locker I grabbed the whole thing in case there was anything else I needed. Normally I also wouldn't leave it in my truck if I was planning on going somewhere sketchy, but I was parking in a well lit hotel parking lot, and quite simply I had my hands full hauling my bag of clothes, my suit and my computer bag into my room. Darned if I wanted to make a second trip back down to my car. What's worse though is I rationalized it away by saying that at least my car was locked, unlike my hotel room where any of the housekeeping staff could access it during my stay; Plus my car's never been broken into before... The reason that's worse is because I didn't simply forget my bag; I realized it might be a problem and then actively convinced myself that, "No, everything really is ok".

I have to confess that this is a difficult post for me to write. I was counseled by several friends never to mention this incident to anyone, (besides the cops and other officials of course). Their comments were along the lines of "You're looking for a security job, and you just had your life stolen because you left it in the back of a car. Even I wouldn't hire you now!" Of course that was said in jest, but the concern is real.

I'm willing to take that risk though for several reasons. First of all I'm really angry, both at the person who did this and myself. Actually mostly at myself which is seriously messed up. By talking about this incident hopefully I can gain a bit more control over the situation. Second, I'm a big fan of disclosure. If the above description doesn't sound like a typical computer attack, let me rewrite it for you:
The attacker performed an SQL injection attack against After gaining access to the database they downloaded the user's social security number, banking information, and other personally identifiable information. Afterwards the attacker performed a 'drop table users' destroying the local copy of the data. When the site administrator was asked about this, he responded that knew SQL injection attacks were common, but he never expected to be targeted by one. As for the reason why the user data was accessible, the administrator admitted the site was in the process of transitioning to a new forum software, and that if the attack happened a week later when the new forum software was in place, this wouldn't have been a problem.
I've always felt that security incidents can happen to anyone, and what's important is to focus on the remediation, and use them as a learning tool to make sure the same attack doesn't happen again. That's one nice thing about having a blog, I'm on record on saying much the same thing when talking about the ZF0 attack, so at least this isn't a new found belief I came to after finding myself completely 0wned ;)

So in the spirit of full disclosure I wanted to talk about this attack in a public forum where hopefully it will benefit other people, and if someone doesn't want to hire me because I'm not perfect, well at least they found out now. So on to a more detailed analysis of the attack:
  1. Only items in the back seat were stolen. Since there was so much stuff it looks like the attacker grabbed what they could, and then left without doing a full search of the car. I just was really unlucky that everything I cared about was in the back seat.
  2. When this happened, my car was parked at the far end of the parking lot, since I'd rather walk than squeeze into a small spot. This was a serious mistake considering all of the stuff I had in my car.
  3. While there was a night watchman, he did not notice the attack. Likewise there were no sensors collecting forensically useful data, (aka cameras, and the thief did not leave any useable fingerprints).
  4. I'm much too focused on digital issues. The fact that I bothered to encrypt my electronic documents and the store them with paper documents that are way more valuable to an attacker shows a serious lack of priorities and/or threat modeling. I'm not saying don't encrypt your files, but simply that I should have taken the same care with my other documents and locked them up in my hotel room safe.
  5. The mindset, "Just because something bad hasn't happened to me in the past means it won't happen to me in the future" is very hard to avoid.
  6. I really hope all that is happening right now is some 16 year old kid is trying to use my passport to buy booze. That being said, I need to plan for much more serious scenarios, hence closing my old bank account rather than just canceling my checks, signing up to a credit check service, etc.
  7. Dealing with issues like this on a holiday weekend is extremely difficult. Canceling my old bank account and moving it to a new one was particularly stressful since I had a couple of hours to do it before the bank closed for the long weekend. Likewise, I will have to wait till Tuesday to have my car window replaced. Of course, cyberattacks never happen during a holiday...
  8. Security policies are important, but what's more important is enforcement of those policies. You really do need some force from on high telling people, "Yes, it is a pain to take a second trip down to your car, but you are going to do it anyway".
  9. Storing all of your valuables in one place has advantages and disadvantages. I still don't know if the idea of a to-go bag was a fundamentally bad idea, but I certainly should have "checked out" my two forms of id from it rather than taking the whole thing.
  10. What makes a fiasco is a cascade of multiple smaller mistakes/failures occurring together. Whether we are talking about the BP oil spill, or a horribly hilarious Peter Pan play, serious problems often are not the result of just one thing going wrong, but several poor decisions.
  11. Combined with my comments in the previous paragraphs, it's really easy to analyze all of the stuff that I did wrong after the fact. The problem is it all of my decisions seemed so reasonable at the time. On the other hand, you can't live a perfectly secure life. What I'm wrestling with right now is how to re-evaluate my personal threat models and learn from this incident without letting it ruin my life.
Well, that about does it for now. Hopefully I can get back to the focus of this blog, the academic study of password cracking techniques, soon. This whole real life security thing can be pretty annoying...

Monday, May 31, 2010 - General Observations and Updates - Part 3

Digging into this data is like watching an episode of Lost. Whenever it seems like one question gets answered, about ten other questions pop up.

Before I get into details, I want to start with a comment Per Thorsheim sent me as to what other password cracking programs support salted sha1 hashes:

The sha1(lowercase_username.password_guess) is at least supported by these:

Extreme GPU Bruteforcer ( hashcat and oclhashcat (cpu/gpu respectively)

I'm kicking myself for not thinking about hashcat, since it's a extremely powerful password cracker; plus it's free. Unfortunately the GPU version doesn't support the salted sha1 hash type, but even the non-gpu version is quite nice.

As for InsidePro, it also is very good, though it does cost some money. I've had a license-free version of questionable origin offered to me before, but I turned that down. Legality aside, installing pirated software given to you by shady people at a hacker conference would just be stupid...

Also, I've been talking to Ron Bowes over at the excellent skullsecurity blog, and he and some other people are hard at work cracking the passwords. It sounds like they have some serious hardware behind the effort, so expect to see something posted on his site about that in the near future.

OK, now onto the analysis:

First of all, I'm downgrading my opinion of the skill showed by the hackers in their password cracking attack. What I didn't realize before was the extent that the users of had been compromised previously. Just about all of the non-trivial passwords that were cracked appear in publicly available input dictionaries which are based on passwords cracked from user submitted hashes - aka hashkiller, insidepro, etc. Please note, I'm not saying they were script kiddies. The attackers were able to target the salted sha1 hash, and they knew where to get some good input dictionaries. It's just that they are not some uber-l33t password crackers, and anyone else using those input dictionaries could crack the same number of passwords in a couple of hours.

What this also means is we might be able to figure out which input dictionaries the attackers were using by looking at the hashes they cracked, vs. what the input dictionary would crack. To demonstrate this, below is a Venn diagram of what an input dictionary the attackers used would look like:

For example, there is a good chance the attackers used the InsidePro Big dictionary, since it cracks the same 598 passwords from the list that the attackers cracked. Add in a couple of the other publicly available input dictionaries, and you get real close to the 920 total passwords they managed to crack. To put this in perspective, I've so far managed to crack 62%, (compared to the 53% that the hackers cracked), of the salted Sha1 passwords on my laptop using basic dictionaries with almost no mangling rules. I fully expect other people to blow past that mark.

Next up, initial thoughts about the database:

What I really need to do is load all of the tables into my own database so I can do quick SQL queries, vs. manually going through the data by hand. That being said there are a few things that stick out:
  1. There's a lot of userids/password hashes in the carders_smf_members table that did not appear in the write-up.
  2. The MD5 hash is salted, but at least the salt is also available in the carders_smf_members table. The hash itself is a vBulletin3 hash type, MD5(MD5(Password).Salt). Both John the Ripper and Hashcat support this hash type.
  3. I'm not sure if the IP addresses stored in the table are accurate or not, since it looks like the site admins tried to obscure it in the webserver logs, but if they are, the database stores the last two ip addresses used.
  4. Other interesting fields include date joined, number of posts, karma level, last login date, etc.
  5. The above doesn't even begin to get into all of the data contained in the actual posts themselves...
That about it for this update. Let me leave you with one last fact that Ron found out:

Another interesting factoid:

Last MD5 password: 2010-01-10 21:54:01

First SHA1 password: 2010-01-10 22:40:16

So it was on January 10, 2010, later in the evening (in CDT) that they upgraded from vBulletin 3 to SMF.

*shrug* the more you know!

Wednesday, May 26, 2010 - Analysis of Password Cracking Techniques - Part 2

So I figure I probably should get around to looking at the passwords in this list, since password cracking techniques are the focus of this blog...

First though, a real quick definition. I needed to decide what to call the various parties involved in this whole shenanigans. For example, when I'm talking about the 'hackers', am I referring to the people collecting stolen credit card data who belonged to the board, or the people who hacked Likewise, if I use the term criminals, that could refer to both groups as well. Therefore, in my blog posts I'm going to use the following terms to refer to the two groups:
  • Carders/Users: The people who belonged to the board. Normally I would also use the term 'victims', but I don't want to honor them with that title.
  • Hackers/Attackers: The people who broke into the forum and posted the data online.
Ok, now that we have that out of the way, the rest of this post is going to be broken up into four parts:
  1. Executive Summary
  2. Brief Description of the Password List
  3. General Observations
  4. Setting Up a Password Cracker to Target Salted Sha1 Hashes
More in-depth analysis will have to wait till later since I need more time to go through the data. Also, I'm moving up to the D.C. area in a week to look for a job/start interviewing, so large scale password cracking sessions are not really feasible for me to perform right now.

-BTW, if you have a job opening please feel free to contact me.

Finally, I apologize but this post is going to be a bit more haphazard then the above outline implies, simply because it's hard to talk about one aspect of this dataset without discussing other aspects as well.

Executive Summary:, on online forum for the buying and selling of stolen bank and credit card data, was broken into sometime before May 5th by vigilante hackers. On May 18th, the hackers then posted data about the users to various file sharing sites, (pastebin, rapidshare), which included IP addresses, usernames, e-mail addresses, and all of the saved forum posts. More significant to this report, the attackers also posted the users' password hashes along with the plaintext passwords that the attackers had managed to crack. These passwords and password hashes provide significant data for researchers. From this data, we now know that cyber-criminals often select weak passwords. Furthermore, by analyzing these passwords we can develop better password cracking techniques that target the specific type of users, (criminals), whom law enforcement is the most interested in. This is an improvement from some of the other disclosed password datasets which were gathered from the general public. In addition, by looking at the passwords the attackers managed to crack, along with the passwords they failed to crack, we can gain a better understanding of the capabilities and techniques employed by hacker groups in real life attacks. This hacker group in particular appears to be quite proficient in password cracking techniques, possessing both the tools to attack salted SHA1 password hashes, and the ability to crack close to 53% of the passwords in the dataset.

Brief Description of the Password List(s):

The first thing that stands out is there are actually two lists of passwords present in the attacker's write-up, though all of the password hashes are mixed together.

List 1)
Contains 1737 salted Sha1 password hashes using the following format:


This is the list that the attackers talked about and the list they attempted to crack passwords from. Of all the salted Sha1 password hashes, the attackers cracked 920 of them, (around 53%), during the course of an approximately two week period.

List 2)
Contains 2036 "one hundred and twenty eight bit" password hashes of unknown origin. Due to their length, the base password hash is most likely MD4 or MD5. It is unknown if the attackers knew of the existence of these separate hashes since they did not mention the second hashing function in their writeup or crack any of the passwords from this list. I've run some quick tests and these hashes do not appear to be a single round of MD4 or MD5, but probably instead are salted in some manner, and/or use multiple rounds of MD4/5. If a salt that was not the username was used, then it probably will be infeasible to crack very many of the passwords due to the salt not being included in the writeup. A few of the passwords might still be vulnerable though since the salt may be short enough to be brute-forced along with the guesses.
So the next question is, why were there two separate types of password hashes? The most likely explanation is that at some point in the past, the site administrators switched over to a new forum software, or upgraded their security settings. The old users were left with the original password hash, and users who joined up afterwards were assigned the newer password hash. The exact same thing happened when was hacked, leaving users who hadn't logged into the site recently, exposed with only a simple MD5 hash protecting their password vs. the tougher phpbb3 hash.

The interesting thing is that in this case, a user's hash probably was not changed to the new format when they logged into the site. This can be seen by comparing the user's hash type to the IP addresses I talked about earlier. Of the 960 username/IP address combinations, 47 of the users had their password stored as the unknown hash, and 726 had their passwords stored as a salted Sha1 hash. You might notice that these two numbers did not add up to 960. There were 187 usernames that appeared in the IP logs but did not appear in either list of password hashes. I need to look into this more, but many of them might represent incorrect login attempts. A second option is the attackers may not have released all of the user password hashes, instead choosing to keep several for themselves to aid in future hacking attacks. I should probably analyze the forum posts to confirm if the hash type correlates to when the users became active, and if any of the users with missing password hashes actually posted previously on the forum.

Why yes, this is my "brief" description...

General Observations:

Observation 1) The attackers are very proficient in password cracking techniques
Level of Confidence: High
Discussion: The fact that the attacker could even target the salted Sha1 password hashes speaks to their competence. Most major password crackers, (Cain&Able, John the Ripper, L0phtcrack, etc), do not natively support that salt/hash combination. While John the Ripper has extensive support for a generic MD5 hashing function, where it is very easy to modify it to support all sorts of weird hash/salt combinations, that functionality has not yet been ported over to support Sha1 as well. So either they developed their own password cracker/workaround, (which isn't as hard as you might think, as I'll discuss later), or they had access to a less well known password cracker that supported that hash.

Question: Does anyone know of any password cracking program that does support Sha(username.password)?

In addition, since the passwords were salted with a unique salt per hash, this negates many common attacks like rainbow tables. Also, I don't know of any online password cracking site that supports this hash format, since they can't use hash lookup tables either. This means the attackers had to crack all of the password hashes themselves instead of relying on community resources.

Next, the fact they cracked 53% of the password hashes in a two week period speaks highly of the attacker's skills. To put this in perspective, in the attack, the attacker had submitted unsalted MD5 password hashes to an online cracker and only managed to crack 24% of the passwords. In that case, while the hacker might have been very skilled in webpage attacks, they were pretty much at the script kiddie level when it came to cracking the passwords. While 53% might not sound that much more impressive, there is the fact that in the case, the hashes were salted with a unique salt. The 'unique' part is important since it means an attacker has to hash each guess independently against each target hash they are trying to crack.

For example, imagine a typical cracking session taking one hour to complete. If an attacker was attempting to crack three thousand unsalted password hashes, then it would take them one hour to run all of their guesses against the entire list. On the other hand, if those password hashes used a unique salt, the same attack would then take three thousand hours, or roughly four months. That's a big difference. Assuming the attackers spent two weeks attempting to crack all 1737 salted Sha1 password hashes, that would mean the guesses they could make in their attack would be equivalent to a 11 minute cracking session against unsalted Sha1 passwords. Even assuming they cracked 50% of the password almost immediately, their cracking session would still be only equivalent to around 20 minutes of attacking unsalted hashes. In conclusion: cracking 53% of close to two thousand salted password hashes in a two week period is fairly impressive.

Observation 2) The Attackers did not user John the Ripper's Incremental mode to brute force guesses.
Level of Confidence: High
Discussion: When I ran JtR's incremental mode, using the default charset 'All', against the list, I almost immediately cracked a new password. Over a longer running session, I managed to crack even more.
Observation 3) The Attackers brute forced digits
Level of Confidence: Low/Medium Very Low
Edit March 28th 2001: I've since managed to crack the password '37721010'. It appears that most of the longer digit based passwords that the attackers had cracked were publicly available in lists of previously cracked password. I'm leaving my original discussion post up for posterity.
Discussion: I have not yet been able to crack any all-digit passwords that the attackers did not crack. Likewise, the attackers cracked several passwords such as '19930720', where unless the attacker has outside information, or had run across that password earlier, it is unlikely to be cracked by any form of attack besides brute force.
Observation 4) The Attackers did not brute force alpha passwords longer than five characters long
Level of Confidence: High
Discussion: I have since managed to crack several passwords that were four lowercase characters long followed by two digits. Likewise I managed to crack several passwords that were six characters long all lowercase.
Observation 5) The attackers may have used knowledge from previous attacks to aid their password cracking sessions
Level of Confidence: Medium
Discussion: Let's be honest, this isn't their first time at the rodeo. It's doubtful that is the first blackhat site these attackers have compromised. As we've seen from the ZF0 attacks, just because a website supports a tech-savy audience, doesn't mean it stores their passwords securely. And as we all know, people re-use passwords ... a lot. It's quite possible that the attackers used passwords cracked from previous sites to in turn crack some of the passwords in the set. For example, one of the cracked passwords was 'Nadia2312'. An unsalted MD5 hash of that same password was cracked via the Hashkiller site's project Opencrack, on February 1st, 2010. While it could be a coincidence, I'd also like to point out that Hashkiller is a German based website. Other plaintext passwords in the list such as '123456r12', and '01724776692' also show up as being cracked by HashKiller and InsidePro in 2009. Note, this may just imply that the attackers used custom input dictionaries from these sites, and did not actually submit the original password hashes to them.

Setting Up a Password Cracker to Target Salted Sha1 Hashes

As I mentioned previously: I'm sure there's already a password cracking program out there that can target salted Sha1 hashes, but I don't know of it. Not to be deterred, I quickly modified an old password cracking program I had written a while ago to support the hash, (note I could have modified John the Ripper instead, but I'll get to that in a bit). The hardest part was I didn't realize that when you added the username as the salt, you had to lowercase the entire thing. That's a couple of hours of my life I'd like back...

So testing this on my Mac laptop I expected to be able to make around 1000 guesses a second against the entire set of 1737 password hashes. I based this guess on benchmarking unsalted Sha1 hashes using John the Ripper, which gave me around 7,000,000 guesses a second. This would give me a maximum of 4029 guesses a second against the salted hashes. Throw in the overhead required to apply the salt plus my general programing sloppiness, and 1000 guesses a second sounded like a good number. So you can imagine my surprise when i was only ended up making around 80 guesses a second. Something's not right here...

Well it turned out, the problem was I had used the OpenSSL implementation of the Sha1 hashing algorithm which is really slow compared to the version in John the Ripper. Rather than trying to jury rig it into my old password cracker, I then decided to work with John the Ripper instead, which is what I should have done in the first place.

Since I'm a bit lazy goal oriented, I didn't bother to modify John the Ripper's source code though. I was able to get away with this since the hashing algorithm only uses one round of Sha1. Let's look at the hashing algorithm again:
The simplest way to do this would be to create a rule in JtR that would insert the username first. Such a rule would look like:
Which would insert Alice in front of all of your password guesses. Since we have close to two thousand usernames we are talking about though, that could get annoying having to create a rule for each one. This calls for the use of one of my favorite programs, Awk.

Awk, (and its sister Sed), is one of the best programs out there for quickly parsing and modifying files. It's also very useful for creating word mangling rules on the fly for use in a password cracking session in conjunction with JtR's stdin option. In that way, I sometimes use it much like MiddleChild, and in retrospect, I could easily have written MiddleChild in awk instead. For example I can create guesses using John the Ripper, pipe them into an awk script, and then pipe them back into John the Ripper where the guess will actually be hashed and compared against the target hashes. The above JtR rule could then be replaced with:
./john -wordlist=passwords.lst -session=cracking1 -rules -stdout | awk '{print "alice" $0}' | ./john -stdin -format=raw-sha1
The advantage of this approach is that 'alice' will be automatically inserted in front of all the guesses/rules that the first instance of John the Ripper is generating. But wait, don't we still have close to two thousand usernames we have to do this for? That would be one nasty command line. Luckily an Awk script can be run from a file with the -f option. And how do we create that file with all 1737 usernames? Well with Awk of course! The original format that the hashes were saved to in the writeup was:
Simply cat that file into Awk using the following command will create your Awk script for you.
cat carders_hashes.txt | awk -F : '{print"{print \"" tolower($1) "\"$0}"}' > awk_add_usernames.txt
The -F tells it to use the ':' as a seperator and the tolower() option lowercases the usernames. To run this whole thing just type:
./john -wordlist=passwords.lst -session=cracking1 -rules -stdout | awk -f awk_add_usernames.txt | ./john -stdin -format=raw-sha1
Using this setup, now I'm getting around 2,300 guesses a second which is much more respectable. There certainly is room for improvement though. First of all, John compares each hashed guess against all of the target hashes, instead of only the hash which the salt belongs too. Also, you have to clean up the cracked hashes file afterwards to remove the username from the plaintext password. But did I mention it works? As Miles said in the Lost finale, "I don't believe in a lot of things, but I do believe in duct tape!"

Well, that about does it for now. Next up, analysis of the cracked passwords.