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How We Learned to Cheat at Online Poker: A Study in Software Security

  • June 7, 2001
  • By Brad Arkin, Frank Hill, Scott Marks, Matt Schmid, Thomas John Walls
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Figure 4: Borland's implementation of Random()
long long RandSeed = #### ;

unsigned long Random(long max)
 long long x ;
 double i ;
 unsigned long final ;
 x = 0xffffffff;
 x += 1 ;

 RandSeed *= ((long long)134775813);
 RandSeed += 1 ;
 RandSeed = RandSeed % x ;
 i = ((double)RandSeed) / (double)0xffffffff ;
 final = (long) (max * i) ;

 return (unsigned long)final;

Based on historical precedent, seeds for number generators are usually produced based on the system clock. The idea is to use some aspect of system time as the seed. This implies if you can figure out what time a generator is seeded, you will know every value produced by the generator (including what order numbers will appear in). The upshot of all this is that there is nothing unpredictable about pseudo-random numbers. Needless to say, this fact has a profound impact on shuffling algorithms!

On To Poker, Or How To Use A Random Number Generator Badly

The shuffling algorithm used in the ASF software always starts with an ordered deck of cards, and then generates a sequence of random numbers used to re-order the deck. Recall that in a real deck of cards, there are 52! (approximately 2^226) possible unique shuffles. Also recall that the seed for a 32-bit random number generator must be a 32-bit number, meaning that there are just over 4 billion possible seeds. Since the deck is reinitialized and the generator re-seeded before each shuffle, only 4 billion possible shuffles can result from this algorithm. Four billion possible shuffles is alarmingly less than 52!.

To make matters worse, the algorithm of Figure 1 chooses the seed for the random number generator using the Pascal function Randomize(). This particular Randomize() function chooses a seed based on the number of milliseconds since midnight. There are a mere 86,400,000 milliseconds in a day. Since this number was being used as the seed for the random number generator, the number of possible decks now reduces to 86,400,000. Eight-six million is alarmingly less than four billion. But that's not all. It gets worse.

Breaking the System

The system clock seed gave us an idea that reduced the number of possible shuffles even further. By synchronizing our program with the system clock on the server generating the pseudo-random number, we are able to reduce the number of possible combinations down to a number on the order of 200,000 possibilities. After that move, the system is ours, since searching through this tiny set of shuffles is trivial and can be done on a PC in real time.

The RST exploit itself requires five cards from the deck to be known. Based on the five known cards, our program searches through the few hundred thousand possible shuffles and deduces which one is a perfect match. In the case of Texas Hold'em poker, this means our program takes as input the two cards that the cheating player is dealt, plus the first three community cards that are dealt face up (the flop). These five cards are known after the first of four rounds of betting and are enough for us to determine (in real time, during play) the exact shuffle. Figure 5 shows the GUI we slapped on our exploit. The "Site Parameters" box in the upper left is used to synchronize the clocks. The "Game Parameters" box in the upper right is used to enter the five cards and initiate the search. Figure 5 is a screen shot taken after all cards have been determined by our program. We know who holds what cards, what the rest of the flop looks, and who is going to win in advance.

Figure 5: The GUI for our exploit

Once it knows the five cards, our program generates shuffles until it discovers the shuffle that contains the five cards in the proper order. Since the Randomize() function is based on the server's system time, it is not very difficult to guess a starting seed with a reasonable degree of accuracy. (The closer you get, the fewer possible shuffles you have to look through.) Here's the kicker though; after finding a correct seed once, it is possible to synchronize our exploit program with the server to within a few seconds. This post facto synchronization allows our program to determine the seed being used by the random number generator, and to identify the shuffle being used during all future games in less than one second!

Technical detail aside, our exploit garnered spectacular press coverage. The coverage emphasizes the human side of our discovery. See our Web site for our original press release, the CNN video clip, and a New York Times story .

Doing Things Properly, or How to Shuffle Virtual Cards

As we have shown, shuffling virtual cards isn't as easy as it may appear at first blush. The best way to go about creating a shuffling algorithm is to develop a technique that can securely produce a well-shuffled deck of cards by relying on sound mathematics. Furthermore, we believe that publishing a good algorithm and opening it up to real-world scrutiny is a good idea (which meshes nicely with the opinions of the Open Source zealots). The main thing here is not relying on security by obscurity. Publishing a bad algorithm (like AFS did) is a bad idea, but so is not publishing a bad algorithm!

Cryptography relies on solid mathematics, not obscurity, to develop strong algorithms used to protect individual, government, and commercial secrets. We think shuffling is similar. We can stretch the analogy to include a parallel between cryptographic key length (which is directly proportional to the strength of many cryptographic algorithms) and the size of the random seed that is used to produce a shuffled deck of cards.

Developing a card-shuffling algorithm is a fairly straightforward task. The first thing to realize is that an algorithm capable of producing each of the 52! shuffles is not really required. The reasoning underlying this claim is that only an infinitesimally small percent of the 52! shuffles will ever be used during play. It is important, however, that the shuffles the algorithm produces maintain an even distribution of cards. A good distribution ensures that each position in the shuffle has an approximately equal chance of holding any one particular card. The distribution requirement is relatively easy to achieve and verify. The following pseudo-code gives a simple card-shuffling algorithm that, when paired with the right random number generator, produces decks of cards with an even distribution.

FOR CT = 1, WHILE CT <= 52, DO

Key to the success of our algorithm is the choice of a random number generator (RNG). The RNG has a direct impact on whether the algorithm above will successfully produce decks of even distribution as well as whether these decks will be useful for secure online card play. To begin with, the RNG itself must produce an even distribution of random numbers. Pseudo-random number generators (PRNG), such as those based on the Lehmer algorithm, have been shown to possess this mathematical property. It is therefore sufficient to use a good PRNG to produce "random" numbers for card shuffling.

As we have seen, choice of initial seed for the PRNG is a make or break proposition. Everything boils down to the seed. It's absolutely essential that players using a deck of cards generated using a PRNG can't determine the seed used to produce that particular shuffle.

A brute force attempt to determine the seed used to produce a particular shuffle can be made by systematically going through each of the possible seeds, producing the associated shuffle, and comparing the result against the deck you're searching for. To avoid susceptibility to this kind of attack, the number of possible seeds needs to be large enough that it is computationally infeasible to perform an exhaustive search within certain time constraints. Note that on average only half of the seed space will need to be searched until a match is found. For the purposes of an online card game, the time constraint would be the length of that game, which is usually on the order of minutes.

In our experience, a simple program running on a Pentium 400 computer is able to examine approximately 2 million seeds per minute. At this rate, this single machine could exhaustively search a 32-bit seed space (2^32 possible seeds) in a little over a day. Although that time period is certainly beyond the time constraints we have imposed on ourselves, it is certainly not infeasible to use a network of computers to perform a distributed search within our real time bounds.

The subject of brute force attacks serves to emphasize the aptness of our analogy between key length in a cryptographic algorithm and the seed behind shuffling. A brute-force cryptographic attack involves attempting every possible key in order to decrypt a secret message. Likewise a brute-force attack against a shuffling algorithm involves examining every possible seed. A significant body of research studying the necessary lengths of cryptographic keys already exists. Generally speaking, things look like this:

Algorithm Weak Key Typical Key Strong Key
DES 40 or 56 56 Triple-DES
RC4 60 80 128
RSA 512 768 or 1024 2048
ECC 125 170 230

People used to think that cracking 56-bit DES in real time would take too long to be feasible, but history has shown otherwise. In January 1997, a secret DES key was recovered in 96 days. Later efforts broke keys in 41 days, then 56 hours, and, in January 1999, in 22 hours and 15 minutes. The leap forward in cracking ability doesn't bode well for small key lengths or small sets of seeds!

People have even gone so far as to invent special machines to crack cryptographic algorithms. In 1998, the EFF created a special-purpose machine to crack DES messages. The purpose of the machine was to emphasize just how vulnerable DES (a popular, government-sanction algorithm) really is. (For more on the DES cracker, see http://www.eff.org/descracker/.) The ease of "breaking" DES is directly related to the length of its key. Special machines to uncover RNG seeds are not outside the realm of possibility.

We believe that a 32-bit seed space is not sufficient to resist a determined brute-force attack. On the other hand, a 64-bit seed should be resistant to almost any brute force attack. A 64-bit seed makes sense since many computers provide the ability to work with 64-bit integers off the shelf, and a 64-bit number should be sufficient for the purposes of protecting card shuffling against brute-force attacks.

64-bits alone won't do it though. We can't overemphasize that there must be absolutely no way for an attacker to predict or approximate the seed that is used by the PRNG. If there is a way to predict the seed, then the computationally-taxing brute-force attack outlined above becomes irrelevant (since the entire system breaks much more easily). In terms of our exploit, not only did ASF's flawed approach rely on a too-small 32-bit PRNG, but the approach relies on a seed based on the time of day as well. As our exploit demonstrated, there is very little randomness in such an approach.

In final analysis, the security of the entire system relies on picking a random seed in a non-predictable manner. The best techniques for choosing such a random number are hardware-based. Hardware-based approaches rely on unpredictably random data gathered directly from the physical environment. Since online poker and other games involving real money are extremely security critical undertakings, it's probably worth investing the necessary resources to ensure that random number generation is done correctly.

In concert, a good shuffling algorithm and a 64-bit pseudo-random number generator seeded with a proven hardware device should produce shuffles that are both fair and secure. Implementing a fair system is not overly difficult. Online poker players should demand it.

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