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Internet GamblingVulnerabilityGamblers :-)AffectedPlaying poker is risky by nature, but playing online poker for real money may be more of a gamble than you ever expected. The Software Security Group at Reliable Software Technologies has discovered a serious flaw in the implementation of Texas Hold 'em Poker that is distributed by ASF Software, Inc. Gary McGraw was able to develop a program that exploits this flaw and is capable of determining the exact ordering of every card in a shuffled deck; this computation can be performed in real-time, during the playing of an actual poker game. This exploit enables someone to know every card that each player has been dealt and what cards will be coming up during the rest of the hand. Given this information, even the weakest of poker players should know when to hold'em, and when to fold'em. Unlike most casino games, poker is played against other players, not against the house. This means that when someone is cheating at poker, innocent people are hurt by the cheater's unscrupulous actions. ASF Software has been notified of the flaw in their system and has taken corrective actions. The exploit that Reliable Software Technologies developed no longer functions, however the potential for people to take advantage of flaws in online gambling software remains. The flaw existed in the algorithm used to produce a shuffled deck of cards before each round of play. Ironically, the code was publicly displayed at http://www.planetpoker.com/ppfaq.htm with the idea of showing how fair the game is to interested players (the page has since been taken down). The algorithm revealed that the cards were being shuffled using random numbers generated by the Delphi Pascal Random() function. Like most common random number generators, the Random() call uses the Lehmer algorithm to produce streams of pseudo-random numbers. These numbers have many of the mathematical properties associated with random numbers, however they are generated in a completely deterministic manner. This means that given a particular starting point (the random number generator's "seed") the sequence of numbers generated will follow an easily calculated pattern. The shuffling algorithm used in this software always started with an ordered deck of cards, and then generated a sequence of random numbers that were used to re-order the deck. 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. This constrains the algorithm to being able to produce only slightly more that 4 billion possible decks of cards; a number much smaller than the 52 factorial (52 * 51 * 50 * ... 1) combinations possible in a real deck of cards. The resulting number is close to 2^225. To make matters worse, the algorithm chose the seed for the random number generator using the Pascal function Randomize(). The Randomize() function chose a seed based on the number of milliseconds since midnight. Since there are only 86,400,000 milliseconds in a day, and this number was being used as the seed for the random number generator, the number of possible decks was now reduced to 86,400,000. By synchronizing program with the system clock on the server generating the pseudo-random number, we were able to further reduce the number of possible combinations down a number on the order of 200,000 possibilities. Searching through this set of shuffles is trivial and can be done on a PC in real time. The exploit that RST developed required that five cards from the deck were known, and the rest of the deck could then be deduced. In Texas hold'em poker, this meant that the program took as input the two cards that a player is dealt, plus the first three community cards that are dealt face up (called the flop). These five cards are known after the first of four rounds of betting. The program then generated shuffled decks of cards until it found a deck that contained these five cards in the proper positions. Since the Randomize() function is based on the server's system time, it was not very difficult to guess a starting seed with a fair degree of accuracy. After finding a correct seed once, it is then possible to synchronize the exploit program with the server to within a few seconds. This synchronization enables the exploit program to accurately guess the seed being used by the random number generator, and to identify the deck of cards being used during all futureystem user must be convinced that such risks have been mitigated. Members of the group involved with the Gambling exploit are: Brad Arkin, Frank Hill, Scott Marks, Matt Schmid, and TJ Walls. The Software Security Group is led by Dr.Gary McGraw.DescriptionFixed by new version.Solution