πŸ”₯ How to Cheat at Online Poker: A Study in Software Security

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Corrupt Poker Sites; Card Mechanics; Angle Shooting. 1. Botting Software. This method is by far the most prevalent type of cheating at the online.


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A new malware infecting online poker sites can let your opponents "see" your cards.


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Cheating at poker only SOUNDS like a good idea. Here you can find some ways of cheating at poker along with 5 reasons why you shouldn't.


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From the biewer-russia.ru Archives: Poker is a card game that many people around the world enjoy. Poker is played at kitchen tables, in casinos.


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Being able to play online in the comfort of your own home not to mention in your pajamas , without having to endure second-hand smoke and obnoxious players, is definitely appealing. The change is important because the n! The position to swap with is chosen by calls to the pseudo-random number generator. The dealer places three cards face up on the board called the flop. Poker is a card game that many people around the world enjoy. Though collusion is a problem in real cardrooms, it is a much more serious problem for online poker. The problem is that a software application can introduce truck-sized security holes into a system. After the second round of betting, the dealer places another card face up on the board called the turn. After the first round, all remaining cards are dealt face up and shared by all players. The best hand among the players is determined by standard poker hand order. Texas Hold'em is usually a fixed limit game, meaning that there are fixed amounts that a player may bet in each betting round. Essentially, every possible combination of cards should have an equal likelihood of appearing. When ctr finally reaches the last card, 52, that card is swapped with any other card except itself. Using tools like instant messaging and telephone conference calls makes collusion a serious risk to online poker players. Collusion occurs when two or more players seated at the same table work together as a team, often using the same bankroll. Was it fair? Texas Hold'em is a fast-paced and exciting game. Since many of us spend a good amount of our days online, it was only a matter of time before some of us put the two interests together. These risks are all very familiar to Internet security experts. The change between this new algorithm and the one used by ASF is that each card i is swapped with a card from the range [i, n], not [1, n]. This is the story of how our interest in online poker and software security mixed to create a spectacular security exploit. The first software flaw we'll focus on involves shuffling virtual cards. Getting everything exactly right from a security perspective is not easy as the rest of this article will show. That means this shuffling algorithm never allows the 52nd card to end up in the 52nd place. Ironically, it had the exact opposite effect on us. The PlanetPoker Internet cardroom offers real-time Texas Hold'em games against other people on the Web for real money. Colluding players know what their team members' hands are often through subtle signals , and bet with the purpose of maximizing their team's profits on any given hand. There are many algorithms that can be used to shuffle a deck of cards, some of which are better than others and some of which are just plain wrong. That is, some shuffles are more likely to be produced than others are. Ideally, we would generate a value on the range from 0 to 1 where every value will occur with equal probability, regardless of the previous value, then multiply that value by Note that there are an infinite number of values between 0 and 1. The initial deal is followed by a round of betting. Figure 2 contains the algorithm we used to shuffle our deck of three cards, and also depicts the tree of all possible decks using this shuffling algorithm. In a real deck of cards, there are 52! In Texas Hold'em, each player is dealt two cards called the pocket cards. Also note that computers do not offer infinite precision!{/INSERTKEYS}{/PARAGRAPH} If our random number source is a good one, then each leaf on the tree in Figure 2 has an equal probability of being produced. The associated skews can be used by a clever gambler to gain an edge, but the flaws really don't constitute a complete break in the system. It is only natural that we turned our attention to online poker. Software problems are a notorious form of security risk often overlooked by companies obsessed with firewalls and cryptography. Finally, the dealer places the last card face up on the board called the river , and a final round of betting ensues. Unfortunately, there are real risks to playing poker online. The server running the online casino could be cracked by a malicious attacker looking for credit card numbers, or trying to leverage some advantage in the game. Now that everybody and their dog is online, and virtually all types of businesses are represented on the Internet, it's only natural that casinos and cardrooms are there too. Last, but not least especially in terms of our story , there is a real risk that the software behind an online poker game may be flawed. To illustrate this problem using a small example, we'll shuffle a deck consisting of only three cards i. We'll call each such ordering of the 52 cards a shuffle. Notice that each possible shuffle is produced once and only once so that each deck has an equal probability of occurring. By contrast, the third flaw, which we explain in this section, is a doozy that allows online poker to be completely compromised. How did it work? Figure 3 provides a much better shuffling algorithm. A third round of betting then takes place. {PARAGRAPH}{INSERTKEYS}From the Developer. The basic algorithm at the heart of the shuffle is shown in Figure 2. Being software professionals who help companies deliver secure, reliable, and robust software, we were curious about the software behind the online game. All this convenience comes at a price. The first set of software flaws we discussed merely changes the probabilities that certain cards will come up. The actual shuffle is performed by swapping every position in the array, in turn, with a randomly chosen position. The crucial difference between the two algorithms is that number of possible swap positions decreases as you progress through the deck. An examination of the FAQs at PlanetPoker, including the shuffling algorithm which was ironically published to help demonstrate the game's integrity was enough to start our analysis wheels rolling. A closer examination of the algorithm reveals that, regardless of the off-by-one error, it doesn't return an even distribution of shuffles. How can you be assured that you're never a victim of this attack? This is an obvious, but easily correctable, violation of fairness. We spend a great deal of time in our day jobs finding and solving software security problems. What if all the players in an online game are all cooperating to bilk an unsuspecting Web patsy? In short, the algorithm in question never chooses to swap the current card with the last card. We have not looked at their new approach. The algorithm starts by initializing an array with values in order from 1 to 52, representing the 52 possible cards. Since a majority of casinos don't authenticate or encrypt the network traffic between the player running the client program and the server hosting the card game, a malicious player could conceivably examine the network traffic with a classic person-in-the-middle attack for the purposes of determining his opponent's cards. Even with the reasonably easy availability of casinos on Indian reservations and riverboats, there is still real demand for more accessible games. As soon as we saw the shuffling algorithm, we began to suspect there might be a problem. When a computer shuffles a virtual deck of cards, it selects one of these possible combinations. The algorithm is supposed to traverse the initial deck while swapping each card with any other card. Each remaining player takes their two pocket cards and combines them with the five community cards to make the best five-card poker hand. A second round of betting then takes place. Then, the program initializes a pseudo-random number generator using the system clock with a call to Randomize. The uneven probabilities become increasingly exaggerated as the number of cards in the deck increase. Problem Two: Bad Distribution Of Shuffles A closer examination of the shuffling algorithm reveals that, regardless of the off-by-one problem, it doesn't return an even distribution of decks. Poker is played at kitchen tables, in casinos, and cardrooms -- and now the Web. The rest of this article is devoted to a discussion of software security problems we found in a popular online poker game. This uneven distribution can be leveraged into an advantage if a tipped-off player is willing to sit at the table long enough. What does it mean to shuffle a deck of cards fairly? Unlike most Pascal functions, the function Random n actually returns a number between 0 and n-1 instead of a number between 1 and n. Now that's fair! Once again, we show a tree illustrating this algorithm on our sample deck of three cards. A short tutorial on pseudo-random number generators sets the stage for the rest of our story. Poker is played at kitchen tables, in casinos, and cardrooms -- and more recently, the Web. Suppose we want to generate a random number between 1 and 52, where every number has an equal probability of appearing. ASF has changed their algorithm since we contacted them regarding our discovery. Experienced poker players who play the odds as a normal course of business can take advantage of the skewed probabilities. Collusion is a problem that is unique to poker as opposed to other games like blackjack or craps , since poker players play against each other and not the casino itself. Bluffing is an essential part of the game, and quick decisions about who is holding what sorts of cards separate winners from losers. A little investigation proved that this intuition was correct. It will produce the decks , , and more often than the decks , , If you were betting on the first card and you knew about these probabilities, you would know that card 2 is more likely to appear than any other card. A few of us here at Reliable Software Technologies play poker. Given even this small example, you can see that the algorithm does not produce shuffles with equal probability. The shuffling algorithm shown in Figure 1 was posted by ASF Software in order to convince people that their computer-generated shuffles were entirely fair. Astute programmers will have noticed that the algorithm in question contains an off-by-one error.