![]() ![]() Using cryptographically secure random number generation algorithms is the key to securely producing random numbers. Some methods may involve statistical analysis, while others may involve reverse-engineering the generator’s algorithm. In pratice, hackers use different methods to predict the next random number. If an attacker can find the used seed or manipulate it, he can easily generate same random numbers. PRNG always generates the same sequence of numbers with the same initial seed value. Seed Leakage or ManipulationĪnother vulnerability related to random number generators is choosing a weak seed. This can lead to vulnerabilities where a user can see another user’s data. And bad random generator does not generate all possible values. Short-length tokens will have a higher chance of collisions. Size of random space (length of session token).The chances of producing duplicate session tokens are related to two factors: This can increase the risk of collision.Ĭonsider an application that generates random session tokens for its users. Collisionīesides the predictability, Some random generators that have low quality produce duplicate values very often. If the token can be predicted, an attacker can reset the password of any user. This can lead to severe vulnerabilities.įor example, consider a forget password functionality that words based on a random token. So if you know the seed and last random number, you can predict the next random number. PRNGs are inherently predictable as they are based on a mathematical formula. You can say predictability is the main vulnerability of weak random number generators. The seed and random number generation algorithm both can have weaknesses. TRNGs measure the physical process and convert it into a random number. These processes include atmospheric noise, radioactive decay, and thermal noise. TRNGs generate truly random numbers based on physical processes that are inherently random. Any PRNG has a fixed length of numbers that can generate before starting over. If you keep executing any PRNG, it will eventually generate the same numbers over and over. The output looks like a random number though it’s just an output of a formula. They change it by adding previous random number, shifting, and doing XOR operators to generate the output. These algorithms get an initial number called seed. The common algorithms used in PRNGs are the Mersenne-Twister and linear congruential generator (LCG). The algorithm takes an initial value as input and produces a series of numbers based on it. ![]() They work by using an algorithm to generate a sequence of numbers that appear to be random. PRNGs are the most commonly used type of RNGs. There are two main types of Random Number Generators (RNGs): pseudo-random number generators (PRNGs) and true random number generators (TRNGs).
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