Final answer:
The flaw with congruential RNG lies in its predictability and susceptibility to patterns. These generators are based on a formula that produces a sequence of seemingly random numbers. However, with enough knowledge of the formula and a few initial numbers, someone could potentially predict the entire sequence of numbers generated.
Step-by-step explanation:
The flaw with congruential RNG (Random Number Generator) lies in its predictability and susceptibility to patterns. These generators are based on a formula that produces a sequence of seemingly random numbers. However, with enough knowledge of the formula and a few initial numbers, someone could potentially predict the entire sequence of numbers generated. This makes them unsuitable for applications that require high levels of randomness and security, such as cryptography.
For example, let's consider a simple linear congruential generator. It uses the formula:
Xn+1 = (aXn + c) % m
Where Xn is the current random number, a and c are constants, and m is the modulus. By knowing the values of a, c, and m, and with just a few generated numbers, one can deduce the internal state and predict all subsequent numbers in the sequence.
To overcome this flaw, more advanced pseudorandom number generators, such as cryptographic RNGs, are used.