Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)
The product uses a Pseudo-Random Number Generator (PRNG) in a security context, but the PRNG's algorithm is not cryptographically strong.
When a non-cryptographic PRNG is used in a cryptographic context, it can expose the cryptography to certain types of attacks.
Often a pseudo-random number generator (PRNG) is not designed for cryptography. Sometimes a mediocre source of randomness is sufficient or preferable for algorithms that use random numbers. Weak generators generally take less processing power and/or do not use the precious, finite, entropy sources on a system. While such PRNGs might have very useful features, these same features could be used to break the cryptography.
The following examples help to illustrate the nature of this weakness and describe methods or techniques which can be used to mitigate the risk.
Note that the examples here are by no means exhaustive and any given weakness may have many subtle varieties, each of which may require different detection methods or runtime controls.
Both of these examples use a statistical PRNG seeded with the current value of the system clock to generate a random number:
The random number functions used in these examples, rand() and Random.nextInt(), are not considered cryptographically strong. An attacker may be able to predict the random numbers generated by these functions. Note that these example also exhibit CWE-337 (Predictable Seed in PRNG).
Weaknesses in this category are related to the A02 category "Cryptographic Failures" in the OWASP Top Ten 2021.
Weaknesses in this category are related to a software system's random number generation.
Weaknesses in this category are related to the rules and recommendations in the Miscellaneous (MSC) section of the SEI CERT C Coding Standard.
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