This vulnerability occurs when a Pseudo-Random Number Generator (PRNG) doesn't have enough randomness (entropy) to start with, or isn't using enough during operation. This makes its outputs predictable, undermining both system stability and security.
Insufficient entropy means the PRNG's starting state or its ongoing process lacks true randomness. Instead of producing unpredictable sequences, it may generate numbers that are weak, repeatable, or follow detectable patterns. Attackers can exploit this predictability to guess security tokens, cryptographic keys, or session identifiers, leading directly to breaches like session hijacking or data decryption. For developers, the core issue often lies in using default or poorly seeded PRNGs, especially in virtualized or embedded environments where entropy sources (like user input or hardware events) are limited. To prevent this, always use cryptographically secure PRNGs (CSPRNGs) provided by your platform's security libraries, ensure they are properly seeded with high-quality entropy from reliable system sources, and avoid creating your own random number generation algorithms.
Impact: DoS: Crash, Exit, or Restart
If a pseudo-random number generator is using a limited entropy source which runs out (if the generator fails closed), the program may pause or crash.
Impact: Bypass Protection MechanismOther
If a PRNG is using a limited entropy source which runs out, and the generator fails open, the generator could produce predictable random numbers. Potentially a weak source of random numbers could weaken the encryption method used for authentication of users.
Strategy: Libraries or Frameworks
Medium