Weakness · High
CWE-332: Insufficient Entropy in PRNG
What it is
The lack of entropy available for, or used by, a Pseudo-Random Number Generator (PRNG) can be a stability and security threat.
- Severity (our rating)
- High
- Likelihood of exploit
- Medium
- Introduced during
- Architecture and Design, Implementation
- Exploited in the wild
- No CVE in CISA's catalog
What an attacker can do
- DoS: Crash, Exit, or Restart (Availability)
- Bypass Protection Mechanism (Access Control, Other)
- Other (Access Control, Other)
How to prevent it
Architecture and Design
- Use products or modules that conform to FIPS 140-2 to avoid obvious entropy problems. Consult FIPS 140-2 Annex C ("Approved Random Number Generators").
- When deciding which PRNG to use, look at its sources of entropy. Depending on what your security needs are, you may need to use a random number generator that always uses strong random data -- i.e., a random number generator that attempts to be strong but will fail in a weak way or will always provide some middle ground of protection through techniques like re-seeding. Generally, something that always provides a predictable amount of strength is preferable.
Implementation
- Consider a PRNG that re-seeds itself as needed from high-quality pseudo-random output, such as hardware devices.
How to find it
- Automated Static Analysis. Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect "sources" (origins of input) with "sinks" (destinations where the data interacts with external components, a lower layer such as the OS, etc.)
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Start freeSource: MITRE Common Weakness Enumeration, CWE version 4.19.1. CWE is a trademark of The MITRE Corporation.