Early Access — Mondoo Vulnerability Intelligence is currently in preview.
A Pseudo-Random Number Generator (PRNG) is initialized from a predictable seed, such as the process ID or system time.
The use of predictable seeds significantly reduces the number of possible seeds that an attacker would need to test in order to predict which random numbers will be generated by the PRNG.
Use non-predictable inputs for seed generation.
Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems, or use the more recent FIPS 140-3 [REF-1192] if possible.
Use a PRNG that periodically re-seeds itself using input from high-quality sources, such as hardware devices with high entropy. However, do not re-seed too frequently, or else the entropy source might block.
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.)
CVE-2020-7010Cloud application on Kubernetes generates passwords using a weak random number generator based on deployment time.
CVE-2019-11495server uses erlang:now() to seed the PRNG, which results in a small search space for potential random seeds
CVE-2008-0166The removal of a couple lines of code caused Debian's OpenSSL Package to only use the current process ID for seeding a PRNG
CVE-2016-10180Router's PIN generation is based on rand(time(0)) seeding.
CVE-2018-9057cloud provider product uses a non-cryptographically secure PRNG and seeds it with the current time