Same Seed in Pseudo-Random Number Generator (PRNG)

A Pseudo-Random Number Generator (PRNG) uses the same seed each time the product is initialized.

Abstraction

Variant

Structure

Simple

Likelihood of Exploit

Medium

Understand the problem

What is the problem?

A Pseudo-Random Number Generator (PRNG) uses the same seed each time the product is initialized.

Extended description

Given the deterministic nature of PRNGs, using the same seed for each initialization will lead to the same output in the same order. If an attacker can guess (or knows) the seed, then the attacker may be able to determine the random numbers that will be produced from the PRNG.

Root cause

How and when does the problem arise?

Lifecycle phase

Implementation

REALIZATION: This weakness is caused during implementation of an architectural security tactic.

Risk

What does successful exploitation lead to?

OtherBypass Protection Mechanism

Affected security scopes: Other, Access Control

Defence

How is it prevented and fixed?

Architecture and Design

Architecture and design approach

Detail

Do not reuse PRNG seeds. Consider a PRNG that periodically re-seeds itself as needed from a high quality pseudo-random output, such as hardware devices.

Architecture and Design · Requirements

Libraries or Frameworks

Detail

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.

Verification

How is it detected?

Automated Static Analysis

High

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.)