Random Number Issues

A category in the Common Weakness Enumeration published by The MITRE Corporation.


Summary

Categories in the Common Weakness Enumeration (CWE) group entries based on some common characteristic or attribute.

Weaknesses in this category are related to a software system's random number generation.

Weaknesses

Incorrect Usage of Seeds in Pseudo-Random Number Generator (PRNG)

The software uses a Pseudo-Random Number Generator (PRNG) that does not correctly manage seeds.

Insufficient Entropy

The software uses an algorithm or scheme that produces insufficient entropy, leaving patterns or clusters of values that are more likely to occur than others.

Predictable Exact Value from Previous Values

An exact value or random number can be precisely predicted by observing previous values.

Predictable from Observable State

A number or object is predictable based on observations that the attacker can make about the state of the system or network, such as time, process ID, etc.

Predictable Value Range from Previous Values

The software's random number generator produces a series of values which, when observed, can be used to infer a relatively small range of possibilities for the next va...

Small Space of Random Values

The number of possible random values is smaller than needed by the product, making it more susceptible to brute force attacks.

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.

Use of Predictable Algorithm in Random Number Generator

The device uses an algorithm that is predictable and generates a pseudo-random number.

Concepts

Software Development

This view organizes weaknesses around concepts that are frequently used or encountered in software development. This includes all aspects of the software development l...


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