# Use of Predictable Algorithm in Random Number Generator

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

## Description

Pseudo-random number generator algorithms are predictable because their registers have a finite number of possible states, which eventually lead to repeating patterns. As a result, pseudo-random number generators (PRNGs) can compromise their randomness or expose their internal state to various attacks, such as reverse engineering or tampering. It is highly recommended to use hardware-based true random number generators (TRNGs) to ensure the security of encryption schemes. TRNGs generate unpredictable, unbiased, and independent random numbers because they employ physical phenomena, e.g., electrical noise, as sources to generate random numbers.

## Demonstrations

The following examples help to illustrate the nature of this weakness and describe methods or techniques which can be used to mitigate the risk.

Note that the examples here are by no means exhaustive and any given weakness may have many subtle varieties, each of which may require different detection methods or runtime controls.

### Example One

Suppose a cryptographic function expects random value to be supplied for the crypto algorithm.

During the implementation phase, due to space constraint, a cryptographically secure random-number-generator could not be used, and instead of using a TRNG (True Random Number Generator), a LFSR (Linear Feedback Shift Register) is used to generate a random value. While an LFSR will provide a pseudo-random number, its entropy (measure of randomness) is insufficient for a cryptographic algorithm.

### Example Two

The example code is taken from the PRNG inside the buggy OpenPiton SoC of HACK@DAC'21 [REF-1370]. The SoC implements a pseudo-random number generator using a Linear Feedback Shift Register (LFSR).

An example of LFSR with the polynomial function P(x) = x

+x

+x

+1 is shown in the figure.

A LFSR's input bit is determined by the output of a linear function of two or more of its previous states. Therefore, given a long cycle, a LFSR-based PRNG will enter a repeating cycle, which is predictable.

Comprehensive Categorization: Randomness

Weaknesses in this category are related to randomness.

Random Number Issues

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

Security Primitives and Cryptography Issues

Weaknesses in this category are related to hardware implementations of cryptographic protocols and other hardware-security primitives such as physical unclonable funct...

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