CWE-1241: Use of Predictable Algorithm in Random Number Generator
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.
Modes of Introduction
Phase | Note |
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Architecture and Design | |
Implementation | In many cases, the design originally defines a cryptographically secure random number generator, but is then changed during implementation due to unforeseen constraints. |
Applicable Platforms
Type | Class | Name | Prevalence |
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Technology | System on Chip |
Common Attack Pattern Enumeration and Classification (CAPEC)
The Common Attack Pattern Enumeration and Classification (CAPECâ„¢) effort provides a publicly available catalog of common attack patterns that helps users understand how adversaries exploit weaknesses in applications and other cyber-enabled capabilities.
CAPEC at Mitre.orgCVEs Published
CVSS Severity
CVSS Severity - By Year
CVSS Base Score
# CVE | Description | CVSS | EPSS | EPSS Trend (30 days) | Affected Products | Weaknesses | Security Advisories | Exploits | PoC | Pubblication Date | Modification Date |
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# CVE | Description | CVSS | EPSS | EPSS Trend (30 days) | Affected Products | Weaknesses | Security Advisories | PoC | Pubblication Date | Modification Date |