CWE-1241 Base Borrador

Use of Predictable Algorithm in Random Number Generator

This vulnerability occurs when a device or application relies on a predictable algorithm to generate pseudo-random numbers, making the output sequence foreseeable.

Definición

What is CWE-1241?

This vulnerability occurs when a device or application relies on a predictable algorithm to generate pseudo-random numbers, making the output sequence foreseeable.
Pseudo-random number generators (PRNGs) create numbers using deterministic algorithms, meaning they have a finite internal state that will eventually repeat. This predictability makes them vulnerable to attacks where an adversary can analyze past outputs to deduce future values or uncover the generator's internal state, compromising the security of any system that depends on this randomness. For robust security, especially in encryption, key generation, or session tokens, it's critical to use hardware-based True Random Number Generators (TRNGs). TRNGs derive randomness from unpredictable physical processes like electrical noise, producing outputs that are unbiased, independent, and fundamentally unpredictable, thereby providing a much stronger foundation for security-critical operations.
Impacto en el mundo real

Real-world CVEs caused by CWE-1241

  • CVE-2021-3692

    PHP framework uses mt_rand() function (Marsenne Twister) when generating tokens

Cómo lo explotan los atacantes

Ruta del atacante paso a paso

  1. 1

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

  2. 2

    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.

  3. 3

    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).

  4. 4

    An example of LFSR with the polynomial function P(x) = x 6+x 4+x 3+1 is shown in the figure.

  5. 5

    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.

Ejemplo de código vulnerable

Vulnerable Verilog

An example of LFSR with the polynomial function P(x) = x 6+x 4+x 3+1 is shown in the figure.

Vulnerable Verilog 
**reg in_sr, entropy16_valid;** 

 **reg [15:0] entropy16;** 


 **assign entropy16_o = entropy16;** 

 **assign entropy16_valid_o = entropy16_valid;** 


 **always @ (*)** 

 **begin** 

```
```
in_sr = ^ (poly_i [15:0] & entropy16 [15:0]);** 
  
 **end**
Ejemplo de código seguro

Secure pseudo

Seguro pseudo 
// Validate, sanitize, or use a safe API before reaching the sink.
function handleRequest(input) {
  const safe = validateAndEscape(input);
  return executeWithGuards(safe);
}
What changed: the unsafe sink is replaced (or the input is validated/escaped) so the same payload no longer triggers the weakness.
Lista de prevención

How to prevent CWE-1241

  • Architecture and Design A true random number generator should be specified for cryptographic algorithms.
  • Implementation A true random number generator should be implemented for cryptographic algorithms.
Señales de detección

How to detect CWE-1241

SAST High

Ejecuta análisis estático (SAST) sobre el código buscando el patrón inseguro en el flujo de datos.

DAST Moderate

Ejecuta pruebas dinámicas de seguridad de aplicaciones (DAST) contra el endpoint en vivo.

Runtime Moderate

Vigila los logs en tiempo de ejecución para detectar trazas de excepción inusuales, entradas malformadas o intentos de bypass de autorización.

Code review Moderate

Revisión de código: marca cualquier código nuevo que maneje entrada desde esta superficie sin usar los helpers validados del framework.

Auto-corrección de Plexicus

Plexicus detecta automáticamente CWE-1241 y abre un PR de corrección en menos de 60 segundos.

Codex Remedium escanea cada commit, identifica esta debilidad concreta y entrega un pull request listo para revisión con el parche. Sin tickets. Sin traspasos.

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Preguntas frecuentes

Frequently asked questions

¿Qué es CWE-1241?

This vulnerability occurs when a device or application relies on a predictable algorithm to generate pseudo-random numbers, making the output sequence foreseeable.

¿Qué gravedad tiene CWE-1241?

MITRE no ha publicado una calificación de probabilidad de explotación para esta debilidad. Trátala como de impacto medio hasta que tu modelo de amenazas demuestre lo contrario.

¿Qué lenguajes o plataformas se ven afectados por CWE-1241?

MITRE lists the following affected platforms: System on Chip.

¿Cómo puedo prevenir CWE-1241?

A true random number generator should be specified for cryptographic algorithms. A true random number generator should be implemented for cryptographic algorithms.

¿Cómo detecta y corrige Plexicus CWE-1241?

El motor SAST de Plexicus detecta la firma de flujo de datos para CWE-1241 en cada commit. Cuando hay coincidencia, nuestro agente Codex Remedium abre un PR de corrección con el código corregido, las pruebas y un resumen de una línea para el revisor.

¿Dónde puedo aprender más sobre CWE-1241?

MITRE publica la definición canónica en https://cwe.mitre.org/data/definitions/1241.html. También puedes consultar la documentación de OWASP y NIST para guías relacionadas.

Debilidades relacionadas

Weaknesses related to CWE-1241

CWE-330 Padre

Use of Insufficiently Random Values

This vulnerability occurs when an application uses random values that are not sufficiently unpredictable in security-sensitive operations,…

CWE-1204 Hermano

Generation of Weak Initialization Vector (IV)

This vulnerability occurs when software uses a weak or predictable Initialization Vector (IV) for cryptographic operations. Many…

CWE-331 Hermano

Insufficient Entropy

This vulnerability occurs when a system's random number generator or algorithm lacks sufficient unpredictability, creating patterns or…

CWE-334 Hermano

Small Space of Random Values

This vulnerability occurs when a system uses a random number generator that produces too few possible values. Attackers can easily predict…

CWE-335 Hermano

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

This vulnerability occurs when a Pseudo-Random Number Generator (PRNG) is used, but its initial seed value is not handled securely or…

CWE-338 Hermano

Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)

This vulnerability occurs when software uses a pseudo-random number generator (PRNG) that is not cryptographically strong for…

CWE-340 Hermano

Generation of Predictable Numbers or Identifiers

This vulnerability occurs when a system creates numbers or identifiers that are too easy to guess, undermining security mechanisms that…

CWE-344 Hermano

Use of Invariant Value in Dynamically Changing Context

This vulnerability occurs when code uses a fixed, unchanging value (like a hardcoded string, number, or reference) in a situation where…

Recursos

Further reading

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