CWE-1426 Base Incompleto

Improper Validation of Generative AI Output

This vulnerability occurs when an application uses a generative AI model (like an LLM) but fails to properly check the AI's output before using it. Without this validation, the AI's responses might…

Definición

What is CWE-1426?

This vulnerability occurs when an application uses a generative AI model (like an LLM) but fails to properly check the AI's output before using it. Without this validation, the AI's responses might contain security flaws, harmful content, or data leaks that violate the application's intended policies.
Generative AI models are powerful but unpredictable. They can be tricked into producing malicious code, biased decisions, offensive content, or sensitive training data. If your application blindly trusts and acts on these outputs, it can lead to injection attacks, compliance violations, or data breaches. You must implement robust validation checks—like content filtering, code sanitization, and policy enforcement—on every AI response before it's processed further. Continuously monitoring for these validation failures across all your AI-integrated services is a complex challenge. An ASPM platform like Plexicus can automatically detect these flaws in your runtime environment, while its AI-powered remediation provides specific fixes to harden your validation logic, ensuring your AI features remain secure and reliable.
Impacto en el mundo real

Real-world CVEs caused by CWE-1426

  • CVE-2024-3402

    chain: GUI for ChatGPT API performs input validation but does not properly "sanitize" or validate model output data (CWE-1426), leading to XSS (CWE-79).

Cómo lo explotan los atacantes

Ruta del atacante paso a paso

  1. 1

    Identifica una ruta de código que maneje entrada no confiable sin validación.

  2. 2

    Crea un payload que ejercite el comportamiento inseguro — inyección, traversal, overflow o abuso de lógica.

  3. 3

    Envía el payload a través de una solicitud normal y observa la reacción de la aplicación.

  4. 4

    Itera hasta que la respuesta filtre datos, ejecute código del atacante o escale privilegios.

Ejemplo de código vulnerable

Vulnerable pseudo

MITRE no ha publicado un ejemplo de código para esta CWE. El patrón siguiente es ilustrativo — consulta Recursos para referencias canónicas.

Vulnerable pseudo 
// Example pattern — see MITRE for the canonical references.
function handleRequest(input) {
  // Untrusted input flows directly into the sensitive sink.
  return executeUnsafe(input);
}
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-1426

  • Architecture and Design Since the output from a generative AI component (such as an LLM) cannot be trusted, ensure that it operates in an untrusted or non-privileged space.
  • Operation Use "semantic comparators," which are mechanisms that provide semantic comparison to identify objects that might appear different but are semantically similar.
  • Operation Use components that operate externally to the system to monitor the output and act as a moderator. These components are called different terms, such as supervisors or guardrails.
  • Build and Compilation During model training, use an appropriate variety of good and bad examples to guide preferred outputs.
Señales de detección

How to detect CWE-1426

Dynamic Analysis with Manual Results Interpretation

Use known techniques for prompt injection and other attacks, and adjust the attacks to be more specific to the model or system.

Dynamic Analysis with Automated Results Interpretation

Use known techniques for prompt injection and other attacks, and adjust the attacks to be more specific to the model or system.

Architecture or Design Review

Review of the product design can be effective, but it works best in conjunction with dynamic analysis.

Auto-corrección de Plexicus

Plexicus detecta automáticamente CWE-1426 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-1426?

This vulnerability occurs when an application uses a generative AI model (like an LLM) but fails to properly check the AI's output before using it. Without this validation, the AI's responses might contain security flaws, harmful content, or data leaks that violate the application's intended policies.

¿Qué gravedad tiene CWE-1426?

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-1426?

MITRE lists the following affected platforms: Not Architecture-Specific, AI/ML, Not Technology-Specific.

¿Cómo puedo prevenir CWE-1426?

Since the output from a generative AI component (such as an LLM) cannot be trusted, ensure that it operates in an untrusted or non-privileged space. Use "semantic comparators," which are mechanisms that provide semantic comparison to identify objects that might appear different but are semantically similar.

¿Cómo detecta y corrige Plexicus CWE-1426?

El motor SAST de Plexicus detecta la firma de flujo de datos para CWE-1426 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-1426?

MITRE publica la definición canónica en https://cwe.mitre.org/data/definitions/1426.html. También puedes consultar la documentación de OWASP y NIST para guías relacionadas.

Debilidades relacionadas

Weaknesses related to CWE-1426

CWE-707 Padre

Improper Neutralization

This vulnerability occurs when an application fails to properly validate or sanitize structured data before it's received from an external…

CWE-116 Hermano

Improper Encoding or Escaping of Output

This vulnerability occurs when an application builds a structured message—like a query, command, or request—for another component but…

CWE-138 Hermano

Improper Neutralization of Special Elements

This vulnerability occurs when an application accepts external input but fails to properly sanitize special characters or syntax that have…

CWE-170 Hermano

Improper Null Termination

This weakness occurs when software fails to properly end a string or array with the required null character or equivalent terminator.

CWE-172 Hermano

Encoding Error

This vulnerability occurs when software incorrectly transforms data between different formats, leading to corrupted or misinterpreted…

CWE-182 Hermano

Collapse of Data into Unsafe Value

This vulnerability occurs when an application's data filtering or transformation process incorrectly merges or simplifies information,…

CWE-20 Hermano

Improper Input Validation

This vulnerability occurs when an application accepts data from an external source but fails to properly verify that the data is safe and…

CWE-228 Hermano

Improper Handling of Syntactically Invalid Structure

This vulnerability occurs when software fails to properly reject or process input that doesn't follow the expected format or structure,…

CWE-240 Hermano

Improper Handling of Inconsistent Structural Elements

This vulnerability occurs when a system fails to properly manage situations where related data structures or elements should match but are…

Recursos

Further reading

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