CWE-1039 Clase Incompleto

Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism

This vulnerability occurs when a system uses automated AI or machine learning to classify complex inputs like images, audio, or text, but fails to correctly identify or process inputs that have been…

Definición

What is CWE-1039?

This vulnerability occurs when a system uses automated AI or machine learning to classify complex inputs like images, audio, or text, but fails to correctly identify or process inputs that have been deliberately altered. Attackers can exploit this by crafting subtle modifications that cause the system to misclassify the input, leading to incorrect and potentially harmful decisions.
When machine learning models are deployed for security-critical tasks—such as autonomous vehicle perception, content moderation, or fraud detection—their classification errors become direct security flaws. Attackers can exploit weaknesses in the model's training or design by creating adversarial inputs (e.g., subtly perturbed images, malicious audio clips, or jailbreak prompts for LLMs) to force misclassification, bypass safeguards, or disrupt services. This is especially dangerous in systems where automated recognition directly triggers actions without human oversight. Preventing these attacks requires robust adversarial training, continuous testing with malicious inputs, and implementing input validation layers. Managing this at scale across multiple AI components is difficult; an ASPM like Plexicus can help you inventory, track, and prioritize these model vulnerabilities alongside traditional code flaws in your entire application stack.
Impacto en el mundo real

Real-world CVEs caused by CWE-1039

Todavía no hay CVEs públicos enlazados a esta CWE en el catálogo de MITRE.

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-1039

  • Architecture and Design Algorithmic modifications such as model pruning or compression can help mitigate this weakness. Model pruning ensures that only weights that are most relevant to the task are used in the inference of incoming data and has shown resilience to adversarial perturbed data.
  • Architecture and Design Consider implementing adversarial training, a method that introduces adversarial examples into the training data to promote robustness of algorithm at inference time.
  • Architecture and Design Consider implementing model hardening to fortify the internal structure of the algorithm, including techniques such as regularization and optimization to desensitize algorithms to minor input perturbations and/or changes.
  • Implementation Consider implementing multiple models or using model ensembling techniques to improve robustness of individual model weaknesses against adversarial input perturbations.
  • Implementation Incorporate uncertainty estimations into the algorithm that trigger human intervention or secondary/fallback software when reached. This could be when inference predictions and confidence scores are abnormally high/low comparative to expected model performance.
  • Integration Reactive defenses such as input sanitization, defensive distillation, and input transformations can all be implemented before input data reaches the algorithm for inference.
  • Integration Consider reducing the output granularity of the inference/prediction such that attackers cannot gain additional information due to leakage in order to craft adversarially perturbed data.
Señales de detección

How to detect CWE-1039

Dynamic Analysis with Manual Results Interpretation

Use indicators from model performance deviations such as sudden drops in accuracy or unexpected outputs to verify the model.

Dynamic Analysis with Manual Results Interpretation

Use indicators from input data collection mechanisms to verify that inputs are statistically within the distribution of the training and test data.

Architecture or Design Review

Use multiple models or model ensembling techniques to check for consistency of predictions/inferences.

Auto-corrección de Plexicus

Plexicus detecta automáticamente CWE-1039 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-1039?

This vulnerability occurs when a system uses automated AI or machine learning to classify complex inputs like images, audio, or text, but fails to correctly identify or process inputs that have been deliberately altered. Attackers can exploit this by crafting subtle modifications that cause the system to misclassify the input, leading to incorrect and potentially harmful decisions.

¿Qué gravedad tiene CWE-1039?

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-1039?

MITRE lists the following affected platforms: AI/ML.

¿Cómo puedo prevenir CWE-1039?

Algorithmic modifications such as model pruning or compression can help mitigate this weakness. Model pruning ensures that only weights that are most relevant to the task are used in the inference of incoming data and has shown resilience to adversarial perturbed data. Consider implementing adversarial training, a method that introduces adversarial examples into the training data to promote robustness of algorithm at inference time.

¿Cómo detecta y corrige Plexicus CWE-1039?

El motor SAST de Plexicus detecta la firma de flujo de datos para CWE-1039 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-1039?

MITRE publica la definición canónica en https://cwe.mitre.org/data/definitions/1039.html. También puedes consultar la documentación de OWASP y NIST para guías relacionadas.

Debilidades relacionadas

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Recursos

Further reading

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