CWE-1039 Classe 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…

Definição

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 no mundo real

Real-world CVEs caused by CWE-1039

Ainda não há referências CVE públicas associadas a este CWE no catálogo da MITRE.

Como os atacantes a exploram

Trajeto do atacante passo a passo

  1. 1

    Identificar um caminho de código que trata input não confiável sem validação.

  2. 2

    Criar um payload que explora o comportamento inseguro — injeção, traversal, overflow ou abuso de lógica.

  3. 3

    Entregar o payload através de um pedido normal e observar a reação da aplicação.

  4. 4

    Iterar até que a resposta exponha dados, execute código do atacante ou escale privilégios.

Exemplo de código vulnerável

Vulnerable pseudo

A MITRE não publicou um exemplo de código para este CWE. O padrão abaixo é ilustrativo — consulte os Recursos para referências canónicas.

Vulnerável pseudo 
// Example pattern — see MITRE for the canonical references.
function handleRequest(input) {
  // Untrusted input flows directly into the sensitive sink.
  return executeUnsafe(input);
}
Exemplo 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 verificação de prevenção

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.
Sinais de deteção

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.

Correção automática do Plexicus

O Plexicus deteta automaticamente o CWE-1039 e abre um PR de correção em menos de 60 segundos.

O Codex Remedium analisa cada commit, identifica esta fraqueza exata e entrega um pull request pronto para revisão com o patch. Sem tickets. Sem transferências.

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Perguntas frequentes

Frequently asked questions

O que é o 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.

Qual a gravidade do CWE-1039?

A MITRE não publicou uma classificação de probabilidade de exploração para esta fraqueza. Trate-a como impacto médio até o seu modelo de ameaças provar o contrário.

Que linguagens ou plataformas são afetadas pelo CWE-1039?

MITRE lists the following affected platforms: AI/ML.

Como posso prevenir o 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.

Como é que o Plexicus deteta e corrige o CWE-1039?

O motor SAST do Plexicus correlaciona a assinatura de fluxo de dados do CWE-1039 em cada commit. Quando é encontrada uma correspondência, o nosso agente Codex Remedium abre um PR de correção com o código corrigido, testes e um resumo de uma linha para o revisor.

Onde posso saber mais sobre o CWE-1039?

A MITRE publica a definição canónica em https://cwe.mitre.org/data/definitions/1039.html. Pode também consultar a documentação da OWASP e do NIST para orientações adjacentes.

Fraquezas relacionadas

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Recursos

Further reading

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