CWE-1039 Classe Incomplet

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…

Définition

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.
Impact réel

Real-world CVEs caused by CWE-1039

Aucune référence CVE publique n'est liée à ce CWE dans le catalogue MITRE pour le moment.

Comment les attaquants l'exploitent

Parcours de l'attaquant étape par étape

  1. 1

    Identifier un chemin de code qui traite des entrées non fiables sans validation.

  2. 2

    Élaborer une charge utile qui exploite le comportement non sécurisé — injection, traversal, débordement ou abus de logique.

  3. 3

    Délivrer la charge utile via une requête normale et observer la réaction de l'application.

  4. 4

    Itérer jusqu'à ce que la réponse divulgue des données, exécute le code de l'attaquant ou élève les privilèges.

Exemple de code vulnérable

Vulnerable pseudo

MITRE n'a pas publié d'exemple de code pour ce CWE. Le motif ci-dessous est illustratif — voir Ressources pour les références canoniques.

Vulnérable pseudo 
// Example pattern — see MITRE for the canonical references.
function handleRequest(input) {
  // Untrusted input flows directly into the sensitive sink.
  return executeUnsafe(input);
}
Exemple de code sécurisé

Secure pseudo

Sécurisé 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.
Liste de contrôle de prévention

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.
Signaux de détection

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.

Correction automatique Plexicus

Plexicus détecte automatiquement CWE-1039 et ouvre une PR de correction en moins de 60 secondes.

Codex Remedium analyse chaque commit, identifie cette faiblesse précise et livre une pull request prête à être relue avec le correctif. Pas de tickets. Pas de transferts.

Obtenir une démo Essayer Plexicus gratuitement
Questions fréquentes

Frequently asked questions

Qu'est-ce que 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.

Quelle est la gravité de CWE-1039 ?

MITRE n'a pas publié de note de probabilité d'exploitation pour cette faiblesse. Traitez-la comme un impact moyen jusqu'à ce que votre modèle de menace prouve le contraire.

Quels langages ou plateformes sont affectés par CWE-1039 ?

MITRE lists the following affected platforms: AI/ML.

Comment puis-je prévenir 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.

Comment Plexicus détecte et corrige CWE-1039 ?

Le moteur SAST de Plexicus reconnaît la signature de flux de données de CWE-1039 à chaque commit. Lorsqu'une correspondance est trouvée, notre agent Codex Remedium ouvre une PR de correction avec le code corrigé, les tests et un résumé d'une ligne pour le relecteur.

Où puis-je en savoir plus sur CWE-1039 ?

MITRE publie la définition canonique à https://cwe.mitre.org/data/definitions/1039.html. Vous pouvez également consulter la documentation OWASP et NIST pour des conseils adjacents.

Faiblesses associées

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Ressources

Further reading

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