CWE-1039 Class Incomplete

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…

Definition

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.
Auswirkungen in der Praxis

Real-world CVEs caused by CWE-1039

Bisher sind in MITREs Katalog keine öffentlichen CVE-Referenzen mit dieser CWE verknüpft.

Wie Angreifer es ausnutzen

Angreiferpfad Schritt für Schritt

  1. 1

    Identifiziere einen Codepfad, der nicht vertrauenswürdige Eingaben ohne Validierung verarbeitet.

  2. 2

    Erzeuge eine Payload, die das unsichere Verhalten auslöst — Injection, Traversal, Overflow oder Logik-Missbrauch.

  3. 3

    Liefere die Payload über einen normalen Request aus und beobachte die Reaktion der Anwendung.

  4. 4

    Iteriere, bis die Antwort Daten preisgibt, Angreifer-Code ausführt oder Berechtigungen eskaliert.

Verwundbares Codebeispiel

Vulnerable pseudo

MITRE hat kein Codebeispiel für diese CWE veröffentlicht. Das untenstehende Muster ist illustrativ — kanonische Referenzen findest du unter Ressourcen.

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

Secure pseudo

Sicher 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.
Präventions-Checkliste

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.
Erkennungssignale

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.

Plexicus Auto-Fix

Plexicus erkennt CWE-1039 automatisch und öffnet in unter 60 Sekunden einen Fix-PR.

Codex Remedium scannt jeden Commit, identifiziert genau diese Schwachstelle und liefert einen reviewer-ready Pull Request mit dem Patch. Keine Tickets. Keine Hand-offs.

Demo anfordern Plexicus kostenlos testen
Häufig gestellte Fragen

Frequently asked questions

Was ist 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.

Wie gravierend ist CWE-1039?

MITRE hat für diese Schwachstelle keine Exploit-Wahrscheinlichkeit veröffentlicht. Behandle sie als mittlere Auswirkung, bis dein Threat Model anderes belegt.

Welche Sprachen oder Plattformen sind von CWE-1039 betroffen?

MITRE lists the following affected platforms: AI/ML.

Wie kann ich CWE-1039 verhindern?

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.

Wie erkennt und behebt Plexicus CWE-1039?

Die SAST-Engine von Plexicus erkennt die Datenfluss-Signatur von CWE-1039 bei jedem Commit. Bei einem Treffer öffnet unser Codex-Remedium-Agent einen Fix-PR mit korrigiertem Code, Tests und einer einzeiligen Zusammenfassung für den Reviewer.

Wo erfahre ich mehr über CWE-1039?

MITRE veröffentlicht die kanonische Definition unter https://cwe.mitre.org/data/definitions/1039.html. Für ergänzende Hinweise kannst du auch die OWASP- und NIST-Dokumentation heranziehen.

Verwandte Schwachstellen

Weaknesses related to CWE-1039

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CWE-1319 Sibling

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Ressourcen

Further reading

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