CWE-1426 Base Incomplete

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…

Definition

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.
Auswirkungen in der Praxis

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).

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-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.
Erkennungssignale

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.

Plexicus Auto-Fix

Plexicus erkennt CWE-1426 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-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.

Wie gravierend ist CWE-1426?

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-1426 betroffen?

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

Wie kann ich CWE-1426 verhindern?

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.

Wie erkennt und behebt Plexicus CWE-1426?

Die SAST-Engine von Plexicus erkennt die Datenfluss-Signatur von CWE-1426 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-1426?

MITRE veröffentlicht die kanonische Definition unter https://cwe.mitre.org/data/definitions/1426.html. Für ergänzende Hinweise kannst du auch die OWASP- und NIST-Dokumentation heranziehen.

Verwandte Schwachstellen

Weaknesses related to CWE-1426

CWE-707 Parent

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 Sibling

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 Sibling

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 Sibling

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 Sibling

Encoding Error

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

CWE-182 Sibling

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 Sibling

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 Sibling

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 Sibling

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…

Ressourcen

Further reading

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