CWE-1426: Improper Validation of Generative AI Output

Definição

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.

Impacto no mundo real

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).

Como os atacantes a exploram

Trajeto do atacante passo a passo

1
Identificar um caminho de código que trata input não confiável sem validação.
2
Criar um payload que explora o comportamento inseguro — injeção, traversal, overflow ou abuso de lógica.
3
Entregar o payload através de um pedido normal e observar a reação da aplicação.
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-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.

Sinais de deteção

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.

CWE-1426

Don't catalog this weakness. Prove it's reachable.

Plexicus turns CWE catalogs into evidence: every CWE-pattern is matched against your real code graph, reach is proven on a sandbox clone, and verified findings ship as reviewed PRs.

Request AI Swarm Pentest

Perguntas frequentes

Frequently asked questions

O que é o 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.

Qual a gravidade do CWE-1426?

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-1426?

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

Como posso prevenir o CWE-1426?

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.

Como é que o Plexicus deteta e corrige o CWE-1426?

O motor SAST do Plexicus correlaciona a assinatura de fluxo de dados do CWE-1426 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-1426?

A MITRE publica a definição canónica em https://cwe.mitre.org/data/definitions/1426.html. Pode também consultar a documentação da OWASP e do NIST para orientações adjacentes.

Fraquezas relacionadas

Weaknesses related to CWE-1426

CWE-707 Pai

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Reservar Pentest

Improper Validation of Generative AI Output

What is CWE-1426?

Real-world CVEs caused by CWE-1426

Trajeto do atacante passo a passo

Vulnerable pseudo

Secure pseudo

How to prevent CWE-1426

How to detect CWE-1426

Don't catalog this weakness. Prove it's reachable.

Frequently asked questions

Weaknesses related to CWE-1426

Improper Neutralization

Improper Encoding or Escaping of Output

Improper Neutralization of Special Elements

Improper Null Termination

Encoding Error

Collapse of Data into Unsafe Value

Improper Input Validation

Improper Handling of Syntactically Invalid Structure

Improper Handling of Inconsistent Structural Elements

Further reading

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Sample evidence handover

Server-Side Request Forgery in `webhooks/receiver`