Use known techniques for prompt injection and other attacks, and adjust the attacks to be more specific to the model or system.
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…
What is CWE-1426?
Real-world CVEs caused by CWE-1426
-
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).
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.
Vulnerable pseudo
A MITRE não publicou um exemplo de código para este CWE. O padrão abaixo é ilustrativo — consulte os Recursos para referências canónicas.
// Example pattern — see MITRE for the canonical references.
function handleRequest(input) {
// Untrusted input flows directly into the sensitive sink.
return executeUnsafe(input);
} Secure pseudo
// Validate, sanitize, or use a safe API before reaching the sink.
function handleRequest(input) {
const safe = validateAndEscape(input);
return executeWithGuards(safe);
} 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.
How to detect CWE-1426
Use known techniques for prompt injection and other attacks, and adjust the attacks to be more specific to the model or system.
Review of the product design can be effective, but it works best in conjunction with dynamic analysis.
O Plexicus deteta automaticamente o CWE-1426 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.
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.
Weaknesses related to CWE-1426
Improper Neutralization
This vulnerability occurs when an application fails to properly validate or sanitize structured data before it's received from an external…
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…
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…
Improper Null Termination
This weakness occurs when software fails to properly end a string or array with the required null character or equivalent terminator.
Encoding Error
This vulnerability occurs when software incorrectly transforms data between different formats, leading to corrupted or misinterpreted…
Collapse of Data into Unsafe Value
This vulnerability occurs when an application's data filtering or transformation process incorrectly merges or simplifies information,…
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…
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,…
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…
Further reading
- MITRE — CWE-1426 oficial https://cwe.mitre.org/data/definitions/1426.html
- LLM02: Insecure Output Handling https://genai.owasp.org/llmrisk/llm02-insecure-output-handling/
- Validating Outputs https://cohere.com/blog/validating-llm-outputs
- NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails https://aclanthology.org/2023.emnlp-demo.40/
- Insecure output handling in LLMs https://learn.snyk.io/lesson/insecure-input-handling/
- Building Guardrails for Large Language Models https://arxiv.org/pdf/2402.01822
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