CWE-1426: Improper Validation of Generative AI Output | Glexia
CWE-1426 (Improper Validation of Generative AI Output) weakness overview with consequences, detection methods, mitigations, related CVEs and MITRE ATT&CK context.
Glexia's Take · Automated analysis
CWE-1426: Improper Validation of Generative AI Output
Improper Validation of Generative AI Output represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Executive Impact
- Integrity: Execute Unauthorized Code or Commands,Varies by Context:
Developer Pattern
CWE-1426 is the kind of defect developers can usually prevent with explicit validation, safer framework defaults, and tests that exercise hostile input or unsafe state transitions.
Automation confidence
high confidence from CWE-1426, 4.20.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Official CWE Definition
CWE-1426: Improper Validation of Generative AI Output
The product invokes a generative AI/ML component whose behaviors and outputs cannot be directly controlled, but the product does not validate or insufficiently validates the outputs to ensure that they align with the intended security, content, or privacy policy.
Developer And Remediation Guidance
How teams prevent and detect this weakness
Causes
- Missing validation
- Unsafe defaults
- Insufficient authorization or memory-safety invariant
Remediation
- 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:
- Build and Compilation:
Detection
- 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.
Mappings
Related CVEs, CWEs, and ATT&CK context
Related CWEs
ATT&CK Relevance
ATT&CK relevance is shown only when reviewed or responsibly inferred.