Input and Output Validation for AI Agent Components - AI Mitigation
AI MitigationImplement validation on inputs and outputs for the tools and data sources used by AI agents. Validation includes enforcing a common data format, schema validation, checks for sensitive or prohibited information leakage, and data sanitization to remove potential injections or unsafe code. Input and output validation can help prevent compromises from spreading in AI-enabled systems and can help secure the workflow w...
Overview
A source-backed snapshot of this defense.
Implement validation on inputs and outputs for the tools and data sources used by AI agents. Validation includes enforcing a common data format, schema validation, checks for sensitive or prohibited information leakage, and data sanitization to remove potential injections or unsafe code. Input and output validation can help prevent compromises from spreading in AI-enabled systems and can help secure the workflow when multiple components are chained together. Validation should be performed external to the AI agent.
Safeguard details
Where this defense applies and how the source classifies it.
- ATLAS ID
- AML.M0033
- Priority score
- 30
Covered techniques
Attacks this defense is designed to help with.
AML.T0053 - AI Agent Tool Invocation
Validation can prevent adversaries from utilizing tools in an agentic workflow to generate unsafe output.
AML.T0051.000 - Direct
Validation can prevent adversaries from executing prompt injections that could affect agentic workflows.
AML.T0086 - Exfiltration via AI Agent Tool Invocation
Validation can prevent adversaries from utilizing tools in an agentic workflow to compromise sensitive data sources.
AML.T0051.001 - Indirect
Validation can prevent adversaries from executing prompt injections that could affect agentic workflows.
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Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.