Best LLM guardrails and security testing tools (2026)

LLM security requires controls across five layers: input filtering, output moderation, schema validation, tool permissions, and evaluation and monitoring, because failures can occur from untrusted prompts through downstream agent actions. Lakera Guard focuses primarily on screening inputs for prompt injections, jailbreaks, sensitive data, and other threats; Guardrails AI combines output moderation with structured-output validation; NVIDIA NeMo Guardrails governs agent behavior and tool calls; and Braintrust evaluates and monitors the effectiveness of these controls before release and in production. Each tool has limits, requiring organizations to combine runtime protections with application authentication, permissions, policies, and operational processes. Evaluation is presented as essential for measuring missed attacks and false positives, detecting regressions caused by changing models or prompts, and turning production failures into future test cases. Recommended stacks depend on application risk, with chat features needing input and output screening, structured-output systems adding schema validation, tool-using agents requiring execution controls, and regulated or high-impact applications mapping protections across all five layers.

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