What guardrails and input/output validation controls cover the full OWASP LLM Top 10 for an AI agent?

To cover the OWASP LLM Top 10 for an AI agent, guardrails and input/output validation controls should focus on treating model output as untrusted, validating all inputs, and implementing strict access and resource management. These controls help mitigate risks from prompt injection to misinformation and resource exhaustion.

• OWASP LLM01 Prompt Injection: Implement input/instruction separation, establish trust boundaries on retrieved/tool content, assume model output can be steered, use least-privilege tool access, and include human-in-the-loop for high-impact actions. Adversarial testing is also a key control.
• OWASP LLM02 Sensitive Information Disclosure: Employ input/output scrubbing and redaction, minimize data in context, enforce strict RAG-source scoping, ensure tenant isolation, apply Data Loss Prevention (DLP) on responses, and avoid placing secrets in prompts. This cross-maps to NIST GenAI "Data privacy".
• OWASP LLM03 Supply Chain: Maintain model/dataset provenance and licensing records, use signed artifacts, create a Software Bill of Materials (SBOM) for the AI stack, vet plugins/MCP tools, and pin versions. This cross-maps to NIST-GOVERN-6.1.
• OWASP LLM04 Data and Model Poisoning: Implement data-source vetting and integrity checks, use anomaly detection on training data, curate and own the RAG corpus, and track provenance.
• OWASP LLM05 Improper Output Handling: Treat all model output as untrusted, encode/sanitize output before rendering, use parameterized queries, schema-validate tool arguments, and never eval model text. Schema validation on every tool call is a highly effective check.
• OWASP LLM06 Excessive Agency: Utilize least-privilege tools, allow-list and narrowly scope tool schemas, require human approval for high-impact/irreversible actions, set spend/refund thresholds, use scoped credentials, and implement rate limits. This cross-maps to NIST-GOVERN-3.2.
• OWASP LLM07 System Prompt Leakage: Avoid embedding secrets, access rules, or sensitive logic in prompts; enforce controls in code/infrastructure, not prompt text; and design plugins/tools to be safe even if the prompt is fully known.
• OWASP LLM08 Vector and Embedding Weaknesses: Implement access-controlled retrieval, per-tenant/source partitioning, sanitize ingested content, and validate retrieval relevance.
• OWASP LLM09 Misinformation: Require grounding/citation for outputs, refuse to answer on weak evidence, incorporate human oversight for consequential decisions, and provide clear AI disclaimers.
• OWASP LLM10 Unbounded Consumption: Apply rate limits and quotas, set token/spend caps, implement abuse detection, and enforce access controls on model endpoints and weights. Per-task and per-agent budgets, circuit breakers, and timeout enforcement are also important.