security-review-owasp-llm-prompt-injection-prevention

Review LLM workflows for prompt injection and tool abuse risks by validating untrusted content handling, tool constraints, context isolation, output validation, and monitoring across agent, retrieval, and plugin execution paths.

Context isolation prevents LLM agent security issues by separating untrusted content from trusted system prompts, ensuring injected instructions cannot override guardrails or trigger unauthorized tool execution across trust boundaries.

Secure RAG pipelines and copilots against context leakage by applying evidence-based validation of retrieval paths, output consumers, least-privilege execution, human approval gates, and abuse containment monitoring.

Yes, you can use this review for plugins, copilots, agents, and RAG pipelines, checking trust-boundary issues, unsafe tool use, memory handling, and weak guardrails in any workflow where model output triggers actions.

Automated LLM security reviews for agent abuse require evidence-based validation of untrusted content handling and tool constraints, but cannot fully replace human oversight of operational controls, logging, and human approval gates.

Check trust boundaries in LLM systems whenever model output can trigger actions, requiring validation of untrusted content handling, tool constraints, context isolation, output validation, and monitoring for abuse in code and architecture.