LLM Output Validation for AI Agents
LLM output validation means checking and constraining AI model responses before they are used to drive agent actions. Without validation, hallucinated function calls, malformed JSON, or prompt-injected commands from the LLM can trigger unintended tool executions, database modifications, or security breaches.
Unvalidated Tool Call
# LLM output directly drives tool execution
tool_name = llm_response.tool_call.name
tool_args = llm_response.tool_call.arguments
# No validation - LLM could hallucinate any tool!
result = execute_tool(tool_name, tool_args)ALLOWED_TOOLS = {"search", "calculator", "weather"}
tool_name = llm_response.tool_call.name
if tool_name not in ALLOWED_TOOLS:
raise ValueError(f"Unauthorized tool: {tool_name}")
tool_args = validate_args(tool_name, llm_response.tool_call.arguments)
result = execute_tool(tool_name, tool_args)Frequently Asked Questions
Why is LLM output validation important for AI agents?
LLM output drives agent actions — tool calls, database queries, API requests. If the output contains hallucinated commands, malformed data, or injected instructions, the agent will execute them. Validation ensures only well-formed, authorized actions are taken.
What should you validate in LLM output?
Validate: (1) Tool call names against an allowlist, (2) Parameter types and ranges, (3) SQL queries against a safe pattern list, (4) URLs before fetching, (5) File paths before reading/writing, (6) Any output used in code execution.
How does Inkog detect missing output validation?
Inkog traces data flow from LLM call outputs to tool invocations, database queries, and external API calls. Paths without intermediate validation functions are flagged as insecure output handling.
How Inkog Detects This
Inkog identifies LLMCallNode outputs that flow directly to ToolCallNodes or DataFlowNodes without intermediate validation. It checks for missing allowlist checks on tool names, unvalidated parameters, and unsanitized data reaching sensitive sinks.
npx -y @inkog-io/cli scan .