LLM Plug-ins and Tool Use Safety

Build and operate LLM plug-ins and tool use safely with strict schemas, auth, policy guardrails, and evaluation.

Plug-ins/tools expand LLM capability but introduce risk. Enforce schemas, permissions, rate limits, and monitoring to prevent misuse, injection, or harmful actions.

Auditability: log prompts, tool calls, responses, and actions.

Architecture Blueprint

Declarative tool specs with types, constraints, and auth.

live demo · related simulation● LIVE

Policy checks before/after tool calls; allow/deny lists.

Fallbacks and human-in-loop for high-risk actions.

Capture telemetry: success/fail, latency, cost, violations.

Frequently asked questions

What is the purpose of adding a policy engine for intents and parameters?

Adding a policy engine enables you to define rules that govern how LLM plug-ins interact with user inputs and outputs, ensuring consistent behavior and preventing unintended consequences.

How can logging and metrics be implemented to monitor tool usage?

Instrumenting logging and metrics allows you to track the performance of tools and identify potential issues or violations of established policies, facilitating proactive monitoring and management.

What steps should be taken to test LLM plug-ins for vulnerabilities like prompt injection?

Running red-team tests, including simulating prompt injection attacks and other misuse scenarios, is crucial before releasing a plug-in to identify and mitigate potential security risks.

What is the recommended approach for gradually deploying LLM plug-ins?

Rolling out plug-ins gradually with kill switches and traffic caps allows you to monitor performance, quickly address any issues that arise, and minimize potential impact if problems are encountered.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation