LLM agent tool exposure audit

An LLM agent tool exposure audit examines the set of tools, endpoints, and utilities an AI assistant can invoke through your application. The focus is on whether tools such as webhooks, callbacks, code execution, database connectors, or external API calls are overly permissive, improperly authenticated, or capable of data exfiltration. Because LLM agents can chain tool calls across multiple services, the audit maps each tool to the minimum required scope and validates that no unintended internal or external surfaces are reachable.

Without an audit, LLM agents can inadvertently or deliberately invoke tools that expose sensitive operations. Risks include webhook callbacks to attacker-controlled endpoints, parameter tampering in tool schemas, excessive permissions that enable lateral movement across services, and indirect prompt injection that triggers unintended tool calls. These patterns can lead to data exposure, cost abuse, or unauthorized actions that are difficult to detect with runtime-only monitoring.

Start by inventorying every tool your agents can invoke, including generated endpoints, webhooks, and background jobs. Classify each tool by required authentication, data sensitivity, and blast radius. Implement schema validation and strict input constraints so that tool parameters cannot be trivially abused. Use a scanner to surface unexpected endpoints or dangerous configurations, then verify findings manually with targeted probes that respect rate limits and avoid production impact.

• Catalog all tool definitions and entrypoints used by LLM agents.
• Define per-tool authentication and authorization boundaries.
• Validate input schemas and enforce strict type/length constraints.
• Run an automated scan focused on tool-exposure patterns.
• Remediate findings and re-scan to confirm reductions in exposure.

For API-facing tools, an OpenAPI-based analysis can highlight undefined security schemes, deprecated operations, and over-exposed fields that increase risk.

middleBrick scans the public surface of your API endpoints to detect indicators of tool exposure without requiring access to your code or runtime. It parses OpenAPI specifications and compares them against runtime behavior to identify undefined security schemes, unversioned paths, and endpoints that return sensitive data or API key patterns. The LLM security scan tier runs adversarial probes across three depth levels to test for system prompt extraction, instruction override, jailbreak attempts, data exfiltration patterns, token smuggling, and tool-abuse scenarios.

The scanner identifies risky headers, CORS misconfigurations, dangerous HTTP methods, and verbose error messages that aid attackers. It flags endpoints that accept URLs or body fields capable of SSRF-like redirection toward internal infrastructure and detects mass-assignment surfaces that could be leveraged for property authorization issues. Findings are mapped to OWASP API Top 10, helping you align security evidence with recognized frameworks.

middleBrick does not fix, patch, or block identified issues; it reports findings with remediation guidance. It does not perform active injection tests that require intrusive payloads, nor does it detect blind SSRF that relies on out-of-band channels. Business logic vulnerabilities surrounding tool workflows require domain expertise and manual review. After scanning, use the provided guidance to tighten authentication, narrow tool permissions, and validate input schemas, then re-run scans to confirm improvements.