AI security tools: categories and how to evaluate them

TL;DR:

• This is a curated index of AI security tools, organised by attack surface, with a reproducible bench you can run to compare them.
• The three categories that matter most are MCP server scanners, AI coding-assistant security posture, and agent runtime hardeners.
• Capabilities described here come from vendor documentation and primary standards, not certified benchmark numbers. Run the bench yourself to get figures for your environment.
• Pair scanners with process controls from the AI agent hardening checklist and the OWASP LLM Top 10 risk guide.

What is this hub for?

This hub is a curated index of AI security tools, organised by the attack surface each one defends. Each linked page explains what a tool is designed to catch, how hard it is to install, and where its limits are. The framing is synthesis of vendor docs and primary standards, not aggregated vendor marketing. Our remit is defensive security for AI agents, Model Context Protocol (MCP) servers, and AI coding assistants such as Cursor, GitHub Copilot, and Claude Code.

For the broader risk model behind these tools, read our sibling hub on the OWASP LLM Top 10 risk guide and the process-focused security guides hub. For deployment-time controls, see the security checklists hub.

What categories of AI security tooling are there?

There are three categories: MCP server scanners, AI coding-assistant guardrails, and agent runtime hardeners. Each category maps to a distinct attack surface. MCP scanners target tool-description poisoning, coding-assistant controls target secret leakage and prompt injection, and runtime hardeners target the confused-deputy problem in agent permissions.

Category	What it defends	What to evaluate it for	Deep dive
MCP server scanners	Third-party MCP tool definitions	Poisoned descriptions, hidden instructions, over-broad scopes	MCP security scanners compared
AI coding-assistant posture	Cursor, Copilot, Claude Code	Sandboxing, secret handling, prompt-injection exposure, audit logging	Cursor vs Copilot vs Claude Code security
Agent runtime hardeners	Autonomous agent loops	Permission scoping, egress control, confused-deputy escalation	AI agent hardening checklist

How should you evaluate an AI security tool?

Run every tool against the same bench of known-bad fixtures, then record true positives, misses, and install friction. A good evaluation bench should include a poisoned MCP tool description, a hard-coded secret placed in agent context, and a confused-deputy permission escalation. A tool only earns a recommendation when it catches a finding a human reviewer would also flag.

This method follows the threat categories in the OWASP GenAI Security Project and aligns control framing with public guidance from the NSA Cybersecurity Information Sheets. Read the official vendor docs first: the Cursor security documentation, the GitHub Copilot documentation, and the Claude Code documentation.

Why methodology comes before scores

A score without a reproducible method is a vendor claim in disguise. Publish the fixture, the command, and the result so anyone can rerun it. Treat any specific number as an example to reproduce, not an industry benchmark to trust. This keeps an index honest and lets readers audit its conclusions.

How is this hub structured?

This hub links down to tool category pages, across to sibling hubs, and up to the site root. The structure follows a simple rule: tools live here, risks live in the OWASP hub, and process controls live in the guides and checklists hubs.

1. Start at the site home page for orientation.
2. Read the MCP security scanners compared review if you run third-party MCP servers.
3. Read the Cursor vs Copilot vs Claude Code security comparison if you choose an AI coding assistant.
4. Apply controls from the MCP security best practices guide and the AI agent hardening checklist.
5. Map residual risk against the OWASP LLM Top 10 risk guide.

Who maintains this index?

This index is maintained from hands-on use of the assistants and agents discussed, plus synthesis of vendor docs and primary standards. We do not accept paid placement in the verdict tables. When a tool changes behaviour between releases, re-evaluate it against the bench and update the page. That re-evaluation discipline is the reason this index exists: AI tooling moves fast, and a stale review is a security liability.

For the underlying standards we lean on, the OWASP GenAI Security Project remains the primary reference for LLM and agent threats, and we cross-check runtime controls against the Claude Code documentation and the Cursor security documentation.