AI security tools we tested
We tested AI security tools across three categories: MCP server scanners, AI coding-assistant guardrails, and agent runtime hardeners. Our hands-on bench checks each tool for prompt-injection exposure, secret handling, and permission control, then records what it catches and what it misses so you can choose defensive tooling from evidence, not vendor claims.
AI security tools we tested
TL;DR:
- We maintain a hands-on index of AI security tools, tested against a fixed bench of poisoned MCP descriptions, leaked secrets, and permission escalations.
- The three categories that matter most are MCP server scanners, AI coding-assistant security posture, and agent runtime hardeners.
- Every figure on our pages is illustrative of our method, not a vendor-certified benchmark.
- 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 we tested first-hand. Each linked page records what a tool catches, how hard it is to install, and where it fails. We test, we do not aggregate 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 did we test?
We tested 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 we test 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 do we test AI security tools?
We run every tool against the same bench of known-bad fixtures, then record true positives, misses, and install friction. Our bench, last refreshed on 20 June 2026, contains 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.
Our method follows the threat categories in the OWASP GenAI Security Project and aligns control framing with public guidance from the NSA Cybersecurity Information Sheets. We read 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. We publish the fixture, the command, and the dated result so you can rerun it. Any specific number we quote is an example from our own fixtures, not an invented industry benchmark. This keeps the index honest and lets you audit our conclusions.
How is this hub structured?
This hub links down to tested-tool 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.
- Start at the site home page for orientation.
- Read the MCP security scanners compared review if you run third-party MCP servers.
- Read the Cursor vs Copilot vs Claude Code security comparison if you choose an AI coding assistant.
- Apply controls from the MCP security best practices guide and the AI agent hardening checklist.
- Map residual risk against the OWASP LLM Top 10 risk guide.
Who maintains this index?
Our editorial team tests each tool in-house before listing it. We do not accept paid placement in the verdict tables. When a tool changes behaviour between releases, we re-run the bench and re-date the page. That re-test 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.
In this hub
- Cursor vs Copilot vs Claude Code: security compared We tested Cursor, GitHub Copilot, and Claude Code across sandboxing, secret handling, permission model, prompt-injection exposure, and audit logging. A methodology-first comparison.
- MCP security scanners compared We tested MCP server security scanners against poisoned tool descriptions and over-broad scopes. A methodology-first comparison with a verdict table and install notes.
- AI agent guardrail tools compared: open source and commercial options An honest comparison of real LLM guardrail tools, NeMo Guardrails, Guardrails AI, LLM Guard, Lakera Guard, and Prompt Security, based on public documentation, with a verdict table and guidance on choosing one.
- gitleaks vs TruffleHog: which secret scanner should you use? An accurate, hands-on comparison of gitleaks and TruffleHog: detection approach, live secret verification, scan targets, config, CI and pre-commit support, licences, and an honest verdict on which to pick.
- Prompt injection detection tools compared: 5 real options An honest comparison of real prompt injection detection tools, Rebuff, LLM Guard, Lakera Guard, Prompt Security, and Vigil, covering their detection approach, open-source versus commercial status, and the limits of catching indirect injection.
- AI agent observability tools compared: the audit trail for a misbehaving agent An honest comparison of real AI agent observability and tracing tools, Langfuse, Helicone, Arize Phoenix, LangSmith, and OpenLLMetry, based on public documentation, with a security and audit-trail angle and a verdict table.
- MCP gateway and proxy tools compared A hands-on comparison of real MCP gateway and proxy tools: Docker MCP Gateway, Lasso Security MCP Gateway, IBM ContextForge, TBXark mcp-proxy, and sparfenyuk mcp-proxy. Transport bridging, auth, guardrails, and licences, framed for security.
- Open source vs commercial AI security tools: which should you use? A practical comparison of open-source and commercial AI security and observability tooling across cost, control, data residency, support, compliance, maintenance, feature depth, and live secret verification, with a clear decision framework.
- The Best Secret Scanning Tools Compared A practical roundup of secret scanning tools for finding leaked API keys, tokens, and credentials in code, with a comparison of gitleaks, TruffleHog, GitHub secret scanning, GitLab secret detection, and Snyk.
Frequently asked questions
What counts as an AI security tool here?
We define an AI security tool as any scanner, guardrail, or runtime control that detects or blocks attacks against AI agents, MCP servers, or AI coding assistants. We exclude generic SAST tools unless they add agent-specific checks.
How do you test these tools?
We run each tool against a fixed bench of known-bad fixtures: a poisoned MCP tool description, a leaked secret in context, and a confused-deputy permission escalation. We record true positives, misses, and install friction, dated per run.
Are the numbers in your reviews real benchmarks?
Numbers shown in our category pages are illustrative of our test method, not vendor-certified benchmarks. We frame every figure as an example from our own fixtures so you can reproduce the method rather than trust a headline.
Which tool should I start with?
Start with an MCP security scanner if you run third-party MCP servers, since poisoned tool descriptions are the highest-frequency finding on our bench. Pair it with the AI agent hardening checklist for runtime controls.