AI Agent Security: 1 Tools compared

Tools that secure AI agents and LLM applications: the guardrails, gateways and controls that sit between a model and the actions it can take. They intercept tool and MCP calls, file access and shell commands, detect prompt injection and…

1 tools|Updated September 2026

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Quick comparison

All ai agent security tools side by side, alphabetical.

ToolDeploymentPricing modelOpen sourceStandards / certs
HOL GuardCloud + Self-hostedOpen source (Apache-2.0) with optional paid cloud subscriptionYes—

HOL Guard

AI Agent Security
Best fit for

Developers and teams using AI coding agents who want local pre-execution controls on risky actions without sending data to a cloud service.

HOL Guard is an open-source, local-first runtime security layer that sits between an AI coding agent and the tools it wants to run. It evaluates shell commands, file access, package installs and MCP tool calls before execution and allows, blocks or routes them for approval according to policy, with the vendor stating that checks take under 50 ms and that files never leave the machine. It supports agents including Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot CLI, installs via pipx on Python 3.10 or later, and offers desktop installers for Apple silicon macOS 13 and later, Linux x86_64 and Windows x64, with Windows marked experimental. The core is Apache-2.0 and needs no account, while an optional Guard Cloud adds shared history, team policy and dashboards at USD 4.99 (Solo), USD 15 (Pro) and USD 30 per seat (Team) per month. It is made by HOL, operated by Hashgraph Online DAO LLC, a consortium established in November 2024 whose founding members include HashPack and Hgraph. The GitHub repository shows 570 stars and 3,705 commits, and PyPI lists version 3.0.131 released on 8 September 2026.

Pricing

Free tier at USD 0 with local scanning, on-device decisions and approvals; Guard Cloud plans at USD 4.99 per month (Solo), USD 15 per month (Pro) and USD 30 per seat per month (Team), with two months free on annual billing; Enterprise is custom pricing with SSO, SIEM integration and on-premises options.

Open source (Apache-2.0) with optional paid cloud subscription

Deployment

CloudSelf-HostedOpen Source
Visit HOL GuardView listing

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About this listing

AI Agent Security tools, listed alphabetically and compared on public information. How we work →

Frequently Asked Questions

AI agent security covers the controls that sit between a language model and the things it is allowed to do. An agent does not only answer questions: it runs commands, reads and writes files, calls tools and APIs, and holds credentials to do so. The tools in this category decide whether a given action should run, detect attempts to steer the agent through its inputs, govern the data it can reach, and test the application before release.

Model security is about the model itself: adversarial testing of classifiers, poisoning, theft of weights. AI agent security is about what happens when a model is wired to tools and given permissions. The risks are prompt injection through documents or web pages the agent reads, excessive permissions, tool calls with unintended effects, and data leaving through the agent's outputs. Some vendors cover both; this guide lists them for the agent and LLM application side.

Prompt injection is content that the model treats as an instruction when it was only meant to be data: a line hidden in a web page, an email or a file that tells the agent to do something its user did not ask for. For a chatbot the damage is a bad answer. For an agent with a shell, a browser or an API key, the same injection can exfiltrate data or take an action. That is why the tools here focus on the boundary between what the agent reads and what it is allowed to do.

Both models exist, and it is the first thing to check. Some products intercept the action before it runs and can allow it, block it, or route it to a human for approval. Others observe traffic and alert afterwards. Blocking is stronger but needs to sit in the execution path, which is easier for a coding agent on a developer's machine than for a production application at scale.

The Model Context Protocol is the common way agents are given tools, so an MCP server is now a large part of an agent's attack surface: a malicious or compromised tool description can carry an injection, and a legitimate tool can be called with harmful arguments. Several tools in this guide scan MCP servers and plugins before they are installed, or control tool calls at runtime.