Independent evaluation guide | 11 synced entries

How to evaluate AI agent and MCP security tools

Secure agent tools, memory, identity, permissions, sandboxes, and MCP servers. Compare selection criteria here, then verify every claim against the linked first-party source.

Decision context

What does agent security tooling need to cover?

AI agent security focuses on systems that can choose actions, call tools, retain memory, and interact with external services. These capabilities turn prompt injection and untrusted context into authorization and execution risks. Model Context Protocol servers add another integration boundary involving tool descriptions, credentials, transport, and user approval. Tools in this category inspect agent workflows, scan MCP or skill definitions, protect memory, test excessive agency, and isolate runtime actions.

README-synced directory

AI Agent and MCP Security catalog entries

Each listing preserves the neutral description maintained in the Awesome MLSecOps README and links to the resource's first-party source.

11 entries
github.com

HOL Guard

Open-source local-first runtime security layer for AI agents that evaluates supported tool actions and local artifacts for prompt injection, secret exposure, unsafe commands, package risks, and MCP threats

github.com

Humanbound

Open-source CLI that sends LLM-generated adversarial attacks to an agent's HTTP endpoint and scores results against OWASP LLM Top 10, OWASP Agentic Top 10, NIST, and EU AI Act mappings

github.com

Prismor

Self-hosted runtime control plane that screens AI coding-agent tool calls against a signed policy before execution, blocking or routing to human approval secret exfiltration, prompt-injection-driven actions, and destructive commands across Claude Code, Codex, and MCP servers

github.com

SourceryKit

Verifies an AI agent's outbound requests and MCP handoffs against a source of truth using zero-knowledge proofs, so a call only goes out if its claims check out; hooks the HTTP libraries, logs each outbound call, and blocks endpoints not on the trusted allow-list

trent.ai

Trent AI

Agentic AI security platform that continuously assesses AI agents, MCP servers, LLM and AI-native applications, and code shipped with AI coding tools, traces attack chains, and verifies proposed fixes landed

Selection framework

How should teams evaluate the options?

Assess agent-security tooling against the permissions and data paths in your architecture. Look for explicit identities, least-privilege scopes, per-tool policy, credential isolation, network controls, and durable audit records. Define which actions require human approval, such as financial transactions, data mutation, privilege changes, or external communications, and make unavailable approval or policy services fail closed. A scanner should cover indirect prompt injection and malicious tool metadata, not only user prompts. Test the complete action path because a safe model response can still trigger an unsafe downstream tool call.

  1. 01Tool-level authorization
  2. 02Memory and context controls
  3. 03Sandbox boundaries
  4. 04Audit and approval workflows

Record evidence and limitations for each criterion. A catalog listing is a discovery aid, not a security certification.

Primary references

Related threats and technical guidance