agent-security

Scan for secrets and audit environment permissions for autonomous AI agents.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/0xAxiom/axiom-public --skill agent-security
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-security
Source: https://github.com/0xAxiom/axiom-public/tree/main/agent-tools/skills/agent-security
Command: npx skills add https://github.com/0xAxiom/axiom-public --skill agent-security

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides security guardrails, self-audit tooling, and secret scanning to protect autonomous AI agents from credential leakage, unauthorized actions, and data exfiltration risks.

Core Features & Use Cases

  • Guardrails & rules: Non-negotiable policies that prevent leaking secrets, executing untrusted code, or acting on untrusted prompts.
  • Self-audit tools: Built-in checks that verify environment permissions, credential handling, and transaction safety.
  • Secret scanning: Detects credentials and sensitive data in workspaces and git history, with remediation guidance.

Quick Start

Copy this skill into the agent's skills directory and run the audit and secret scan:

  • node skills/agent-security/scripts/security-audit.mjs
  • node skills/agent-security/scripts/secret-scanner.mjs [directory]

Frequently Asked Questions about agent-security

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent AI agents from leaking credentials and secrets?

Credential leakage in autonomous agents is prevented through guardrails that enforce non-disclosure policies, secret scanning that detects exposed credentials in workspaces and git history, and environment-based safety checks that block unauthorized data exfiltration before it occurs.

What security guardrails do autonomous AI agents need?

Security guardrails for AI agents enforce non-negotiable policies: prevent credential leakage, block untrusted code execution, reject prompts from untrusted sources, and require explicit human approval for sensitive actions, protecting against unauthorized behavior and data risks.

How do I audit an AI agent for security vulnerabilities?

Self-audit tools verify environment permissions, credential handling practices, and transaction safety by running built-in checks that report on guardrail compliance, secret exposure, and permission misconfigurations across the agent's workspace.

Can I scan for exposed credentials in my agent's codebase and history?

Secret scanning detects credentials and sensitive data in current workspaces and git history, then provides remediation guidance to remove or rotate exposed secrets and prevent future leakage.

What AI agent frameworks does this security approach work with?

These guardrails and audit tools apply to Moltbot and OpenClaw agents, and generalize to any autonomous AI agent requiring hardening, credential protection, and security auditing.