Agent-Reach Security Audit Report

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Agent-Reach is an AI agent skill, created by Panniantong and published at Panniantong/Agent-Reach. ClawSecure audited Agent-Reach across 61 files through the 3-Layer Audit Protocol covering all ten OWASP ASI Top 10 categories, assigning a security score of 85/100 (Safe). The 2 findings concentrate in Code Injection and Permissions Manifest, including Potentially dangerous code pattern detected: system\( and Missing config.json - agent may not be properly configured. 1 was rated high or critical severity.

Is Agent-Reach safe?

ClawSecure audited Agent-Reach and assigned a security score of 85/100 (Safe), identifying 2 findings across Code Injection and Permissions Manifest. Review the findings below before installing.

What did ClawSecure find in Agent-Reach?

ClawSecure identified 2 findings in Agent-Reach, concentrated in Code Injection and Permissions Manifest. 1 was rated high or critical severity. The most severe include Potentially dangerous code pattern detected: system\( and Missing config.json - agent may not be properly configured.

How was Agent-Reach audited?

ClawSecure ran Agent-Reach through its 3-Layer Audit Protocol with full OWASP ASI Top 10 coverage, scanning 61 files from Panniantong/Agent-Reach.

What does a score of 85 mean?

ClawSecure assigned Agent-Reach a security score of 85/100, placing it in the Safe range. Scores of 80 or above qualify for ClawSecure Verified status, though 1 finding was rated high or critical and warrants review. ClawSecure derives this score with a weighted deduction model (critical -20, high -10, medium -5, low -2 from a base of 100).

Audit Findings for Agent-Reach

ClawSecure detected 2 security findings in Agent-Reach, spanning Code Injection and Permissions Manifest.

high · Potentially dangerous code pattern detected: system\(. Code Injection finding detected in cli.py.
medium · Missing config.json - agent may not be properly configured. Permissions Manifest finding.

Each finding is expandable in the interactive list below.

3-Layer Audit Protocol

Security Recommendations for Agent-Reach

Eliminate dynamic code execution

Agent-Reach evaluates code at runtime (for example eval or dynamic exec). Remove dynamic evaluation of untrusted input, and where code generation is unavoidable, sandbox it and validate every input.

Add a config.json permissions manifest

A config.json file declares what an agent component can access: file system, network, shell execution and more. Without it, users have no visibility into what the component can do before installing. This is the single most impactful security improvement for any AI agent skill.

Pin dependencies to exact versions

Unpinned dependencies allow supply-chain attacks where a compromised version is pulled in automatically. Use exact version numbers in package.json (for example 1.2.3 instead of ^1.2.3) to keep unauthorized code out of your dependency tree. ClawSecure checks every dependency against known CVE databases.

Related Security Research

Why Generic Scanners Fail at AI Agent Security→Beyond Static Scans: Why ClawSecure Verifies Agentic Intent→How to Secure an MCP Server: The 2026 Guide→

Related AI Agent Security Audits

agent-creditScore 75/100 hermes-agentScore 85/100 Self-Improving Agent (With Self-Reflection)Score 93/100 Agent-ReachScore 75/100 wake-upScore 95/100

Scanned on August 6, 2026. Agent-Reach is one of thousands of agents audited by ClawSecure from the community-curated awesome-openclaw-skills list and the openclaw/skills repository.

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