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agent-credit Security Audit Report

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agent-credit is an AI agent skill, created by aaronjmars and published at aaronjmars/agent-credit. ClawSecure audited agent-credit across 20 files through the 3-Layer Audit Protocol covering all ten OWASP ASI Top 10 categories, assigning a security score of 75/100 (Medium Risk). The 4 findings concentrate in Command Injection, Code Injection and Permissions Manifest, including Pipeline downloads data from the network and executes it:… and Potentially dangerous code pattern detected: curl.*\|.*sh. 2 were rated high or critical severity.

Is agent-credit safe?

ClawSecure audited agent-credit and assigned a security score of 75/100 (Medium Risk), identifying 4 findings across Command Injection and Code Injection. Review the findings below before installing.

What did ClawSecure find in agent-credit?

ClawSecure identified 4 findings in agent-credit, concentrated in Command Injection, Code Injection and Permissions Manifest. 2 were rated high or critical severity. The most severe include Pipeline downloads data from the network and executes it:… and Potentially dangerous code pattern detected: curl.*\|.*sh.

How was agent-credit audited?

ClawSecure ran agent-credit through its 3-Layer Audit Protocol with full OWASP ASI Top 10 coverage, scanning 20 files from aaronjmars/agent-credit.

What does a score of 75 mean?

ClawSecure assigned agent-credit a security score of 75/100, placing it in the Medium Risk range. This reflects 4 findings led by Command Injection that warrant review before production use. 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-credit

ClawSecure detected 4 security findings in agent-credit, spanning Command Injection, Code Injection, Permissions Manifest and Policy Violation.

Each finding is expandable in the interactive list below.

3-Layer Audit Protocol

Security Recommendations for agent-credit

Harden command execution
agent-credit constructs or runs system commands. Validate that commands are built only from trusted inputs, never pass user-controlled strings directly to a shell, and restrict execution to an allow-list of expected commands.
Eliminate dynamic code execution
agent-credit 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.
Resolve policy violations
agent-credit trips ClawSecure policy checks. Review each flagged pattern against your security policy and remediate or document an accepted exception before production use.

Related Security Research

Why Generic Scanners Fail at AI Agent SecurityUnderstanding Our 3-Layer Audit ProtocolBeyond Static Scans: Why ClawSecure Verifies Agentic Intent

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Scanned on July 21, 2026. agent-credit 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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