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llm_interview_note Security Audit Report

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llm_interview_note is an AI agent skill, created by wdndev and published at wdndev/llm_interview_note. ClawSecure audited llm_interview_note across 58 files through the 3-Layer Audit Protocol covering all ten OWASP ASI Top 10 categories, assigning a security score of 65/100 (Medium Risk). The 5 findings concentrate in Code Injection, ReDoS and Permissions Manifest, including Potentially dangerous code pattern detected: eval\( and Potentially dangerous code pattern detected: eval\(. 2 were rated high or critical severity.

Is llm_interview_note safe?

ClawSecure audited llm_interview_note and assigned a security score of 65/100 (Medium Risk), identifying 5 findings across Code Injection and ReDoS. Review the findings below before installing.

What did ClawSecure find in llm_interview_note?

ClawSecure identified 5 findings in llm_interview_note, concentrated in Code Injection, ReDoS and Permissions Manifest. 2 were rated high or critical severity. The most severe include Potentially dangerous code pattern detected: eval\( and Potentially dangerous code pattern detected: eval\(.

How was llm_interview_note audited?

ClawSecure ran llm_interview_note through its 3-Layer Audit Protocol with full OWASP ASI Top 10 coverage, scanning 58 files from wdndev/llm_interview_note.

What does a score of 65 mean?

ClawSecure assigned llm_interview_note a security score of 65/100, placing it in the Medium Risk range. This reflects 5 findings led by Code 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 llm_interview_note

ClawSecure detected 5 security findings in llm_interview_note, spanning Code Injection, ReDoS and Permissions Manifest.

Each finding is expandable in the interactive list below.

3-Layer Audit Protocol

Security Recommendations for llm_interview_note

Eliminate dynamic code execution
llm_interview_note 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.
Fix ReDoS-prone patterns
llm_interview_note contains regular expressions vulnerable to catastrophic backtracking (ReDoS). Replace vulnerable patterns, bound input length, and prefer linear-time matching so a crafted input cannot hang the agent.
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→Understanding Our 3-Layer Audit Protocol→

Related AI Agent Security Audits

GitNexusScore 75/100antigravity-awesome-skillsScore 75/100openclawScore 55/100ui-ux-pro-max-skillScore 75/100skill-scannerScore 65/100

Scanned on June 22, 2026. llm_interview_note 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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