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Security audit

Product Judgment

Security checks for vulnerabilities and agentic risk

Overview

This skill provides a coherent product-review workflow with local document generation and manageable privacy and recommendation-quality caveats.

Run scans in the intended project and review generated judgments before relying on or committing them. For the meeting example, obtain participant consent and use approved processing tools or redacted samples. Adopt the CLAUDE.md template only if you want its ongoing feature-development gate.

Vulnerability Patterns
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
Vulnerability Patterns
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Output HandlingUnvalidated Output Injection, Cross-Context Output, Unbounded Output
  • Behavioral ASTexec() Call, eval() Call, Dynamic Import
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
Findings (7)

Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: file_read, file_write, network, shell.

Medium
Category
MCP Least Privilege
Confidence
70% confidence
Finding

Without declared permissions the skill's intent is opaque and cannot be validated.

Content

No source excerpt is available for this finding.

The validation workflow directs use of real meeting content with transcription tools and an LLM without warning about third-party data disclosure.

Medium
Category
Output Handling
Confidence
95% confidence
Finding

The recommended experiment uses real meeting recordings with transcription tools and an LLM without requiring participant consent or approval of the processing destination. Although the example recognizes recording acceptance elsewhere, that does not authorize sharing confidential meeting content with external providers; following this workflow could expose personal or proprietary information.

Content

No source excerpt is available for this finding.

subprocess module call

Medium
Category
Dangerous Code Execution
Confidence
70% confidence
Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · scripts/demand-check.py (reported line 361)May include surrounding context.

python
pass

    try:
        log = subprocess.check_output(
            ["git", "log", "--oneline", "-10"],
            cwd=root, stderr=subprocess.DEVNULL
        ).decode().strip().split("\n")

subprocess module call

Medium
Category
Dangerous Code Execution
Confidence
70% confidence
Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · scripts/demand-check.py (reported line 367)May include surrounding context.

python
).decode().strip().split("\n")
        summary["recent_commits"] = [l for l in log if l]

        first = subprocess.check_output(
            ["git", "log", "--oneline", "--reverse", "-1", "--format=%ar"],
            cwd=root, stderr=subprocess.DEVNULL
        ).decode().strip()

The script presents rule-based decisions as final product judgments despite documenting that judgment comes from the LLM rather than if-else rules.

Medium
Category
Excessive Agency
Confidence
90% confidence
Finding

The script describes itself as a collector rather than a substantive judge, yet computes verdicts from coarse rules and publishes them as final judgments without a provisional label. Because the resulting document is intended to govern implementation decisions, users or agents could mistake an unreviewed heuristic result for an evidence-based assessment, incorrectly blocking or greenlighting work.

Content

No source excerpt is available for this finding.

Autonomous Decision Making

Medium
Category
Excessive Agency
Confidence
75% confidence
Finding

Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Content

Scanner excerpt · templates/CLAUDE.md (reported line 24)May include surrounding context.

md
- You're about to build something because a competitor has it
- A PR adds scope beyond what JUDGMENT.md describes

**DO NOT trigger the gate — proceed without checking:**
- Bug fixes (behavior unchanged)
- Performance optimizations on existing features
- Refactors that don't change user-facing behavior

Verdicts are required to be bilingual without user opt-in.

Low
Category
Output Handling
Confidence
98% confidence
Finding

The instruction requires every verdict to include both English and Chinese, regardless of the user's language preference. No opt-in or justification for this mandatory language constraint is provided.

Content

No source excerpt is available for this finding.