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Security audit

Rohoon Six Sigma

Security checks for vulnerabilities and agentic risk

Overview

This skill is a coherent local Six Sigma analysis and report-generation toolkit, with some operational cautions but no artifact-backed malicious behavior.

Install only in an environment where you are comfortable running local Python report-generation scripts. Prefer pinning or locking dependencies before production use, and avoid running the batch PDF script on folders that may contain untrusted PDFs unless you remove or disable its automatic open step.

SkillSpector

Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Behavioral ASTexec() Call, eval() Call, Dynamic Import
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Findings (9)

subprocess module call

Medium
Category
Dangerous Code Execution
Content
for output_dir in [output_dir_en, output_dir_cn]:
    for f in sorted(os.listdir(output_dir)):
        if f.endswith('.pdf'):
            subprocess.run(['open', os.path.join(output_dir, f)], check=False)

print("完成！")
Confidence
88% confidence
Finding
The script launches every discovered PDF using the local OS 'open' handler via subprocess. Although it avoids shell injection by passing an argument list, it still executes an external program on files found in a directory, which can trigger unsafe file handling, unexpected application launches, or exploitation of vulnerable PDF viewers if an attacker can place a crafted file in that output directory.

Context-Inappropriate Capability

Medium
Confidence
90% confidence
Finding
Automatically opening generated files is beyond the minimum capability needed for report generation and increases the attack surface. In this context, the danger is higher because the code enumerates all PDFs in the output folders, so any malicious PDF already present there would also be opened, not just files produced by this execution.

Unpinned Dependencies

Low
Category
Supply Chain
Content
numpy>=1.23.0
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0
Confidence
96% confidence
Finding
The dependency is specified with a lower bound only, which allows future unreviewed versions to be installed. This weakens reproducibility and can introduce supply-chain risk if a later release contains a security regression or breaking behavior.

Unpinned Dependencies

Low
Category
Supply Chain
Content
numpy>=1.23.0
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0
matplotlib>=3.7.0
Confidence
96% confidence
Finding
The scipy requirement is unpinned and permits any newer version above the minimum. That creates a supply-chain exposure window because deployments may silently pick up untested or vulnerable releases.

Unpinned Dependencies

Low
Category
Supply Chain
Content
numpy>=1.23.0
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0
matplotlib>=3.7.0
pandas>=2.0.0
Confidence
97% confidence
Finding
The reportlab dependency is unpinned, so environments may install different upstream releases over time. Because reportlab has had severe historical issues including RCE-class flaws, leaving it unpinned increases the chance of pulling an unsafe version.

Unpinned Dependencies

Low
Category
Supply Chain
Content
numpy>=1.23.0
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0
matplotlib>=3.7.0
pandas>=2.0.0
Confidence
95% confidence
Finding
Using only a minimum version for openpyxl makes builds non-reproducible and can introduce vulnerable or incompatible upstream releases without review. This is a common supply-chain hygiene issue rather than direct malicious behavior.

Unpinned Dependencies

Low
Category
Supply Chain
Content
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0
matplotlib>=3.7.0
pandas>=2.0.0
Confidence
95% confidence
Finding
The matplotlib package is not version-pinned, which permits uncontrolled upgrades. While not a direct exploit by itself, it increases exposure to future vulnerable releases and reduces reproducibility.

Unpinned Dependencies

Low
Category
Supply Chain
Content
reportlab>=4.0.0
openpyxl>=3.1.0
matplotlib>=3.7.0
pandas>=2.0.0
Confidence
95% confidence
Finding
The pandas dependency is expressed as a lower bound only, allowing any later release to be selected at install time. This can unexpectedly introduce security issues or behavior changes into deployments.

Known Vulnerable Dependency: reportlab — 6 advisory(ies): CVE-2023-33733 (Reportlab vulnerable to remote code execution); CVE-2020-28463 (Server-side Request Forgery (SSRF) via img tags in reportlab); CVE-2019-19450 (ReportLab vulnerable to remote code execution via paraparser) +3 more

Critical
Category
Supply Chain
Confidence
83% confidence
Finding
The requirement reportlab>=4.0.0 is concerning because reportlab has a history of severe issues, including RCE and SSRF, and the range is unconstrained above the minimum. Even if not every 4.x release is affected, this file does not pin to a known-safe version, so the skill could resolve to a vulnerable build depending on environment and mirror state.

Static analysis

No suspicious patterns detected.