SQL Dataviz

• Outcome: Pass
• Latest audit: May 29, 2026 · 8:31 PM UTC
• Version: 1.0.1

Overview

The skill appears to do what it claims: install charting libraries and generate charts, with ordinary dependency and CDN risks to review.

Install it in a virtual environment if possible, review or pin dependencies for production use, and avoid CDN-backed generated HTML when charts contain sensitive data or must work offline; use local Chart.js/Plotly assets in those cases.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (4)

Missing User Warnings

Low

• Confidence: 89% confidence
• Finding: The document instructs loading Chart.js from a public CDN, which causes an external network request and leaks metadata such as client IP, user agent, and access timing to a third party. In a tool/agent context, failing to disclose this behavior can violate privacy expectations and may break in restricted or air-gapped environments.

Known Vulnerable Dependency: numpy — 10 advisory(ies): CVE-2014-1859 (Numpy arbitrary file write via symlink attack); CVE-2021-41495 (NumPy NULL Pointer Dereference); CVE-2021-33430 (NumPy Buffer Overflow (Disputed)) +7 more

Critical

• Category: Supply Chain
• Confidence: 82% confidence
• Finding: The requirement allows any numpy version >=1.21.0, and the scanner reports multiple advisories affecting some numpy releases. Because the dependency is not pinned, installations could resolve to a vulnerable version within the allowed range depending on environment and index state, creating a real but indirect supply-chain exposure. In this data-visualization context, exploitability is somewhat constrained, but vulnerable native-code libraries can still introduce crash, denial-of-service, or file-handling risks.

Known Vulnerable Dependency: pillow — 10 advisory(ies): CVE-2016-2533 (Pillow buffer overflow in ImagingPcdDecode); CVE-2023-50447 (Arbitrary Code Execution in Pillow); CVE-2021-27922 (Pillow Uncontrolled Resource Consumption) +7 more

Critical

• Category: Supply Chain
• Confidence: 91% confidence
• Finding: The requirement permits any pillow version >=9.0.0, and Pillow has a history of serious issues including image parsing vulnerabilities and reported code-execution or resource-consumption flaws in some releases. Since this skill is for data visualization and may plausibly process images or render image-derived content, an unpinned Pillow dependency materially increases the chance of installing an affected version and exposing parsing attack surface.

Known Vulnerable Dependency: scipy — 4 advisory(ies): CVE-2013-4251 (SciPy creates insecure temporary directories); CVE-2013-4251 (The scipy.weave component in SciPy before 0.12.1 creates insecure temporary dire); CVE-2023-25399 (A refcounting issue which leads to potential memory leak was discovered in scipy) +1 more

High

• Category: Supply Chain
• Confidence: 72% confidence
• Finding: The scipy requirement is only lower-bounded, so vulnerable releases may still satisfy the constraint if installed in some environments. While several cited advisories are old and may have limited applicability, the lack of exact pinning means the dependency policy does not prevent known-affected versions, creating a genuine dependency-risk issue rather than a purely theoretical false positive.

VirusTotal

64/64 vendors flagged this skill as clean.

Static analysis

No suspicious patterns detected.