Electricity Forecasting Framework

• Outcome: Pass
• Latest audit: May 29, 2026 at 8:34 PM UTC
• Version:
  1.0.0

Overview

This is a coherent electricity forecasting skill with local ML scripts; the main caution is to only load model files you trust.

Install and run this in a clean virtual environment, review dependencies, and do not use joblib model files from untrusted sources. Keep forecasting datasets and generated deployment outputs in a dedicated project folder, and only expose the generated API server intentionally.

SkillSpector

By NVIDIA

Vulnerability Patterns

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

Findings (1)

Missing User Warnings

Medium

• Confidence: 98% confidence
• Finding: `joblib.load(model_path)` performs unsafe deserialization and can execute arbitrary code during loading if the `.joblib` file is malicious. In this skill context, the script accepts a user-supplied model path and loads it before validation, making untrusted model artifacts a realistic code-execution vector on the deployment host.

VirusTotal

60/60 vendors flagged this skill as clean.