designing-privacy-preserving-analytics

Pass

Audited by Gen Agent Trust Hub on Jun 16, 2026

Risk Level: SAFE
Full Analysis
  • [SAFE]: The skill is primarily educational, providing frameworks and methodologies for GDPR-compliant data anonymization.
  • [PROMPT_INJECTION]: No instructions were found that attempt to override agent behavior, bypass safety filters, or extract system prompts.
  • [EXTERNAL_DOWNLOADS]: The documentation references official GitHub repositories for established privacy libraries from Google, OpenDP (Harvard/Microsoft), and IBM. These are well-known and trusted sources for privacy technology.
  • [COMMAND_EXECUTION]: The Python script scripts/process.py is self-contained and uses only standard mathematical and data manipulation libraries (math, random, collections) without invoking shell commands or external processes.
  • [DATA_EXFILTRATION]: No network operations, credential access, or sensitive file path interactions were detected. The script processes data entirely in-memory.
Audit Metadata
Risk Level
SAFE
Analyzed
Jun 16, 2026, 03:33 PM
Security Audit — agent-trust-hub — designing-privacy-preserving-analytics