privacy-data-sharing
Pass
Audited by Gen Agent Trust Hub on May 12, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: No security issues detected in the instructions or scripts. The skill focuses on legitimate privacy engineering practices.- [EXTERNAL_DOWNLOADS]: The skill references the Synthetic Data Vault (SDV) library and standard data science packages including pandas and numpy. These are well-known, reputable tools within the privacy research and data science communities.- [DATA_EXFILTRATION]: No network operations, credential harvesting, or sensitive file access patterns were identified. The logic is self-contained for local data processing and policy validation.- [PROMPT_INJECTION]: No attempts to override agent constraints, bypass safety filters, or extract system prompts were found. The instructions are strictly technical and educational.- [SAFE]: Data Ingestion Surface Analysis:
- Ingestion points:
SKILL.mdandscripts/process.pyprocess external DataFrames for synthetic data generation and assessment. - Boundary markers: Standard data processing implementation without specific delimiters.
- Capability inventory: No subprocess calls, shell execution, or network operations are present across the scripts.
- Sanitization: Not applicable to the statistical profiling use case.
- Risk Assessment: Although the skill processes untrusted external data, the absence of dangerous capabilities (network/shell) prevents exploitation via indirect prompt injection.
Audit Metadata