Pandas Data Analysis
Pass
Audited by Gen Agent Trust Hub on Feb 17, 2026
Risk Level: SAFE
Full Analysis
- [Prompt Injection] (SAFE): The content consists of educational material and code snippets. There are no instructions that attempt to override agent behavior or bypass safety filters.
- [Data Exposure & Exfiltration] (SAFE): Code examples use internal dummy data or reference local CSV files. No hardcoded credentials, sensitive file paths, or unauthorized network operations were found.
- [Obfuscation] (SAFE): All content and code are provided in clear text. No Base64, zero-width characters, or other encoding techniques are used to hide malicious logic.
- [Unverifiable Dependencies & Remote Code Execution] (SAFE): The skill references standard, well-known Python libraries (pandas, numpy, matplotlib, seaborn). There are no commands to download or execute remote scripts (e.g., curl | bash).
- [Indirect Prompt Injection] (LOW): The skill demonstrates data ingestion via
pd.read_csv(). While this is a standard data science practice, processing untrusted external files always presents a theoretical surface for indirect prompt injection. - Ingestion points:
SKILL.md(code examples referencing 'sales_data.csv') - Boundary markers: Absent in educational snippets
- Capability inventory: Local file reading
- Sanitization: Not applicable to these static code examples
Audit Metadata