regression-modeler
Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The required runtime workflow loads user-supplied input datasets from an arbitrary file path (CSV/TSV/Excel/JSON) via pandas (e.g., `load_data(args.input)`), and then turns that contents (including column names/values) into readable text embedded in the output—specifically `result.summary().as_text()` and the “interpretation” strings—so if the input file is outsider-authored, its free text/labels can flow into the LLM context indirectly through the agent reading the produced output.
- Repository
- zebbern/claude-code-guide
- Commit
af7d491
- Audited
- Security analysis
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