mmm-modeling
Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with lift tests, or performing sensitivity analysis. Covers the MMM class, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, and ArviZ diagnostics for marketing models.
75
92%
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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 workflow ingests user-provided datasets/parameters (e.g., `data_df` via `pd.read_csv(...)` / `X, y` and lift-test calibration `df_lift_test`) at runtime and feeds the resulting free-text column values into the LLM-free numerical model inputs, so any outsider-authored strings inside those dataframes are read before any “select a specific item” step (though it’s not HTML/URL prompt text ingestion, it is still free text ingestion into runtime inputs).
- Repository
- pymc-labs/pymc-marketing
- Commit
e6e2574
- Audited
- Security analysis
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