pymc-bayesian-modeling
Warn
Audited by Gen Agent Trust Hub on Mar 3, 2026
Risk Level: MEDIUMCOMMAND_EXECUTIONPROMPT_INJECTION
Full Analysis
- [DYNAMIC_EXECUTION]: The documentation includes code snippets demonstrating the use of the Python 'pickle' module for serializing and deserializing PyMC models and results.
- Evidence: In
references/workflows.md, the code sample showswith open('model.pkl', 'rb') as f: saved = pickle.load(f). Deserializing untrusted data withpicklecan lead to arbitrary command execution on the host system. - [INDIRECT_PROMPT_INJECTION]: The skill defines workflows that ingest external data, creating an attack surface where malicious instructions could be embedded in the data to influence agent behavior.
- Ingestion points: Data is ingested via
pd.read_csv('data.csv')inSKILL.mdandassets/hierarchical_model_template.py. - Boundary markers: No boundary markers or instructions to ignore embedded content are used during data ingestion.
- Capability inventory: The skill possesses file-writing capabilities through
idata.to_netcdf(),summary.to_csv(), andplt.savefig()across multiple scripts and templates. - Sanitization: There is no evidence of data validation, sanitization, or schema enforcement before the data is processed by the modeling engine.
Audit Metadata