arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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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 expression/TF lists from user-supplied local files via `pd.read_csv(expression_file, ...)` and `load_tf_names(tf_file)`, and those file contents become readable text/data fed into the model-invocation context (notably TF free-text lines), so outsider-authored file content can be ingested.
- Repository
- synthetic-sciences/openscience
- Commit
52845c3
- Audited
- Security analysis
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