tooluniverse-clinical-trial-design

MEDIUM W011: Third-party content exposure detected (indirect prompt injection risk).

• Third-party content exposure detected (medium risk: 0.30). In python_implementation.py the runtime ingests outsider-authored free text directly as the user-supplied indication and drug_name arguments, then passes them into tool searches (e.g., PubMed_search_articles, OpenTargets_get_disease_id_description_by_name, search_clinical_trials) where the tool results become LLM input for report generation.

HIGH W008: Secret detected in skill content (API keys, tokens, passwords).

• Secret detected (high risk: 1.00). I scanned all files for literal, high-entropy credentials. I flagged the Slack shared_invite token because it is a long, random-looking value embedded in a join URL that can grant access to the project's Slack workspace — this meets the definition of a high-entropy literal that provides access to a service.

I ignored obvious placeholders and low-security setup values:

• .env.template entries like BIOGRID_API_KEY=your_api_key_here, BRENDA_PASSWORD=your_api_key_here, export OPENAI_API_KEY="sk-..." in README, and other "your_api_key_here" or "sk-..." placeholders are documentation placeholders and explicitly listed as things to ignore.
• No private keys, bearer tokens, or other API secrets (beyond the Slack invite token) were present.

Files/lines ignored as placeholders: .env.template (all lines), README.md lines showing export OPENAI_API_KEY="sk-..." (placeholder), and other example keys in documentation.