parsing-trial-eligibility
Parses free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then matches them against patient facts that OpenMed extracted. Use when the user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules, screen a synthetic patient for trial fit, or explain why a patient does or does not meet criteria. Trigger keywords: eligibility criteria, inclusion, exclusion, trial matching, patient screening, criteria parsing, eligibilityModule, age/sex gates. Pairs after OpenMed and after searching-clinicaltrials: consume the eligibilityModule text from a study, structure it, and match against conditions, medications, labs, and demographics from openmed.analyze_text. Decision-support only — never autonomous enrollment.
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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 workflow ingests outsider-authored ClinicalTrials.gov trial text (`protocolSection.eligibilityModule.eligibilityCriteria` and bullets) at runtime (“From searching-clinicaltrials. Studies arrive with their eligibilityModule populated”), then runs `openmed.analyze_text` over those free-text eligibility bullets, so the LLM context can contain untrusted third-party prose (indirect prompt injection risk).
- Repository
- maziyarpanahi/openmed
- Commit
80da98c
- Audited
- Security analysis
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