A new open-source Python tool named EpsteIn enables users to check if their LinkedIn connections appear in over 3.5 million pages of Jeffrey Epstein court documents recently released by the U.S. Department of Justice. Developed by Christopher Finke, it runs locally to prioritize privacy amid rising interest in OSINT for network validation.
EpsteIn indexes mentions from public Epstein files using an API built by Patrick Duggan on DugganUSA.com, scanning user-exported LinkedIn Connections.csv files.
It generates an interactive HTML report (default: EpsteIn.html) sorted by mention count, featuring contact cards with names, positions, companies, excerpt snippets, and direct links to DOJ PDFs.
404 Media tested it, finding 22 potential matches, including common names like “Adam S.,” underscoring false positive risks from ambiguous references.
Users need Python 3.6+, the requests library, and a virtual environment via pip install -r requirements.txt. Export LinkedIn connections through Settings > Data privacy > Get a copy of your data (up to 24 hours wait), then run python EpsteIn.py --connections /path/to/Connections.csv. Options include --output for custom HTML paths; no cloud processing ensures data stays local.
This tool exemplifies accessible OSINT for threat hunting in professional networks, helping cybersecurity pros audit associations post-data dumps. Local execution mitigates server-side privacy leaks, but exporting LinkedIn data exposes contacts, and misuse could enable targeted doxxing or harassment. False positives demand manual review, aligning with best practices in IOC validation.
| Aspect | Benefits | Risks |
|---|---|---|
| Privacy | Runs offline; no API keys shared | LinkedIn export reveals full network |
| Accuracy | Contextual excerpts for verification | Common names yield false matches |
| Scalability | Handles 3.5M pages via indexed API | Setup requires technical skills |
EpsteIn Tool aids personal risk assessment without judgment, but experts urge caution on inferences from public records.
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