Case Studyllm agentshealthcareconfused deputy
Agentic AI Exposes Confused Deputy Vulnerability
|
8.1
Researchers demonstrate that agentic AI can create confused deputy authorization flaws that allow LLMs to retrieve unauthorized patient records. In a lab using a FailMed AI demo with claude-3-haiku, a vulnerable Flask endpoint (/api/chat) and a SQLite backend leaked other patients' data despite hardened system prompts. Recommended fix is tool-level authorization tying requested patient_id to session user_id to prevent exfiltration.
Scoring Rationale
Strong practical relevance and actionable mitigation guidance, limited by single-demo scope and lack of peer-reviewed validation.
Practice with real Health & Insurance data
90 SQL & Python problems · 15 industry datasets
Used by DS/ML engineers at top companies
250 free problems · No credit card
See all Health & Insurance problems
