Analysis
Prompt Injection Threats in Azure-Based Large Language Model Applications
Publication date unknown · Discovered repec.org
Page published
Publication date unknown · First observed: 6 Nov 2026
Coverage timeline
Single-source analysis — one report is available.
Why it matters
Azure-hosted LLM applications combine untrusted content with instructions and tools, making trust-boundary enforcement and layered defenses important beyond native content filtering alone.
“Prompt Injection Threats in Azure-Based Large Language Model Applications” reviews existing literature and maps six attack categories to Azure-hosted LLM applications and their retrieval and tool integrations. The paper recommends layered defenses combining content filtering, guardrails, instruction–data separation, least-privilege tool permissions, provenance checks, and systematic red-teaming.