Mitigating Prompt Injection Attacks in Large Language Model Applications: A Defense-In-Depth Framework for Secure Generative AI Deployment and Governance by Mohammad Shaidujjaman :: SSRN

Publication date unknown · First observed: 13 Oct 2026

A research paper by Mohammad Shaidujjaman proposes a Defense-in-Depth Prompt Injection Mitigation Framework for LLM applications, combining layered controls such as input sanitization, instruction-context separation, trust-aware retrieval, privilege restriction, tool authorization, runtime monitoring, and output validation. The study evaluates whether coordinated defenses outperform standalone controls using metrics like attack success rate, false positive rate, and utility degradation across direct/indirect prompt injection, jailbreaks, and data-exfiltration scenarios.