Adversial Prompt Injection in Large Language Models: Taxonomy, Exploits, and Mitigation Frameworks

Abstract

Adversarial prompt injection attacks pose a critical security threat to Large Language Models (LLMs) by manipulating model instructions through malicious inputs. In this paper, we present a comprehensive analysis of prompt injection vulnerabilities in LLMs. We develop a taxonomy encompassing direct, indirect, and multi-stage (chained) prompt injection attacks, detailing various exploits from simple “ignore previous instructions” overrides to covert multi-turn schemes. Through case studies and experimental evidence from recent literature, we demonstrate that even state-of-the-art models (e.g. GPT-4) can be consistently coerced into producing disallowed content, leaking confidential data, or executing unintended actions. We evaluate real-world risks via documented incidents (such as system prompt leaks and compromised LLM-integrated applications) and quantitative benchmarks, finding adversarial success rates exceeding $80 %$ in many scenarios. To address these threats, we propose a defense-in-depth mitigation framework. Our framework combines prompt sanitization (input filtering and normalization), context isolation (segregating user input from system instructions and external data), and model hardening (enhanced alignment tuning and adversarial training) to substantially reduce injection success. We also outline practical defensive strategies including role-based privilege restriction, output validation, and continuous red-teaming. Finally, we discuss the broader implications of prompt injection for AI safety, ethics, and policy, and highlight directions for future work in building robust, secure LLM systems.