Prompt Injection 2.0: Hybrid AI Threats
Jeremy McHugh, Kristina \v{S}ekrst, and Jon Cefalu

TL;DR
This paper analyzes the evolution of prompt injection attacks into hybrid AI threats, highlighting their integration with web security vulnerabilities and proposing architectural solutions for enhanced defense.
Contribution
It provides a comprehensive analysis of Prompt Injection 2.0, examining hybrid threats and evaluating existing mitigation technologies against modern AI security challenges.
Findings
Traditional security controls are ineffective against AI-enhanced attacks
Hybrid threats can bypass web application firewalls and filters
Proposed architectural solutions improve threat detection and mitigation
Abstract
Prompt injection attacks, where malicious input is designed to manipulate AI systems into ignoring their original instructions and following unauthorized commands instead, were first discovered by Preamble, Inc. in May 2022 and responsibly disclosed to OpenAI. Over the last three years, these attacks have continued to pose a critical security threat to LLM-integrated systems. The emergence of agentic AI systems, where LLMs autonomously perform multistep tasks through tools and coordination with other agents, has fundamentally transformed the threat landscape. Modern prompt injection attacks can now combine with traditional cybersecurity exploits to create hybrid threats that systematically evade traditional security controls. This paper presents a comprehensive analysis of Prompt Injection 2.0, examining how prompt injections integrate with Cross-Site Scripting (XSS), Cross-Site Request…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Artificial Intelligence in Healthcare · Brain Tumor Detection and Classification
