Zero Day Threat Analysis Using Prompt Engineering and AI Agents: A Framework for Autonomous Cyber Defense
Zero Day Threat Analysis Using Prompt Engineering and AI Agents: A Framework for Autonomous Cyber Defense
This research paper proposes a novel AI-powered approach for detecting and responding to Zero Day cyber threats by combining Prompt Engineering with Large Language Model (LLM) agents. Using the real-world CVE-2021-40444 as a case study, the paper demonstrates how AI agents can simulate human threat analysis, detect novel patterns in logs, and recommend immediate containment steps. A self-defending AI system is also proposed to automate response workflows and vendor notifications. This framework aims to accelerate detection, reduce reliance on static rules, and make cybersecurity systems more autonomous and resilient.
Prompt Engineering, CVE-2021-40444, Cybersecurity, Cyber Defense, AI Incident Response, Zero Day, Intelligent Agents, Behavioral Detection, Threat Detection, Large Language Models, AI-Augmented SOC, Self-Defending Networks, AI, GPT-4, Autonomous Systems, Security Automation, LLMs, Self-Healing Security, Exploit Detection, NLP in Security, AI Security Framework
Prompt Engineering, CVE-2021-40444, Cybersecurity, Cyber Defense, AI Incident Response, Zero Day, Intelligent Agents, Behavioral Detection, Threat Detection, Large Language Models, AI-Augmented SOC, Self-Defending Networks, AI, GPT-4, Autonomous Systems, Security Automation, LLMs, Self-Healing Security, Exploit Detection, NLP in Security, AI Security Framework
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