Cyberattacks on Large Language Models - Attack Detection and Architecture Adaptability

Large Language Models (LLMs) like GPT and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$PaLM$</tex> have transformed natural language processing, enabling advancements in text generation, language translation, and conversational AI. However, their increasing adoption has exposed critical vulnerabilities, making them susceptible to cyberattacks such as prompt injection, data poisoning, and Distributed Denial of Service (DDoS) attacks. These threats compromise the security, reliability, and integrity of LLM applications in real-world scenarios. To mitigate these challenges, advanced defense mechanisms such as attack detectors and attack libraries play a crucial role. Attack detectors analyze input patterns and monitor model responses to identify anomalies and potential security breaches. These systems rely on attack libraries that act as structured repositories of predefined attack patterns, known exploits, and evolving threats. The attack library functions as a dynamic lookup table, enabling real-time vulnerability detection and rapid response to existing and novel threats. By integrating systematic vulnerability detection techniques and structured attack analysis, these mechanisms strengthen LLM security. Attack detectors and libraries provide a comprehensive approach to identifying, classifying, and mitigating security risks. This framework ensures the safe deployment of LLMs in various industries, strengthening their resilience and minimizing exposure to emerging cyber threats in sensitive applications.