Definition
LLM Attacks refers to malicious techniques designed to exploit vulnerabilities in Large Language Models, causing them to behave in unintended or harmful ways. These attacks aim to compromise the integrity, confidentiality, or availability of the LLM or the data it processes. They work by crafting specific inputs, known as prompts, that trick the model into revealing sensitive information, generating biased or inappropriate content, or executing unauthorized actions. Understanding LLM attacks is crucial for developers and security professionals to build robust and secure AI systems. For instance, prompt injection can trick an LLM into ignoring its safety guidelines and performing harmful tasks. Unlike traditional cyberattacks that target network infrastructure, LLM attacks specifically target the model's understanding and response generation. Concepts related to LLM security and potential vulnerabilities are increasingly appearing in advanced cybersecurity certifications.