Model Level Defense

Model-level defenses aim to protect large language models (LLMs) and other machine learning models from adversarial attacks, such as "jailbreaking" or data poisoning, which manipulate models to produce undesirable outputs or compromise their integrity. Current research focuses on developing robust defenses against various attack vectors, including prompt engineering, multilingual manipulation, and the injection of poisoned training data, often employing techniques like critic models to identify malicious inputs or run-time correction mechanisms. Effective model-level defenses are crucial for ensuring the safe and reliable deployment of AI systems across diverse applications, mitigating risks associated with malicious use and biased outputs.