New Attack

Research on attacks against large language models (LLMs) and related AI systems is rapidly expanding, focusing on vulnerabilities exploited to elicit harmful outputs or extract sensitive information. Current efforts concentrate on developing and evaluating various attack methods, including jailbreaking, data poisoning, prompt injection, and membership inference attacks, often targeting specific model architectures like transformer-based LLMs and diffusion models. This research is crucial for understanding and mitigating the risks associated with increasingly powerful AI systems, informing the development of more robust and trustworthy AI applications across diverse sectors.