Malicious User

Malicious user activity encompasses a broad range of threats, from sophisticated attacks exploiting vulnerabilities in large language models (LLMs) and autonomous systems to more subtle manipulations like data poisoning in machine learning and the spread of misinformation on social media. Current research focuses on developing robust detection methods, often employing machine learning techniques such as Q-learning, graph attention networks, and generative adversarial networks (GANs), alongside multi-agent systems and blockchain technologies for enhanced security. Understanding and mitigating these threats is crucial for ensuring the safety and reliability of AI systems, online platforms, and critical infrastructure, driving significant efforts in both theoretical advancements and practical applications within cybersecurity and AI safety.