The Safety Frontier in LLMs: Risks, Defenses, and Open Challenges

Large Language Models (LLMs) such as ChatGPT, Claude, and LLaMA are rapidly transforming domains including healthcare, education, law, and finance. However, their increasing adoption in high-stakes applications raises serious safety concerns. Without sufficient safeguards, LLM-powered AI systems are vulnerable to threats such as prompt injection, training data poisoning, model theft, denial-of-service through prompt abuse, misinformation generation, adversarial examples, model inversion, data leakage, and insider misuse. These vulnerabilities can produce harmful, biased, or misleading outputs, compromise privacy, and undermine trust. To address these risks, researchers have proposed safety approaches that aim to constrain and guide model behaviour, proactively detect and resist attacks, and incorporate ethical alignment into model design. Together, these methods form a multi-layered defence that combines preventive, detective, and corrective measures to improve LLM robustness and trustworthiness. This paper presents a comprehensive review of key safety mechanisms for LLMs, categorizing them into guardrails, red teaming, adversarial training, alignment techniques, jailbreak detection and prevention, watermarking, and fuzzing. For each category, recent advances are summarized, strengths and limitations are assessed, and open challenges are identified to support safer and more reliable real-world deployment of LLMs.