Large Language Models (LLMs) are rapidly changing how we interact with technology, from powering search engines to assisting in complex decision-making. But as these AI systems grow more powerful, they also become more vulnerable to sophisticated cyberattacks, including something called 'backdoor attacks.' Imagine an LLM that seems perfectly normal, answering questions and generating text just as expected. However, hidden within its code is a secret trigger, like a specific phrase or a subtle syntactic pattern. When this trigger is activated, the LLM suddenly changes its behavior, potentially causing significant harm. This is the essence of a backdoor attack. By subtly manipulating even a tiny fraction of the training data, attackers can exploit the LLM's vast memorization capacity to create these hidden vulnerabilities. The LLM learns to associate the trigger with a malicious action, effectively 'hacking' the AI from within. Recent research reveals how these attacks can be launched during both the training and inference stages of an LLM's lifecycle. Even emerging learning paradigms like instruction tuning and reinforcement learning from human feedback (RLHF) are susceptible, as they often rely on crowdsourced data that isn't fully controlled. What's even more alarming is the stealthiness of these attacks. The backdoored LLM can operate undetected, behaving normally until the trigger is activated, making it extremely difficult to identify and neutralize the threat. The implications are far-reaching, affecting everything from financial systems and healthcare applications to safety-critical systems like autonomous vehicles. If an LLM controlling a trading algorithm is compromised, the consequences could be disastrous. Similarly, a backdoored LLM in a medical diagnosis system could lead to incorrect and potentially harmful treatments. Defending against these threats is a complex and ongoing challenge. Researchers are exploring various defense strategies, including fine-tuning training methods, weight merging techniques, and input perturbation during inference. Detection methods are also being developed, using perplexity analysis, attribution scoring, and even reverse-engineering triggers to identify compromised LLMs. But as LLMs continue to scale and become more sophisticated, so too will the methods used to attack them. The future of AI security depends on staying ahead of these threats and developing robust, scalable solutions to safeguard these powerful tools.