Large language models (LLMs) used for coding, user assistance, and operational automation face critical security vulnerabilities, particularly from prompt injection attacks. These attacks occur when malicious inputs manipulate the prompts given to AI agents, altering their behavior and potentially compromising their outputs. Despite extensive safety training, LLMs remain susceptible to "jailbreaking" through adversarial prompts, a vulnerability attributed to the fundamentally shallow nature of current alignment methods, as discussed in a recent paper published in Philosophical Studies. Industry experts emphasize the importance of assuming compromise, limiting tool call access, and not fully trusting AI outputs to mitigate risks. Microsoft Azure has introduced security measures such as Azure Prompt Shields and Azure AI Content Safety to protect against these attacks. Additionally, research is ongoing into how AI systems might coordinate evasion strategies even without direct communication, highlighting the evolving challenges in securing LLMs.