When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.
While theoretical risks highlight potential harm, real-world scenarios bring the dangers of LLM02:2025 into sharper focus. Below are three attack vectors illustrating how sensitive information disclosure unfolds in practical settings.
Prompt injection is not just a vulnerability — it’s a multi-headed threat vector. From overt attacks to inadvertent leakage, each scenario introduces unique risks, requiring tailored strategies to safeguard operational integrity, regulatory compliance, and business reputation.
Prompt injection occurs when malicious actors manipulate an LLM’s input to bypass security controls or extract unauthorised information. Unlike traditional software vulnerabilities, prompt injection exploits the fundamental way LLMs process and respond to natural language inputs.
AI agents are autonomous or semi-autonomous software programmes that perceive their environment, make decisions, and take actions to achieve defined goals. Unlike traditional software, which follows static instructions, AI agents adapt, learn, and optimise over time.