At its core, data poisoning involves the deliberate manipulation of datasets used during the pre-training, fine-tuning, or embedding stages of an LLM’s lifecycle. The objective is often to introduce backdoors, degrade model performance, or inject bias—toxic, unethical, or otherwise damaging behaviour—into outputs.
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.