Direct Prompt Injection is a technique where a user inputs specific instructions or queries directly into an LLM (Large Language Model) to influence or control its behavior. By crafting the prompt in a particular way, the user can direct the LLM to perform specific tasks, generate specific outputs, or follow certain conversational pathways. This technique can be used for legitimate purposes, such as guiding an LLM to focus on a particular topic, or for more experimental purposes, like testing the boundaries of the model’s understanding and response capabilities. However, if misused, direct prompt injection can lead to unintended consequences, such as generating inappropriate or misleading content.
Sensitive Information Disclosure in LLMs via Prompt Injection occurs when a user manipulates the prompt to extract or expose information that should remain confidential or restricted. LLMs trained on large datasets may inadvertently learn and potentially reproduce sensitive information, such as personal data, proprietary knowledge, or private conversations. Through carefully crafted prompts, an attacker could coerce the model into revealing this sensitive data, posing a significant privacy risk. Mitigating this risk requires rigorous data handling practices, including the anonymization of training data and implementing guardrails within the LLM to recognize and resist prompts that seek to extract sensitive information.