Prompt Injection refers to a security vulnerability in @Large Language Model (LLM)s where malicious or adversarial input is crafted to manipulate the model’s behavior. This can involve overriding system instructions, exfiltrating sensitive information, or causing the model to perform unintended actions. Prompt injection is related to adversarial prompting and is a concern for organizations deploying AI systems in sensitive or production environments. Researchers highlight that these attacks may be explicit, such as directly instructing the model to ignore previous instructions, or subtle, using carefully constructed contexts to achieve similar effects. Mitigation strategies under exploration include input filtering, sandboxing, and improved alignment techniques, though no universally effective solution has been established. The issue is part of a broader field of AI security and reliability research, with ongoing work from both academic and industry groups to standardize defenses. The presence of prompt injection emphasizes the need for responsible AI deployment and rigorous evaluation of model vulnerabilities.

Contexts

• #artificial-intelligence-lexicon (See: @AI Glossary)