Securing LLMs: Prompt Engineering & Mitigating Prompt Injection Attacks - Parth Patel & Ishneet Dua

Securing Large Language Models: Best Practices for Prompt Engineering and Mitigating Prompt Injection Attacks [Beginner] - Parth Patel & Ishneet Dua

The rapid adoption of large language models (LLMs) in enterprise IT environments has introduced new challenges in security, responsible AI, and privacy. One critical risk is the vulnerability to prompt injection attacks, where malicious actors manipulate input prompts to influence the LLM's outputs and introduce biases or harmful outcomes. This guide outlines security guardrails for mitigating prompt engineering and prompt injection attacks. The authors present a comprehensive approach to enhancing the prompt-level security of LLM-powered applications, including robust authentication mechanisms, encryption protocols, and optimized prompt designs. These measures aim to significantly improve the reliability and trustworthiness of AI-generated outputs, while maintaining high accuracy for non-malicious queries. The proposed security guardrails are compatible with various model providers and prompt templates, but require additional customization for specific models. By implementing these best practices, organizations can instill higher trust and credibility in the use of generative AI-based solutions, maintain uninterrupted system operations, and enable in-house data scientists and prompt engineers to uphold responsible AI practices.

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