The article emphasizes the importance of securing Generative AI (Gen-AI) applications to prevent prompt injection attacks by malicious actors. Prompt injection vulnerabilities can lead to data exfiltration, social engineering, and other security issues. The article discusses direct and indirect prompt injections, highlighting the risks associated with each method.
Examples of indirect prompt injection are provided, such as bypassing automatic CV screening, exfiltrating user emails, and bypassing LLM-based supply chain audits. Best practices to reduce the risk of prompt injection include prompt engineering techniques, clearly signaling AI-generated outputs, sandboxing unsafe inputs, input and output validations, testing for prompt injection, using dedicated prevention tools, implementing a robust logging system, and extending traditional security measures to include LLM risks.
Developers are encouraged to write good prompts, clearly mark AI-generated outputs, sandbox unsafe inputs, implement validations and filtering, conduct security testing, use dedicated prevention tools, maintain robust logging, and utilize dedicated AI security solutions. By following a prompt injection defense checklist, developers can reduce the risk and impact of indirect prompt injection attacks, ensuring a balance between productivity and security in Gen-AI applications.
