A guide to the hidden threat of prompt injection

Large language models (LLMs) are increasingly integrated into various systems, transforming from experimental features to critical components within production environments, which has significantly expanded their attack surfaces. One notable vulnerability is prompt injection, where attackers manipulate LLM inputs to bypass safety guidelines, leading to unauthorized actions or data exposure. This vulnerability is recognized by OWASP as a primary risk, as it targets an LLM's reasoning layer, distinct from classic injection attacks like SQL or XSS. Prompt injection can be direct, using user prompts to override system instructions, or indirect, embedding instructions within content the LLM processes later. Real-world cases demonstrate the severity of these vulnerabilities, showing how LLMs can be exploited for credential theft or sensitive data leaks. For security professionals, testing for prompt injection entails understanding the interaction between models, tools, and data, and it presents both a challenge and an opportunity for discovering high-impact vulnerabilities. The application of AI in security testing can automate routine tasks, allowing researchers to focus on identifying complex security flaws, thereby enhancing their effectiveness in uncovering and mitigating risks associated with LLMs.