Is Your LLM at Risk? Explaining Prompt Injection Attacks

This article explains prompt injection attacks against large language models—how attackers can manipulate model behavior by injecting instructions into prompts or external content, the distinction between direct and indirect injections, real-world examples and demonstrations, and practical mitigations (treating LLMs as untrusted, restricting tool/data access, specialized testing). It emphasizes that prompt injection is a systemic risk for LLM deployments rather than a traditional software bug and recommends architecture-level protections and adversarial testing.