How Cross-Step Injection Attacks Exploit AI Workflows and Four Ways to Stop Them

AI workflows face a distinct security threat where malicious payloads embedded in external inputs, such as a Jira ticket description, can silently propagate across multiple processing phases before reaching a code execution layer. Unlike single-skill injection, the payload transforms at each step, making it harder to detect and trace after an incident. To counter this, security best practices recommend sanitizing all external input at the first entry point by extracting structured fields rather than passing raw text downstream. When raw text must be used in later phases, it should be isolated using explicit data-boundary declarations in prompts, instructing the model to treat any instruction-like content as inert data. Additionally, each workflow phase should operate under strict permission scopes, limiting read, write, and network access only to what that specific phase genuinely requires.