Threat Modeling Prompt Injection in Content Automation Pipelines

Threat Modeling Prompt Injection in Content Automation Pipelines

Prompt injection is not just a chatbot issue. In commit-to-content pipelines, untrusted text can steer model behavior unless you isolate and sanitize inputs.

Step 1: Classify trust boundaries for every text source

trusted: internal templates, approved taxonomies
untrusted: commit messages, issue titles, external docs

Step 2: Apply neutralization before context assembly

def neutralize(text: str) -> str:
    return text.replace("```", "` ` `").replace("<script>", "")

Step 3: Enforce output policy gates

if detects_secret(output) or contains_private_paths(output):
    block_publish(output)

Common pitfall

Mixing trusted instructions and untrusted payload in one undifferentiated prompt string.