Prompt Injection Shield

It's a Thursday code review. Your team's LLM feature is two days from release. Someone pastes a test case into the chat: a crafted user input that makes your model ignore its system prompt and spit out the exact thing it was never supposed to say.

• Enumerate every prompt surface in your listing context that represents a potential injection entry point
• Rank identified vulnerabilities by exploitability and downstream impact so your team works the right problems first
• Classify attack vectors by technique - instruction override, role confusion, context leakage, jailbreak chaining - for precise remediation
• Generate concise rollout guidance scoped to your actual architecture, not generic best practices
• Distinguish low risk edge cases from high priority exposures that block safe deployment
• Detect indirect injection paths where attacker controlled data reaches your model through retrieval, tools, or third party inputs

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