Prompt Injection Scanner for LLM Apps

Where it fits

• Pre-release checks for ChatGPT-style apps, copilots, agent workflows, and RAG assistants.
• Security regression tests before merging changes to prompts, tools, retrievers, or model providers.
• Vendor review when a customer asks how prompt injection and jailbreak risks are tested.

Operational steps

• Map every LLM entry point: user chat, uploaded files, retrieved documents, tool responses, web pages, and system prompts.
• Run direct, indirect, and multi-turn injection tests against the same policy the production app uses.
• Fail the build when high-risk findings show tool misuse, policy override, secret leakage, or hidden instruction disclosure.
• Export the report with severity, CVSS score, evidence, and remediation tasks for the engineering owner.

Common risks

• A malicious document in a RAG index tells the model to reveal internal instructions.
• A user asks the model to ignore the developer message and call an unsafe tool.
• A multi-turn conversation slowly reframes the policy until the model leaks private context.

How PromptGuard Scan fits the workflow

PromptGuard Scan packages these checks into a repeatable scan suite with CI/CD blocking, jailbreak template coverage, leakage detection, and reports that security reviewers can read without replaying every prompt.