Jev AI use cases
Safety & security
Screen untrusted text for injected instructions and data-exfiltration attempts before it reaches a model or tool.
Safety & security
Screen untrusted text for injected instructions and data-exfiltration attempts before it reaches a model or tool.
About safety & security
Any text a model reads can try to steer it: a chat message, an email, a web page, a tool result. Safety checks look at that text before the model does and decide whether it should pass, go to a person or be stopped.
Jev answers these checks as typed questions: a risk category, a severity score and a handling decision, each with a probability or confidence. Your code applies the thresholds, so the policy stays readable and outside the model an attacker is talking to.
Which page fits your task
- Prompt Injection DetectorYou pass user messages, documents, emails or tool outputs to a model and want to catch overrides, hidden instructions and data-exfiltration attempts.
Common mistakes
- Screening only user messages, while retrieved documents and tool outputs go straight to the model.
- Blocking on keywords, which flags articles and tickets that only discuss attacks.
- Relying on detection alone instead of limiting tool permissions and confirming risky actions.
Safety & security FAQ
Is a detector enough to stop prompt injection?
No. Use it as one layer alongside least-privilege tools, confirmation for irreversible actions and keeping untrusted text out of system prompts.
Should I check model outputs as well?
Yes. TypeSafe’s guardrails cookbook runs the same kind of check on both the messages going into a model and the replies coming out.
Further reading · reviewed 2026-09-23
- Guardrails for LLM inputs and outputsTypeSafe documentation · docs.typesafe.ai/cookbooks/llm_guardrails
- Confidence-gated routingTypeSafe documentation · docs.typesafe.ai/patterns/confidence-routing
One decision model. Your own rules.
Use a Noul for a yes/no probability, a Choice for a fixed set of outcomes, or a Score for an ordered rubric. Ask focused questions about the same input and combine the results in code.