What is the single most important control?

Input boundary validation. Most injection enters through untrusted input, so validating and escaping it — and treating retrieved content as hostile — has the highest leverage.

Should the model be able to return its own system prompt?

No. If the model can echo its instructions, an exfiltration injection is trivial. Keep the system prompt and secrets out of anything returnable.

How should tool calls be guarded?

Default-deny: allow only explicit actions, require confirmation for sensitive ones, put an injection classifier before tool calls, and scope credentials per action with least privilege.

Why scan on every deploy instead of once?

A one-time scan goes stale the moment you change a prompt or add a tool. Per-deploy scanning (a CI gate) catches the new patterns your changes introduce. PromptShield Pro supports CI-ready scanning.

LLM app hardening checklist (pre-ship)

Input boundary controls

Start at the edge. Validate and escape untrusted input before it reaches the model, and treat every external source — user text, retrieved pages, uploaded files, API payloads — as hostile. Bound input length and shape where you can, and never let retrieved content sit in the same trusted block as your instructions.

This is the single highest-leverage control, because most injection enters here. PromptShield's report flags patterns in your sample input so you can see exactly which boundary checks to tighten first.

Context and system-prompt hygiene

Keep your system prompt and secrets out of anything the model can return. If the model can echo its own instructions, an exfiltration injection becomes trivial. Separate trusted instructions from untrusted data architecturally, not just by wording.

Also avoid embedding credentials, internal schema, or customer data in the prompt. The less sensitive material lives in the context window, the smaller the blast radius of any successful injection.

Tool and action guards

If your app calls tools, default-deny: the model should only invoke explicitly allowed actions, and sensitive ones (send email, delete, transfer, external HTTP) should require confirmation or an out-of-band check. Put an injection classifier before tool calls so a flagged input cannot silently trigger an action.

Least privilege applies to tools too — grant the minimum scope each action needs, and scope credentials per action rather than sharing one all-powerful key.

Output and monitoring

Validate model output before it touches other systems (LLM02). Don't pipe raw model text into shells, SQL, or email without checks. Log inputs and outputs for the requests that matter, and alert on patterns that look like successful injection (unexpected tool calls, leaked config).

Monitoring won't stop the first attack, but it shortens the time between a breach and your noticing — and it feeds better samples back into scanning.

Process: scan before every deploy

Make scanning a CI gate. A one-time check goes stale the moment you change a prompt or add a tool; scanning on every deploy catches the new patterns your changes introduce. PromptShield Pro supports CI-ready scanning so the check runs automatically.

None of these controls, alone or together, makes an app unhackable. They lower the odds of the known attacks and make the residual risk visible — the realistic goal for LLM security today. PromptShield is a decision-support scanner in this process, not a guarantee.

Authoritative references

OWASP Top 10 for Large Language Model Applications (2025): https://owasp.org/www-project-top-10-for-large-language-model-applications/
OWASP (general application security): https://owasp.org/