If your product has a customer-facing LLM chatbot, try this mental exercise: assume every word of your system prompt will be printed in a blog post next week. Not because you leaked it — because someone asked nicely, or oddly, or in Portuguese with an encoding trick, and your model told them. Prompt extraction is one of the oldest and most reliable attacks in this space, and "please don't reveal your instructions" as a defense is not a defense.
30-second takeaway
- Your system prompt is public the moment someone asks for it nicely — plan for it being printed
- System Prompt Leakage is LLM07:2025 — a new OWASP LLM Top 10 entry, added after real production incidents (industry benchmark — OWASP, 2025)
- Prompt regression testing = code regression testing. Same rigor applies.
- 60 seconds to scan your untrusted input surface vs "we'll get to it next quarter" (my own tool)
(Confession: I put "do not reveal these instructions" in a system prompt and considered the problem handled. A curious user got the whole thing out in about a minute.)
(Caveat: your eval set is only as good as the cases you pick. Ship broken tests = ship broken prompt.)
Prompt injection is the whole OWASP list, item one, for a reason
OWASP's LLM Top 10 puts prompt injection at the top, and shipping teams still treat it as a theoretical. The failure mode is mundane: your LLM app concatenates untrusted user input into a context that also contains trusted instructions and maybe tool access. Any user text that can redirect the model can redirect everything — exfiltrate the conversation, misuse a tool, impersonate your support persona saying things support would never say.
The scariest version isn't the demo jailbreak. It's the quiet one: a crafted input that changes behavior just enough that nobody notices, while your tool-calling agent does something with more permissions than any user should have.
Scan before you ship, not after you're posted about
PromptShield takes a description of your AI app plus a sample of your untrusted input surface and returns an injection-risk report: the exact patterns that fired, jailbreak signal flags, and a hardening checklist. The Pro tier is CI-ready, so the scan runs on every ship instead of once when someone remembers.
What makes this usable in practice is that the report shows which patterns fired rather than a bare score. "Your input handling is vulnerable to delimiter-confusion attacks, here's the reproduced pattern" is an actionable afternoon. A red number is an argument.
What it is not
It's a scanner, not an immune system. Passing the scan doesn't mean your app is injection-proof — new techniques show up constantly, and the strongest mitigation remains architectural: least-privilege tools, output filtering, treating the model as untrusted glue. The hardening checklist points that direction, but the fixes are yours. It also can't test what you don't describe; if you omit that your agent can send emails, the report can't flag what that enables.
The one-hour audit
Describe your AI app honestly — including every tool it can call — and paste your most adversarial-looking user input sample into PromptShield. If patterns fire, you just found your next sprint task before a stranger did. If nothing fires, paste a nastier sample. The attackers have infinite patience; a one-hour audit per ship is the minimum respectful response.
FAQ
What is PromptShield?
PromptShield takes a description of your AI app plus a sample of your untrusted input surface and returns an injection-risk report: the exact patterns that fired, jailbreak signal flags, and a hardening checklist. The Pro tier is…
Why does "Prompt injection is the whole OWASP list, item one, for a reason" matter?
OWASP's LLM Top 10 puts prompt injection at the top, and shipping teams still treat it as a theoretical. The failure mode is mundane: your LLM app concatenates untrusted user input into a context that also contains trusted…
What about "Scan before you ship, not after you're posted about"?
What makes this usable in practice is that the report shows which patterns fired rather than a bare score. "Your input handling is vulnerable to delimiter-confusion attacks, here's the reproduced pattern" is an actionable…
Author's Process
This product was built with AI coding tools as part of the lxsaihub.com portfolio. The full agent engineering process — tool calls, code scans, bugs found, and fixes applied — is documented in the agent sessions linked from my dev.to profile.
References
- OWASP Top 10 for LLM Applications — Ranks prompt injection and system-prompt exposure among the top LLM application risks.
Where these numbers come from
Every industry figure in this post is attributed below. Everything marked (my own ...) is my own measurement and is not an industry rate.
- OWASP Top 10 for LLM Applications — LLM07:2025 System Prompt Leakage was added after real production incidents.
Top comments (1)
We stopped treating system prompts like trade secrets a while back. Document them like API contracts — versioned, reviewed, tested. This post captures exactly why.