We locked down SQL injection decades ago. We built linters, scanners, automated pipelines that scream before bad code ever ships. And yet, right now, most teams pushing AI features are sending system prompts into production completely untested for injection attacks. That gap is starting to look a lot like the early 2000s all over again.
A new tool just shipped that targets exactly this blind spot. The author, u/MomentInfinite2940, originally built a web-based prompt security scanner, but the community pushed back with a fair question: what about wiring this into a deploy pipeline? So this Redditor built the API version, and it’s about as lean as a security tool gets.
What It Actually Does
The core idea is simple. You POST your system prompt to one endpoint. You get back a structured JSON response that tells you exactly how exposed that prompt is. Here’s what the response includes:
- 🔢 An overall security score from 0 to 1
- ⚙️ Results from 15 different attack patterns, all run in parallel
- Attack category labels: jailbreak, role hijack, data extraction, instruction override, context manipulation
- Pass/fail status for each attack, with details on what went wrong
- Clean JSON output that plugs into virtually any pipeline
The parallel testing matters. Running 15 attack simulations sequentially would make this too slow for CI/CD. Running them together keeps latency low enough to actually use in an automated workflow.
The Twist: It’s Already GitHub Actions-Ready
The creator didn’t just build an API and leave you to figure out the integration. The post walks through a practical GitHub Actions pattern: add a step after deployment, POST your system prompt, parse the security_score field from the response, and fail the build if that score drops below whatever threshold makes sense for your risk tolerance.
That’s a meaningful shift. Prompt security stops being a manual audit you run occasionally and becomes a hard gate in your shipping process. If someone edits a system prompt and accidentally introduces a role hijack vulnerability, the build catches it before users do.
How to Use It
🔧 Here’s the basic workflow the author describes:
- Take your system prompt (the one you’re shipping to production)
- Send a POST request to the scanner endpoint with the prompt in the body
- Parse the JSON response for
security_scoreand individual attack results - Set a threshold (the author doesn’t prescribe one, which is smart: your tolerance depends on your use case)
- Fail the pipeline if the score is below that threshold or specific attack categories fail
For pricing, there are two tiers. The free tier requires no API key and gives you access to run scans without any setup. The BYOK option lets you pass your own OpenRouter API key via the x-api-key header for unlimited scans at roughly $0.02 to $0.03 per scan on your own key.
Privacy Is Handled Thoughtfully
System prompts are often the IP of an AI product. The creator is explicit about this: your API key and system prompt are never stored or logged. Everything is processed in memory, results are returned, and the data is discarded. Traffic is HTTPS encrypted in transit.
That’s not just a nice-to-have. For teams working on proprietary AI features, this is the difference between using a tool and not using it.
Pro Tips Worth Noting
💡 A few things to think about as you evaluate this:
- Threshold calibration matters. A score cutoff that works for a customer-facing chatbot might be too strict or too loose for an internal tool. Start with a soft threshold and observe before making it a hard block.
- Category-level filtering is useful. You might care more about data extraction failures than instruction override results, depending on what your prompt has access to. The per-attack categories let you build logic around that.
- BYOK scales cleanly. At $0.02 to $0.03 per scan, running this on every PR against a staging prompt is genuinely affordable for most teams.
One honest caveat: 15 attack patterns is a solid starting set, but prompt injection is a moving target. No static scanner catches everything, and this tool should be one layer of a broader security posture, not the whole thing.
How It Compares
Most prompt security work today is manual, like red-teaming sessions, occasional adversarial testing, or ad hoc audits. Tools like Garak exist for more comprehensive LLM vulnerability testing but come with setup overhead. This scanner is positioning itself as the lightweight, pipeline-native option: one endpoint, JSON out, no configuration required to get started.
If you’ve used dependency vulnerability scanners in your code pipelines, the mental model here is similar. It’s not a full penetration test. It’s a fast, automated check that catches common issues before they ship.
The original Reddit discussion has the endpoint details, the GitHub Actions snippet, and the author is actively asking for feedback on the response format. If you’re already doing prompt security testing in a different way, that thread is worth jumping into.
Noticed nobody’s testing their AI prompts for injection attacks it’s the SQL injection era all over again
by u/MomentInfinite2940 in PromptEngineering