What 78K attack samples taught me about catching prompt injection
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I spent the last while building a prompt-injection detector trained on 78,000+ attack samples. Here's what surprised me, and why I ended up going the unfashionable route. The trendy approach is to use an LLM. I didn't. The default move in 2026 is "use an LLM to judge whether input is an attack." It's appealing because models understand nuance. But once you try to run it inline on every request, the problems pile up fast: Latency. You've added a full model round-trip to every single call.…
1Key Takeaways
- I spent the last while building a prompt-injection detector trained on 78,000+ attack samples.
- Here's what surprised me, and why I ended up going the unfashionable route.
- The trendy approach is to use an LLM.
- The default move in 2026 is "use an LLM to judge whether input is an attack." It's appealing because models understand nuance.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i spent the last while building a prompt-injection detector trained on 78,000+ attack samples.
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