Secure & Audit
Prompt Injection Defense
Multi-layer defense system detecting adversarial LLM attacks with 98.9% accuracy at under 200ms latency.
The problem
LLM apps are exposed to prompt injection, jailbreaks, and data exfiltration through untrusted user input, with no systematic defense in place.
- A support bot gets talked into ignoring its instructions by a cleverly worded ticket.
- You find out about a jailbreak from a screenshot on social media, not your own logs.
- There's no gate between "user typed something" and "model acted on it."
What NEO built
NEO built a multi-layer defense (input sanitization, PII detection, pattern matching against 500+ known attack vectors, and a fine-tuned classifier for semantic analysis) wired into real-time scoring and output validation.
The result
98.9% detection accuracy
Reaches 98.9% detection accuracy at under 200ms latency overhead, with a low false-positive rate in production.
From the blog · 8 min
How NEO Built a Prompt Injection Defense System with 98.9% Detection Accuracy
NEO built a production-ready, multi-layer prompt injection defense system that detects adversarial LLM attacks with 98.9% accuracy and under 200ms latency. Here's how it works.
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Add a defense layer in front of my LLM app that catches prompt injection and PII exfiltration attempts in real time, before they reach the model.
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