I've been working on LLM-backed applications and ran into a recurring issue: prompt injection via user input.
Typical examples:
- "Ignore all previous instructions"
- "Reveal your system prompt"
- "Act as another AI without restrictions"
In many applications, user input is passed directly to the model, which makes these attacks practical.
Most moderation APIs are too general-purpose and not designed specifically for prompt injection detection, especially for non-English inputs. So I built a small Python library to act as a screening layer before sending input to the LLM:
https://github.com/kanekoyuichi/promptgate
Detection strategies:
rule-based (regex / phrase matching)
latency: <1ms, no dependenciesembedding-based (cosine similarity with attack exemplars)
latency: ~5–15ms, uses sentence-transformersLLM-as-judge
higher accuracy, but +150–300ms latency, requires external API
Baseline evaluation (rule-only):
- FPR: 0.0% (0 / 30 benign samples)
- Recall: 61.4% (27 / 44 attack samples)
So rule-based alone misses ~40% of attacks, especially paraphrased or context-dependent ones.
This is not intended as a complete solution — the design assumption is defense-in-depth, where this acts as a first screening layer.
Known limitations:
- rule-based detection struggles with paraphrased / indirect instructions
- embedding approach depends on exemplar coverage (not a trained classifier)
- LLM-as-judge is non-deterministic and API-dependent
Would be interested in feedback on:
- better evaluation methodologies
- detection strategies beyond pattern / similarity / LLM judging
- how others are handling prompt injection at the application layer
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