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IntentGuard: Building a Production-Grade Vertical Intent Classifier for LLM Safety
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perfecXion
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# IntentGuard: Open-Source Vertical Intent Classifiers for LLM Guardrails
Three models published to the Hub:
- [perfecXion/intentguard-finance](https://huggingface.co/perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare](https://huggingface.co/perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal](https://huggingface.co/perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
```bash
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:finance-latest
curl -X POST http://localhost:8080/v1/classify \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "What are current mortgage rates?"}]}'
```
Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
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upvoted an article about 6 hours ago
published an
article about 9 hours ago
Article
IntentGuard: Building a Production-Grade Vertical Intent Classifier for LLM Safety
•
2
reacted to their post with 👍 5 days ago
Post
2523
# IntentGuard: Open-Source Vertical Intent Classifiers for LLM Guardrails
Three models published to the Hub:
- [perfecXion/intentguard-finance]( perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare]( perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal]( perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
Three models published to the Hub:
- [perfecXion/intentguard-finance]( perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare]( perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal]( perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:finance-latest
curl -X POST http://localhost:8080/v1/classify \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "What are current mortgage rates?"}]}'Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
posted an
update 7 days ago
Post
2523
# IntentGuard: Open-Source Vertical Intent Classifiers for LLM Guardrails
Three models published to the Hub:
- [perfecXion/intentguard-finance]( perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare]( perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal]( perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
Three models published to the Hub:
- [perfecXion/intentguard-finance]( perfecXion/intentguard-finance)
- [perfecXion/intentguard-healthcare]( perfecXion/intentguard-healthcare)
- [perfecXion/intentguard-legal]( perfecXion/intentguard-legal)
DeBERTa-v3-xsmall fine-tuned for three-way classification: **allow**, **deny**, or **abstain**. ONNX + INT8 quantized, under 80MB, p99 <30ms on CPU. Margin-based thresholds (not argmax) — uncertain queries route to clarification instead of forcing a guess.
**Eval results (adversarial test sets, ~470-480 examples per vertical):**
| Vertical | Accuracy | Legit-Block Rate | Off-Topic-Pass Rate |
|----------|----------|------------------|---------------------|
| Finance | 99.6% | 0.00% | 0.00% |
| Healthcare | 98.9% | 0.00% | 0.98% |
| Legal | 97.9% | 0.00% | 0.50% |
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:finance-latest
curl -X POST http://localhost:8080/v1/classify \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "What are current mortgage rates?"}]}'Apache 2.0. Full pipeline + Docker configs on [GitHub](https://github.com/perfecxion-ai/intentguard).
Feedback welcome on domain coverage, adversarial robustness, and multilingual demand.
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upvoted an article 14 days ago
Article
AI Coding Assistants Keep Shipping Vulnerable Code -- Here's What We're Doing About It
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3
reacted to scthornton's post with 👀🚀 14 days ago
Post
1881
# SecureCode Dataset Family Update: 2,185 Security Examples, Framework-Specific Patterns, Clean Parquet Loading
Hey y'all,
Quick update on the SecureCode dataset family. We've restructured things and fixed several issues:
**What changed:**
- The datasets are now properly split into three repos: [unified]( scthornton/securecode) (2,185), [web]( scthornton/securecode-web) (1,378), [AI/ML]( scthornton/securecode-aiml) (750)
- All repos now use Parquet format --
- SecureCode Web now includes 219 framework-specific examples (Express, Django, Spring Boot, Flask, Rails, Laravel, ASP.NET Core, FastAPI, NestJS)
- Data cards have been corrected and split sizes fixed
**Why it matters:**
With AI-generated code accounting for 60%+ of some codebases (Checkmarx 2025), security training data is more important than ever. Every example in SecureCode is grounded in a real CVE with 4-turn conversations that mirror actual developer-AI workflows.
If you're working on code generation models, I'd love to hear how you're approaching the security angle. Are there vulnerability categories or frameworks you'd like to see covered?
Paper: [arxiv.org/abs/2512.18542](https://arxiv.org/abs/2512.18542)
Hey y'all,
Quick update on the SecureCode dataset family. We've restructured things and fixed several issues:
**What changed:**
- The datasets are now properly split into three repos: [unified]( scthornton/securecode) (2,185), [web]( scthornton/securecode-web) (1,378), [AI/ML]( scthornton/securecode-aiml) (750)
- All repos now use Parquet format --
load_dataset() just works, no deprecated loading scripts- SecureCode Web now includes 219 framework-specific examples (Express, Django, Spring Boot, Flask, Rails, Laravel, ASP.NET Core, FastAPI, NestJS)
- Data cards have been corrected and split sizes fixed
**Why it matters:**
With AI-generated code accounting for 60%+ of some codebases (Checkmarx 2025), security training data is more important than ever. Every example in SecureCode is grounded in a real CVE with 4-turn conversations that mirror actual developer-AI workflows.
If you're working on code generation models, I'd love to hear how you're approaching the security angle. Are there vulnerability categories or frameworks you'd like to see covered?
Paper: [arxiv.org/abs/2512.18542](https://arxiv.org/abs/2512.18542)
liked 3 datasets about 1 month ago
Convert to Parquet format (fixes datasets >= 4.0.0 compatibility) Fixes https://huggingface.co/datasets/scthornton/securecode-v2/discussions/2 - Removes deprecated loading script - Uses modern Parquet format - Aligns schema across all splits - Compatible with datasets >= 4.0.0
1
#3 opened about 1 month ago
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perfecXion
Add paper citation and comprehensive model card
#2 opened about 2 months ago
by
perfecXion
Add paper citation and comprehensive model card
#2 opened about 2 months ago
by
perfecXion
Add paper citation and comprehensive model card
#2 opened about 2 months ago
by
perfecXion