perfecXion (perfecxion.ai)

reacted to their post with 👍 about 2 months ago

Post

2563

# 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% |

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 about 2 months ago

Post

2563

# 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% |

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.

reacted to scthornton's post with 👀🚀 about 2 months ago

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1925

# 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 -- 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)

reacted to scthornton's post with 🔥👍 3 months ago

Post

2188

# SecureCode: Security-Aware Code Models

**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**

## The Problem

Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.

## What SecureCode Does

- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks

## Resources

| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |

## How to Test It

Copy and paste this prompt with your code:

You are a senior application security engineer. Review the code below.

Output: 
(1) findings with severity, 
(2) likely exploit scenarios (high level),
(3) secure rewrite,
(4) defense-in-depth recommendations, 
(5) regression tests/checks.

Code: `...`

## Dataset Coverage

SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns

## We Want Your Feedback

We're looking for real-world contributions:

- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered

**Please include**: language/framework + what the correct remediation looks like in your environment.

---

**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!

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