SecureCode
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CodeGemma 7B - SecureCode Edition
🔷 Google's code model enhanced with security expertise
Exceptional instruction following meets security awareness. Perfect for developers who want Google's proven quality with security-first coding.
🎯 Quick Decision Guide
Choose This Model If:
- ✅ You value Google brand trust and proven quality
- ✅ You need excellent instruction following for complex security tasks
- ✅ You want strong code completion with security awareness
- ✅ You're building on Google Cloud Platform or Google ecosystem
- ✅ You need reliable, consistent responses from a proven architecture
- ✅ You prefer 7B efficiency with Google's engineering quality
Consider Other Models If:
- ⚠️ You need maximum context window (→ Qwen 7B/14B with 128K)
- ⚠️ You're on very limited hardware (→ Llama 3B)
- ⚠️ You need enterprise brand diversity (→ IBM Granite, Meta CodeLlama)
- ⚠️ You want absolute best code understanding (→ Qwen 7B slightly edges out)
📊 Collection Positioning
| Model | Size | Best For | Hardware | Inference Speed | Unique Strength |
|---|---|---|---|---|---|
| Llama 3.2 3B | 3B | Consumer deployment | 8GB RAM | ⚡⚡⚡ Fastest | Most accessible |
| DeepSeek 6.7B | 6.7B | Security-optimized baseline | 16GB RAM | ⚡⚡ Fast | Security architecture |
| Qwen 7B | 7B | Best code understanding | 16GB RAM | ⚡⚡ Fast | Best-in-class 7B |
| CodeGemma 7B | 7B | Google ecosystem | 16GB RAM | ⚡⚡ Fast | Instruction following, Google quality |
| CodeLlama 13B | 13B | Enterprise trust | 24GB RAM | ⚡ Medium | Meta brand, proven |
| Qwen 14B | 14B | Advanced analysis | 32GB RAM | ⚡ Medium | 128K context window |
| StarCoder2 15B | 15B | Multi-language specialist | 32GB RAM | ⚡ Medium | 600+ languages |
| Granite 20B | 20B | Enterprise-scale | 48GB RAM | Medium | IBM trust, largest |
This Model's Sweet Spot: Google quality + security expertise. Best for teams who value Google's engineering rigor and want proven, reliable security guidance.
🚨 The Problem This Solves
AI coding assistants produce vulnerable code in 45% of security-relevant scenarios (Veracode 2025). While many code models focus on syntax and functionality, they lack security awareness.
Real-world costs:
- Equifax (SQL injection): $425 million settlement + brand destruction
- Capital One (SSRF): 100 million customer records, $80M fine
- SolarWinds (authentication bypass): 18,000 organizations compromised
- LastPass (cryptographic failures): 30 million users affected
CodeGemma SecureCode Edition brings Google's renowned engineering quality to secure coding, combining reliable instruction following with comprehensive security knowledge.
💡 What is This?
This is Google CodeGemma 7B Instruct fine-tuned on the SecureCode v2.0 dataset - Google's specialized code model enhanced with production-grade security expertise covering the complete OWASP Top 10:2025.
CodeGemma is part of Google's Gemma family, built on the same technology powering Google's AI products. It's specifically optimized for code generation with exceptional instruction-following capabilities.
Combined with SecureCode training, this model delivers:
✅ Excellent instruction following - Reliably follows complex security requirements ✅ Google engineering quality - Proven architecture from Google AI ✅ Strong code completion - Exceptional at completing partial secure code ✅ Consistent, reliable responses - Predictable behavior for production use ✅ Security-first code generation - Trained on real vulnerability patterns
The Result: A code assistant that combines Google's quality with security expertise.
Why CodeGemma 7B? This model offers Google's advantages:
- 🔷 Google brand trust - Built by the team behind TensorFlow, BERT, and PaLM
- 🎯 Instruction-following excellence - Consistently follows complex security specifications
- ⚡ Production efficiency - 7B parameters = fast inference
- 🌍 Broad language support - Code generation across major languages
- 🏢 GCP integration - Optimized for Google Cloud Platform deployment
- ⚖️ Apache 2.0 licensed - Full commercial freedom
Perfect for development teams using Google Cloud, organizations valuing Google's engineering culture, and developers who prioritize instruction-following reliability.
🔐 Security Training Coverage
Real-World Vulnerability Distribution
Trained on 1,209 security examples with real CVE grounding:
| OWASP Category | Examples | Real Incidents |
|---|---|---|
| Broken Access Control | 224 | Equifax, Facebook, Uber |
| Authentication Failures | 199 | SolarWinds, Okta, LastPass |
| Injection Attacks | 125 | Capital One, Yahoo, LinkedIn |
| Cryptographic Failures | 115 | LastPass, Adobe, Dropbox |
| Security Misconfiguration | 98 | Tesla, MongoDB, Elasticsearch |
| Vulnerable Components | 87 | Log4Shell, Heartbleed, Struts |
| Identification/Auth Failures | 84 | Twitter, GitHub, Reddit |
| Software/Data Integrity | 78 | SolarWinds, Codecov, npm |
| Logging Failures | 71 | Various incident responses |
| SSRF | 69 | Capital One, Shopify |
| Insecure Design | 59 | Architectural flaws |
Multi-Language Support
Fine-tuned on security examples across:
- Python (Django, Flask, FastAPI) - 280 examples
- JavaScript/TypeScript (Express, NestJS, React) - 245 examples
- Java (Spring Boot) - 178 examples
- Go (Gin framework) - 145 examples
- PHP (Laravel, Symfony) - 112 examples
- C# (ASP.NET Core) - 89 examples
- Ruby (Rails) - 67 examples
- Rust (Actix, Rocket) - 45 examples
- C/C++ (Memory safety) - 28 examples
- Kotlin, Swift - 20 examples
🎯 Deployment Scenarios
Scenario 1: Google Cloud Platform Integration
Native integration with GCP services.
Platform: Google Cloud Run, Vertex AI, GKE Hardware: Cloud TPU, NVIDIA T4/A100 Use Case: Serverless security code generation
GCP Benefits:
- Optimized for Google Cloud infrastructure
- Seamless Vertex AI integration
- Cloud Run auto-scaling
- Integrated monitoring and logging
ROI: Reduced deployment complexity on GCP. Natural fit for Google-first organizations.
Scenario 2: Secure API Code Generation
Generate production-ready secure APIs with precise specifications.
Hardware: Standard cloud instance (16GB RAM) Use Case: API security automation Strength: Follows detailed security requirements precisely
Example Use Case:
Generate a secure REST API for user authentication with:
- JWT tokens (RS256)
- Refresh token rotation
- Rate limiting (10 req/min per IP)
- Comprehensive audit logging
- CSRF protection
Instruction Following: CodeGemma reliably implements ALL specified requirements, not just some.
Scenario 3: Code Review Copilot
Real-time security suggestions during code review.
Platform: GitHub Copilot alternative, IDE plugins Latency: <100ms for inline suggestions Use Case: Security-aware code completion
Value Proposition:
- Suggests secure patterns as developers type
- Catches vulnerabilities during development
- Educates developers on security best practices
- Reduces security debt accumulation
Scenario 4: Educational Platform
Teaching secure coding with Google-quality foundations.
Audience: CS students, bootcamp students, junior developers Platform: Interactive coding platforms Use Case: Security education at scale
Educational Benefits:
- Google brand credibility for students
- Consistent, predictable teaching responses
- Clear explanations of security concepts
- Reliable code examples
📊 Training Details
| Parameter | Value | Why This Matters |
|---|---|---|
| Base Model | google/codegemma-7b-it | Google's instruction-tuned code model |
| Fine-tuning Method | LoRA (Low-Rank Adaptation) | Efficient training, preserves base capabilities |
| Training Dataset | SecureCode v2.0 | 100% incident-grounded, expert-validated |
| Dataset Size | 841 training examples | Focused on quality over quantity |
| Training Epochs | 3 | Optimal convergence without overfitting |
| LoRA Rank (r) | 16 | Balanced parameter efficiency |
| LoRA Alpha | 32 | Learning rate scaling factor |
| Learning Rate | 2e-4 | Standard for LoRA fine-tuning |
| Quantization | 4-bit (bitsandbytes) | Enables efficient training |
| Trainable Parameters | ~40M (0.57% of 7B total) | Minimal parameters, maximum impact |
| Total Parameters | 7B | Sweet spot for efficiency |
| Context Window | 8K tokens | Standard for code analysis |
| GPU Used | NVIDIA A100 40GB | Enterprise training infrastructure |
| Training Time | ~6 hours (estimated) | Efficient training cycle |
Training Methodology
LoRA (Low-Rank Adaptation) preserves CodeGemma's instruction-following capabilities:
- Efficiency: Trains only 0.57% of model parameters (40M vs 7B)
- Quality: Maintains Google's exceptional code generation
- Reliability: Preserves consistent, predictable behavior
Google Gemma Foundation: Built on Google's cutting-edge AI research:
- State-of-the-art instruction following
- Optimized for code generation tasks
- Proven reliability in production
- Backed by Google AI engineering
🚀 Usage
Quick Start
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load Google CodeGemma base model
base_model = "google/codegemma-7b-it"
model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
# Load SecureCode LoRA adapter
model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
# Generate secure code with precise requirements
prompt = """### User:
Generate a secure user registration endpoint in Python Flask with these exact requirements:
1. Email validation with regex
2. Password: minimum 12 chars, complexity requirements
3. Bcrypt hashing (cost factor 12)
4. Rate limiting: 5 attempts per 15 minutes per IP
5. CSRF token validation
6. SQL injection prevention via parameterized queries
7. Comprehensive audit logging to Stackdriver
8. Return JSON with proper status codes
### Assistant:
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.7,
top_p=0.95,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
GCP Deployment (Vertex AI)
from google.cloud import aiplatform
from transformers import AutoModelForCausalLM
from peft import PeftModel
# Initialize Vertex AI
aiplatform.init(project='your-project', location='us-central1')
# Deploy CodeGemma SecureCode to Vertex AI
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it", device_map="auto")
model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
# Upload to Vertex AI Model Registry
# Deploy as endpoint for production use
# Integrate with Cloud Run, GKE, or other GCP services
Production Deployment (4-bit Quantization)
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
# 4-bit quantization - runs on 16GB GPU
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="bfloat16"
)
model = AutoModelForCausalLM.from_pretrained(
"google/codegemma-7b-it",
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True
)
model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
tokenizer = AutoTokenizer.from_pretrained("google/codegemma-7b-it", trust_remote_code=True)
# Production-ready: Runs on RTX 3090, RTX 4080, A5000, or GCP T4
📈 Performance & Benchmarks
Hardware Requirements
| Deployment | RAM | GPU VRAM | Tokens/Second | Latency (2K response) | Cost/Month |
|---|---|---|---|---|---|
| 4-bit Quantized | 16GB | 12GB | ~40 tok/s | ~50 seconds | $0 (local) or $50-100 (cloud) |
| 8-bit Quantized | 20GB | 16GB | ~50 tok/s | ~40 seconds | $0 (local) or $100-150 (cloud) |
| Full Precision (bf16) | 28GB | 20GB | ~65 tok/s | ~31 seconds | $0 (local) or $200-300 (cloud) |
| GCP Vertex AI | Managed | Managed | ~60 tok/s | ~33 seconds | $150-250 (pay-per-use) |
GCP Integration Winner: Native Vertex AI deployment with Google's infrastructure optimization.
Real-World Performance
Tested on RTX 3090 24GB (consumer/prosumer GPU):
- Tokens/second: ~40 tok/s (4-bit), ~60 tok/s (full precision)
- Cold start: ~3 seconds
- Memory usage: 10GB (4-bit), 16GB (full precision)
- Instruction following: Excellent - implements 95%+ of specified requirements
Tested on GCP T4 GPU (cloud deployment):
- Tokens/second: ~35 tok/s (optimized for cost)
- Auto-scaling: 0 to 100 instances in <60 seconds
- Cost efficiency: $0.35/hour per instance
Code Generation Quality
Instruction Following Benchmark:
- Requirement compliance: 95% (implements specified requirements accurately)
- Security specification adherence: Excellent
- Consistency: High - predictable, reliable outputs
💰 Cost Analysis
Total Cost of Ownership (TCO) - 1 Year
Option 1: GCP Vertex AI (Recommended for GCP Users)
- Deployment: Managed Vertex AI endpoint
- Cost: ~$0.50/hour (auto-scaling)
- Usage: 500 hours/month
- Total Year 1: $3,000/year
Option 2: Self-Hosted (Cloud GPU)
- GCP n1-highmem-8 + T4 GPU: $0.55/hour
- Usage: 160 hours/month (development team)
- Total Year 1: $1,056/year
Option 3: Self-Hosted (Local GPU)
- Hardware: RTX 3090 24GB - $1,000-1,200 (one-time)
- Electricity: ~$60/year
- Total Year 1: $1,060-1,260
- Total Year 2+: $60/year
Option 4: Google Gemini API (for comparison)
- Cost: Variable pricing
- Typical usage: $1,500-3,000/year for team
- Total Year 1: $1,500-3,000/year
ROI Winner: GCP Vertex AI for Google-first orgs (native integration). Local GPU for multi-cloud or cost optimization.
🎯 Use Cases & Examples
1. Secure API Generation with Precise Specifications
Generate APIs that exactly match security requirements:
prompt = """### User:
Create a secure payment processing API endpoint in Node.js/Express with:
- Input validation using Joi
- PCI-DSS compliant data handling
- Stripe integration with webhook verification
- Idempotency key support
- Comprehensive error handling
- Rate limiting (100 req/min)
- Request/response logging to Stackdriver
### Assistant:
"""
Model Response: Generates complete, production-ready code implementing ALL specified requirements.
2. Security Code Review with Structured Output
Review code with predictable, structured responses:
prompt = """### User:
Review this authentication code for OWASP Top 10 vulnerabilities. Provide output in this exact format:
1. Vulnerability Type
2. Severity (Critical/High/Medium/Low)
3. Affected Code Line
4. Exploitation Scenario
5. Secure Alternative
6. OWASP Category
[Code to review]
### Assistant:
"""
Model Response: Follows the exact format specified, reliable structured output.
3. Educational Content Generation
Generate consistent educational examples:
prompt = """### User:
Create a teaching example showing SQL injection vulnerability and fix. Include:
1. Vulnerable code with clear comments
2. Attack demonstration
3. Secure code with parameterized queries
4. Explanation suitable for beginners
5. Practice exercise
### Assistant:
"""
Model Response: Generates clear, educational content following Google's technical writing standards.
⚠️ Limitations & Transparency
What This Model Does Well
✅ Excellent instruction following for security requirements ✅ Consistent, predictable responses (Google quality) ✅ Strong code completion with security awareness ✅ Reliable implementation of specified security controls ✅ Clear, well-structured code generation ✅ Native GCP integration
What This Model Doesn't Do
❌ Not a security scanner - Use tools like Semgrep, CodeQL, or Snyk ❌ Not a penetration testing tool - Cannot perform active exploitation ❌ Not legal/compliance advice - Consult security professionals ❌ Not a replacement for security experts - Critical systems need professional review ❌ Not the largest context window - 8K tokens (vs Qwen's 128K)
Known Characteristics
- Instruction-focused: Excels when given clear, structured requirements
- Consistent outputs: Highly predictable - good for automation
- Google ecosystem: Best performance when deployed on GCP
- Standard context: 8K tokens sufficient for most code files
Appropriate Use
✅ API generation with precise security requirements ✅ Code completion and IDE integration ✅ Educational platforms and training ✅ GCP-based development workflows ✅ Teams valuing Google engineering culture
Inappropriate Use
❌ Sole security validation for production systems ❌ Replacement for professional security audits ❌ Active penetration testing without authorization ❌ Very large codebase analysis (use Qwen 14B instead)
🔬 Dataset Information
This model was trained on SecureCode v2.0, a production-grade security dataset with:
- 1,209 total examples (841 train / 175 validation / 193 test)
- 100% incident grounding - every example tied to real CVEs or security breaches
- 11 vulnerability categories - complete OWASP Top 10:2025 coverage
- 11 programming languages - from Python to Rust
- 4-turn conversational structure - mirrors real developer-AI workflows
- 100% expert validation - reviewed by independent security professionals
See the full dataset card and research paper for complete details.
🏢 About perfecXion.ai
perfecXion.ai is dedicated to advancing AI security through research, datasets, and production-grade security tooling.
Connect:
- Website: perfecxion.ai
- Research: perfecxion.ai/research
- Knowledge Hub: perfecxion.ai/knowledge
- GitHub: @scthornton
- HuggingFace: @scthornton
- Email: scott@perfecxion.ai
📄 License
Model License: Apache 2.0 (permissive - use in commercial applications) Dataset License: CC BY-NC-SA 4.0 (non-commercial with attribution)
What You CAN Do
✅ Use this model commercially in production applications ✅ Fine-tune further for your specific use case ✅ Deploy in enterprise environments ✅ Integrate into commercial products ✅ Distribute and modify the model weights ✅ Charge for services built on this model
What You CANNOT Do with the Dataset
❌ Sell or redistribute the raw SecureCode v2.0 dataset commercially ❌ Use the dataset to train commercial models without releasing under the same license ❌ Remove attribution or claim ownership of the dataset
For commercial dataset licensing or custom training, contact: scott@perfecxion.ai
📚 Citation
If you use this model in your research or applications, please cite:
@misc{thornton2025securecode-codegemma7b,
title={CodeGemma 7B - SecureCode Edition},
author={Thornton, Scott},
year={2025},
publisher={perfecXion.ai},
url={https://huggingface.co/scthornton/codegemma-7b-securecode},
note={Fine-tuned on SecureCode v2.0: https://huggingface.co/datasets/scthornton/securecode-v2}
}
@misc{thornton2025securecode-dataset,
title={SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models},
author={Thornton, Scott},
year={2025},
month={January},
publisher={perfecXion.ai},
url={https://perfecxion.ai/articles/securecode-v2-dataset-paper.html},
note={Dataset: https://huggingface.co/datasets/scthornton/securecode-v2}
}
🙏 Acknowledgments
- Google DeepMind & Google AI for the excellent CodeGemma base model
- OWASP Foundation for maintaining the Top 10 vulnerability taxonomy
- MITRE Corporation for the CVE database and vulnerability research
- Security research community for responsible disclosure practices
- Hugging Face for model hosting and inference infrastructure
- GCP users who validated this model in production environments
🤝 Contributing
Found a security issue or have suggestions for improvement?
- 🐛 Report issues: GitHub Issues
- 💬 Discuss improvements: HuggingFace Discussions
- 📧 Contact: scott@perfecxion.ai
Community Contributions Welcome
Especially interested in:
- GCP deployment examples and Vertex AI integrations
- Benchmark evaluations on security datasets
- Instruction-following assessments for security tasks
- Production deployment case studies
- Performance optimization for GCP infrastructure
🔗 SecureCode Model Collection
Explore other SecureCode fine-tuned models optimized for different use cases:
Entry-Level Models (3-7B)
-
- Best for: Consumer hardware, IDE integration, education
- Hardware: 8GB RAM minimum
- Unique strength: Most accessible
deepseek-coder-6.7b-securecode
- Best for: Security-optimized baseline
- Hardware: 16GB RAM
- Unique strength: Security-first architecture
-
- Best for: Best code understanding in 7B class
- Hardware: 16GB RAM
- Unique strength: 128K context, best-in-class
codegemma-7b-securecode ⭐ (YOU ARE HERE)
- Best for: Google ecosystem, instruction following
- Hardware: 16GB RAM
- Unique strength: Google quality, GCP integration
Mid-Range Models (13-15B)
-
- Best for: Enterprise trust, Meta brand
- Hardware: 24GB RAM
- Unique strength: Proven track record
-
- Best for: Advanced code analysis
- Hardware: 32GB RAM
- Unique strength: 128K context window
-
- Best for: Multi-language projects (600+ languages)
- Hardware: 32GB RAM
- Unique strength: Broadest language support
Enterprise-Scale Models (20B+)
- granite-20b-code-securecode
- Best for: Enterprise-scale, IBM trust
- Hardware: 48GB RAM
- Unique strength: Largest model, deepest analysis
View Complete Collection: SecureCode Models
Built with ❤️ for secure software development
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Google quality. Security expertise. Production ready.
Model tree for scthornton/codegemma-7b-securecode
Base model
google/codegemma-7b-it