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scthornton/
starcoder2-15b-securecode

Name: starcoder2-15b-securecode
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StarCoder2 15B SecureCode

Parameters

Dataset

OWASP

Method

Security-specialized code model fine-tuned on the SecureCode dataset

Dataset | Paper (arXiv:2512.18542) | Model Collection | perfecXion.ai

What This Model Does

This model generates secure code when developers ask about building features. Instead of producing vulnerable implementations (like 45% of AI-generated code does), it:

Identifies the security risks in common coding patterns
Provides vulnerable and secure implementations side by side
Explains how attackers would exploit the vulnerability
Includes defense-in-depth guidance: logging, monitoring, SIEM integration, infrastructure hardening

The model was fine-tuned on 2,372 security training examples covering both traditional web security (OWASP Top 10 2021) and AI/ML security (OWASP LLM Top 10 2025).

Model Details


Base Model	StarCoder2 15B Instruct
Parameters	15B
Architecture	StarCoder2
Tier	Tier 3: Large Model
Method	bf16 LoRA (no quantization)
LoRA Rank	16 (alpha=32)
Target Modules	`q_proj, k_proj, v_proj, o_proj` (4 modules)
Training Data	scthornton/securecode (2,372 examples)
Hardware	NVIDIA DGX Spark GB10 (Blackwell, unified memory)

BigCode's flagship model trained on The Stack v2. Broad language coverage with strong code understanding.

Quick Start

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Optional: 4-bit quantization for low-VRAM inference (training was bf16 LoRA)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

base_model = AutoModelForCausalLM.from_pretrained(
    "bigcode/starcoder2-15b-instruct-v0.1",
    quantization_config=bnb_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("scthornton/starcoder2-15b-securecode")
model = PeftModel.from_pretrained(base_model, "scthornton/starcoder2-15b-securecode")

# Ask a security-relevant coding question
messages = [
    {"role": "user", "content": "How do I implement JWT authentication with refresh tokens in Python?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Dataset

Trained on the full SecureCode unified dataset:

2,372 total examples (1,625 web security + 747 AI/ML security)
20 vulnerability categories across OWASP Top 10 2021 and OWASP LLM Top 10 2025
12+ programming languages and 49+ frameworks
4-turn conversational structure: feature request, vulnerable/secure implementations, advanced probing, operational guidance
100% incident grounding: every example tied to real CVEs, vendor advisories, or published attack research

Hyperparameters

Parameter	Value
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	4 linear layers
Quantization	None (bf16 base weights)
Learning rate	2e-4
LR scheduler	Cosine with 100-step warmup
Epochs	3
Per-device batch size	1
Gradient accumulation	16x
Effective batch size	16
Max sequence length	4096 tokens
Optimizer	adamw_torch_fused
Attention	PyTorch SDPA (fused)
Precision	bf16

Notes: Compact LoRA targeting attention layers only (4 modules). Tight A100 40GB memory budget.

Security Coverage

Web Security (1,625 examples)

OWASP Top 10 2021: Broken Access Control, Cryptographic Failures, Injection, Insecure Design, Security Misconfiguration, Vulnerable Components, Authentication Failures, Software Integrity Failures, Logging/Monitoring Failures, SSRF.

Languages: Python, JavaScript, Java, Go, PHP, C#, TypeScript, Ruby, Rust, Kotlin, YAML.

AI/ML Security (747 examples)

OWASP LLM Top 10 2025: Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, Data/Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector/Embedding Weaknesses, Misinformation, Unbounded Consumption.

Frameworks: LangChain, OpenAI, Anthropic, HuggingFace, LlamaIndex, ChromaDB, Pinecone, FastAPI, Flask, vLLM, CrewAI, and 30+ more.

SecureCode Model Collection

This model is part of the SecureCode collection of 9 security-specialized models:

Model	Base	Size	Tier	HuggingFace
Llama 3.2 SecureCode	meta-llama/Llama-3.2-3B-Instruct	3B	Accessible	`llama-3.2-3b-securecode`
Gemma 4 E4B SecureCode	google/gemma-4-E4B-it	E4B (8B raw)	Accessible	`gemma-4-e4b-securecode`
Qwen2.5 Coder SecureCode	Qwen/Qwen2.5-Coder-7B-Instruct	7B	Mid-size	`qwen2.5-coder-7b-securecode`
DeepSeek Coder SecureCode	deepseek-ai/deepseek-coder-6.7b-instruct	6.7B	Mid-size	`deepseek-coder-6.7b-securecode`
CodeGemma SecureCode	google/codegemma-7b-it	7B	Mid-size	`codegemma-7b-securecode`
CodeLlama SecureCode	codellama/CodeLlama-13b-Instruct-hf	13B	Large	`codellama-13b-securecode`
Qwen2.5 Coder 14B SecureCode	Qwen/Qwen2.5-Coder-14B-Instruct	14B	Large	`qwen2.5-coder-14b-securecode`
StarCoder2 SecureCode	bigcode/starcoder2-15b-instruct-v0.1	15B	Large	`starcoder2-15b-securecode`
Granite 20B Code SecureCode	ibm-granite/granite-20b-code-instruct-8k	20B	XL	`granite-20b-code-securecode`

Choose based on your deployment constraints: 3B for edge/mobile, 7B for general use, 13B-15B for deeper reasoning, 20B for maximum capability.

SecureCode Dataset Family

Dataset	Examples	Focus	Link
SecureCode	2,372	Unified (web + AI/ML)	scthornton/securecode
SecureCode Web	1,625	Web security (OWASP Top 10 2021)	scthornton/securecode-web
SecureCode AI/ML	747	AI/ML security (OWASP LLM Top 10 2025)	scthornton/securecode-aiml

Intended Use

Use this model for:

Training AI coding assistants to write secure code
Security education and training
Vulnerability research and secure code review
Building security-aware development tools

Do not use this model for:

Offensive exploitation or automated attack generation
Circumventing security controls
Any activity that violates the base model's license

Changelog

2026-07 (v2, current): Retrained on the audited SecureCode release (2,372 examples: 1,625 web + 747 AI/ML) using bf16 LoRA on an NVIDIA DGX Spark GB10 (Blackwell). Same LoRA shape and hyperparameters as v1, except: no quantization during training, optimizer adamw_torch_fused instead of paged_adamw_8bit, PyTorch SDPA attention, dynamic per-batch padding. A new family member, gemma-4-e4b-securecode, was added in this refresh.
v1 (2026-02): QLoRA 4-bit NF4 on NVIDIA A100 40GB, trained on the pre-audit 2,185-example dataset.

Citation

bibtex

@misc{thornton2026securecode,
  title={SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models},
  author={Thornton, Scott},
  year={2026},
  publisher={perfecXion.ai},
  url={https://huggingface.co/datasets/scthornton/securecode},
  note={arXiv:2512.18542}
}

License

This model is released under the bigcode-openrail-m license (inherited from the base model). The training dataset (SecureCode) is licensed under CC BY-NC-SA 4.0.

scthornton/starcoder2-15b-securecode

StarCoder2 15B SecureCode

What This Model Does

Model Details

Quick Start

Training Details

Dataset

Hyperparameters

Security Coverage

Web Security (1,625 examples)

AI/ML Security (747 examples)

SecureCode Model Collection

SecureCode Dataset Family

Intended Use

Changelog

Citation

Links

License

scthornton/
starcoder2-15b-securecode