# Instruction Tuning for Secure Code Generation

arXiv: https://arxiv.org/abs/2402.09497 · HTML: https://mlsanity.com/paper/2402.09497/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [OctoPack: Instruction Tuning Code Large Language Models](https://mlsanity.com/paper/2308.07124/) (2023)
- [Large Language Models for Code: Security Hardening and Adversarial Testing](https://mlsanity.com/paper/2302.05319/) (2023)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [Data Quality for Software Vulnerability Datasets](https://mlsanity.com/paper/2301.05456/) (2023)
- [VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python](https://mlsanity.com/paper/2201.08441/) (2022)
- [SourceFinder: Finding Malware Source-Code from Publicly Available Repositories](https://mlsanity.com/paper/2005.14311/) (2020)

## Built on by (most cited first, 30 of 68)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Exploiting LLM Quantization](https://mlsanity.com/paper/2405.18137/) (2024)
- [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025)
- [SWE-chat: Coding Agent Interactions From Real Users in the Wild](https://mlsanity.com/paper/2604.20779/) (2026)
- [BaxBench: Can LLMs Generate Correct and Secure Backends?](https://mlsanity.com/paper/2502.11844/) (2025)
- [Constrained Decoding for Secure Code Generation](https://mlsanity.com/paper/2405.00218/) (2024)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking](https://mlsanity.com/paper/2407.02395/) (2024)
- [MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation](https://mlsanity.com/paper/2405.11430/) (2024)
- [ProSec: Fortifying Code LLMs with Proactive Security Alignment](https://mlsanity.com/paper/2411.12882/) (2024)
- [PurpCode: Reasoning for Safer Code Generation](https://mlsanity.com/paper/2507.19060/) (2025)
- [HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data](https://mlsanity.com/paper/2409.06446/) (2024)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Black-Box Adversarial Attacks on LLM-Based Code Completion](https://mlsanity.com/paper/2408.02509/) (2024)
- [SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios](https://mlsanity.com/paper/2509.22097/) (2025)
- [Co-PatcheR: Collaborative Software Patching with Component(s)-specific Small Reasoning Models](https://mlsanity.com/paper/2505.18955/) (2025)
- [Training Language Models to Generate Quality Code with Program Analysis Feedback](https://mlsanity.com/paper/2505.22704/) (2025)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [Rethinking the Evaluation of Secure Code Generation](https://mlsanity.com/paper/2503.15554/) (2025)
- [Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences](https://mlsanity.com/paper/2506.00419/) (2025)
- [INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness](https://mlsanity.com/paper/2407.02518/) (2024)
- [The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models](https://mlsanity.com/paper/2505.02931/) (2025)
- [RESCUE: Retrieval Augmented Secure Code Generation](https://mlsanity.com/paper/2510.18204/) (2025)
- [Helping LLMs Improve Code Generation Using Feedback from Testing and Static Analysis](https://mlsanity.com/paper/2412.14841/) (2024)
- [CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval](https://mlsanity.com/paper/2506.11066/) (2025)
- [Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model](https://mlsanity.com/paper/2602.07422/) (2026)
- [AutoBaxBuilder: Bootstrapping Code Security Benchmarking](https://mlsanity.com/paper/2512.21132/) (2025)
- [SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI](https://mlsanity.com/paper/2410.11096/) (2024)
- [SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows](https://mlsanity.com/paper/2506.07313/) (2025)
- [Large Language Model Unlearning for Source Code](https://mlsanity.com/paper/2506.17125/) (2025)

## Abstract (arXiv)

Modern language models (LMs) have gained widespread acceptance in everyday and professional contexts, particularly in programming. An essential procedure enabling this adoption is instruction tuning, which substantially enhances LMs' practical utility by training them to follow user instructions and human preferences. However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code. As a result, even the state-of-the-art instruction-tuned LMs frequently produce unsafe code, posing significant security risks. In this work, we introduce SafeCoder to address this gap. SafeCoder performs security-centric fine-tuning using a diverse and high-quality dataset that we collected using an automated pipeline. We integrate the security fine-tuning with standard instruction tuning, to facilitate a joint optimization of both security and utility. Despite its simplicity, we show that SafeCoder is effective across a variety of popular LMs and datasets. It is able to drastically improve security (by about 30%), while preserving utility.