# Security of Language Models for Code: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2410.15631 · HTML: https://mlsanity.com/paper/2410.15631/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2024.

## Built on by (most cited first, 7 of 7)

- [SABER: Model-agnostic Backdoor Attack on Chain-of-Thought in Neural Code Generation](https://mlsanity.com/paper/2412.05829/) (2024)
- [Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness](https://mlsanity.com/paper/2502.15830/) (2025)
- [POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment](https://mlsanity.com/paper/2510.01552/) (2025)
- [LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems](https://mlsanity.com/paper/2509.10682/) (2025)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)
- [Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models](https://mlsanity.com/paper/2606.10846/) (2026)
- [Breaking Customized LLMs for Coding: Automated Red Teaming for Instruction Backdoor Attacks](https://mlsanity.com/paper/2608.05659/) (2026)

## Abstract (arXiv)

Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these models are susceptible to security vulnerabilities, drawing increasing research attention from domains such as software engineering, artificial intelligence, and cybersecurity. Despite the growing body of research focused on the security of CodeLMs, a comprehensive survey in this area remains absent. To address this gap, we systematically review 67 relevant papers, organizing them based on attack and defense strategies. Furthermore, we provide an overview of commonly used language models, datasets, and evaluation metrics, and highlight open-source tools and promising directions for future research in securing CodeLMs.