# SecCoder: Towards Generalizable and Robust Secure Code Generation

arXiv: https://arxiv.org/abs/2410.01488 · HTML: https://mlsanity.com/paper/2410.01488/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2024.

## Built on by (most cited first, 14 of 14)

- [PurpCode: Reasoning for Safer Code Generation](https://mlsanity.com/paper/2507.19060/) (2025)
- [Training Language Models to Generate Quality Code with Program Analysis Feedback](https://mlsanity.com/paper/2505.22704/) (2025)
- [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)
- [RESCUE: Retrieval Augmented Secure Code Generation](https://mlsanity.com/paper/2510.18204/) (2025)
- [CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval](https://mlsanity.com/paper/2506.11066/) (2025)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test](https://mlsanity.com/paper/2601.07084/) (2026)
- [DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation](https://mlsanity.com/paper/2604.09089/) (2026)
- [SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization](https://mlsanity.com/paper/2604.03587/) (2026)
- [Position: Intelligent Coding Systems Should Write Programs with Justifications](https://mlsanity.com/paper/2508.06017/) (2025)
- [Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms](https://mlsanity.com/paper/2603.11212/) (2026)
- [SoK: AI Secure Code Generation: Progress, Pitfalls, and Paths Forward](https://mlsanity.com/paper/2606.25195/) (2026)
- [MACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration](https://mlsanity.com/paper/2608.25457/) (2026)

## Abstract (arXiv)

After large models (LMs) have gained widespread acceptance in code-related tasks, their superior generative capacity has greatly promoted the application of the code LM. Nevertheless, the security of the generated code has raised attention to its potential damage. Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation. In this paper, we propose a generalizable and robust secure code generation method SecCoder by using in-context learning (ICL) and the safe demonstration. The dense retriever is also used to select the most helpful demonstration to maximize the improvement of the generated code's security. Experimental results show the superior generalizability of the proposed model SecCoder compared to the current secure code generation method, achieving a significant security improvement of an average of 7.20% on unseen test cases. The results also show the better robustness of SecCoder compared to the current attacked code LM, achieving a significant security improvement of an average of 7.74%. Our analysis indicates that SecCoder enhances the security of LMs in generating code, and it is more generalizable and robust.