# LLM Security Guard for Code

arXiv: https://arxiv.org/abs/2405.01103 · HTML: https://mlsanity.com/paper/2405.01103/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2024.

## Built on by (most cited first, 15 of 15)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios](https://mlsanity.com/paper/2410.12468/) (2024)
- [Correctness Assessment of Code Generated by Large Language Models Using Internal Representations](https://mlsanity.com/paper/2501.12934/) (2025)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection](https://mlsanity.com/paper/2504.16429/) (2025)
- [Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models](https://mlsanity.com/paper/2502.06039/) (2025)
- [FATS: A Prompt Injection Attack Utilizing Feign Security Agents with Deceptive Few-shots Learning](https://mlsanity.com/paper/2410.08776/) (2024)
- [SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows](https://mlsanity.com/paper/2506.07313/) (2025)
- [SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment](https://mlsanity.com/paper/2502.18468/) (2025)
- [PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines](https://mlsanity.com/paper/2605.10614/) (2026)
- [Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs](https://mlsanity.com/paper/2507.20977/) (2025)
- [Feedback-based Automated Verification in Vibe Coding of CAS Adaptation Built on Constraint Logic](https://mlsanity.com/paper/2602.18607/) (2026)
- [Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification](https://mlsanity.com/paper/2511.00202/) (2025)
- [From Data Leak to Secret Misses: The Impact of Data Leakage on Secret Detection Models](https://mlsanity.com/paper/2601.22946/) (2026)
- [From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practice](https://mlsanity.com/paper/2607.16660/) (2026)

## Abstract (arXiv)

Many developers rely on Large Language Models (LLMs) to facilitate software development. Nevertheless, these models have exhibited limited capabilities in the security domain. We introduce LLMSecGuard, a framework to offer enhanced code security through the synergy between static code analyzers and LLMs. LLMSecGuard is open source and aims to equip developers with code solutions that are more secure than the code initially generated by LLMs. This framework also has a benchmarking feature, aimed at providing insights into the evolving security attributes of these models.