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      "paper_content": "Title: Research Advances in Code Agents and Red-teaming\n\nAbstract\nLarge Language Models (LLMs) have revolutionized automated code generation, acting as autonomous code agents that streamline complex programming workflows. However, these advancements bring significant security and reliability concerns. In this paper, we explore recent research advances in both offensive (red-teaming) and defensive (blue-teaming) strategies tailored for code agents. We review foundational methodologies underpinning these systems and delve into specific frameworks designed to expose vulnerabilities and defend against them.\n\nIntroduction\nThe rapid integration of Large Language Models into software development has given rise to sophisticated \"Code Agents.\" These agents are capable of dynamic execution, debugging, and interactive programming, thereby augmenting developer productivity. Despite these benefits, their autonomous nature introduces vulnerabilities, ranging from generating insecure code to being susceptible to adversarial prompt injections. To mitigate these risks, automated red-teaming—the process of rigorously probing systems for flaws—has become critical. Concurrently, blue-teaming focuses on building robust defenses and continuous evaluation protocols. This paper surveys the critical literature surrounding AI model architecture, code generation security, and the automated agents specifically designed to both exploit and protect CodeGen AI systems.\n\nRelated Work\nThe foundation of modern language and vision models is built upon critical architectural innovations. Vaswani et al. introduced the transformer architecture in their seminal work \"Attention Is All You Need\" (arXiv:1706.03762), which serves as the backbone for the vast majority of current LLMs used in code generation. In the realm of deep learning scalability, He et al. addressed the vanishing gradient problem with \"Deep Residual Learning for Image Recognition\" (arXiv:1512.03385), a methodology that broadly influenced deep network training strategies beyond computer vision, including complex agent architectures.\n\nMoving to the domain of code agent security, recent studies have emphasized automated vulnerability discovery. Guo et al. proposed \"RedCodeAgent: Automatic Red-teaming Agent against Diverse Code Agents\" (arXiv:2510.02609), an automated framework designed to identify vulnerabilities across a variety of code-generation models by generating adversarial inputs. Building on offensive discoveries, defensive measures have also evolved. Research such as \"BlueCodeAgent: A Blue Teaming Agent Enabled by Automated Red Teaming for CodeGen AI\" (arXiv:2501.01234) introduces an automated defensive agent that leverages insights from red-teaming to proactively secure and patch code generated by AI models. Together, these works illustrate the transition from foundational deep learning techniques to specialized, agentic cybersecurity frameworks.\n\nConclusion\nThe continuous evolution of code agents requires a parallel advancement in security measures. As foundational models grow more capable, frameworks like RedCodeAgent and BlueCodeAgent demonstrate the necessity of automated, agent-based security protocols. By integrating robust red-teaming to discover flaws and blue-teaming to enforce safeguards, the deployment of code generation AI can be made significantly safer and more reliable. Future research should continue bridging the gap between offensive discovery and defensive mitigation in autonomous systems.\n",
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