With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software development cycle. While CodeQL stands as the premier static code analysis tool currently available on the market, its high barrier to entry poses challenges for meeting the implementation requirements of shift-left security initiatives. While large language model (LLM) offers potential assistance in QL code development, the inherent complexity of code generation tasks often leads to persistent issues such as syntactic inaccuracies and references to non-existent modules, which consequently constrains their practical applicability in this domain. To address these challenges, this paper proposes CQLLM (CodeQL-enhanced Large Language Model), a novel framework for automating the generation of CodeQL security vulnerability detection code by leveraging LLM. This framework is designed to enhance both the efficiency and the accuracy of automated QL code generation, thereby advancing static code analysis for a more efficient and intelligent paradigm for vulnerability detection. First, retrieval-augmented generation (RAG) is employed to search the vector database for dependency libraries and code snippets that are highly similar to the user’s input, thereby constraining the model’s generation process and preventing the import of invalid modules. Then, the user input and the knowledge chunks retrieved by RAG are fed into a fine-tuned LLM to perform reasoning and generate QL code. By integrating external knowledge bases with the large model, the framework enhances the correctness and completeness of the generated code. Experimental results show that CQLLM significantly improves the executability of the generated QL code, with the execution success rate improving from 0.31% to 72.48%, outperforming the original model by a large margin. Meanwhile, CQLLM also enhances the effectiveness of the generated results, achieving a CWE (Common Weakness Enumeration) coverage rate of 57.4% in vulnerability detection tasks, demonstrating its practical applicability in real-world vulnerability detection.

Phishing emails present a significant and persistent cybersecurity threat to individuals and organizations globally due to the difficulty in detecting these malicious messages. Large Language Models (LLMs) have inadvertently intensified this challenge by facilitating the automated creation of high-quality, covert phishing emails that can evade traditional rule-based detection systems. In this study, we examine the offensive capabilities of LLMs in generating phishing emails and introduce Phish-Master, a novel algorithm that integrates Chain-of-Thought (COT) reasoning, MetaPrompt techniques, and domain-specific insights to produce phishing emails designed to bypass enterprise-level filters. Our experiment, involving 100 malicious emails, validates Phish-Master’s real-world effectiveness, achieving a 99% evasion rate within authentic campus networks, successfully bypassing filters and targeting recipients, a testament to its capability in navigating complex network environments. To counteract the threat posed by Phish-Master and similar LLM-generated phishing emails, we have developed a multi-machine learning model integration framework trained on Kaggle’s phishing email dataset. This framework achieved an impressive detection rate of 99.87% on a rigorous test set of LLM-generated phishing emails, highlighting the critical role of our specialized dataset in enabling the detection tool to effectively recognize sophisticated patterns in LLM-crafted phishing emails. This study highlights the evolving threat of LLM-generated phishing emails and introduces an effective detection algorithm to mitigate this risk, emphasizing the importance of continued research in this domain.

With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software development cycle. While CodeQL stands as the premier static code analysis tool currently available on the market, its high barrier to entry poses challenges for meeting the implementation requirements of shift-left security initiatives. While large language model (LLM) offers potential assistance in QL code development, the inherent complexity of code generation tasks often leads to persistent issues such as syntactic inaccuracies and references to non-existent modules, which consequently constrains their practical applicability in this domain. To address these challenges, this paper proposes CQLLM, a novel framework for automating the generation of CodeQL security vulnerability detection code by leveraging LLM. This framework is designed to enhance both the efficiency and the accuracy of automated QL code generation, thereby advancing static code analysis for a more efficient and intelligent paradigm for vulnerability detection.First, retrieval-augmented generation (RAG) is employed to search the vector database for dependency libraries and code snippets that are highly similar to the user’s input, thereby constraining the model’s generation process and preventing the import of invalid modules. Then, the user input and the knowledge chunks retrieved by RAG are fed into a fine-tuned LLM to perform reasoning and generate QL code. By integrating external knowledge bases with the large model, the framework enhances the correctness and completeness of the generated code.Experimental results show that CQLLM significantly improves the executability of the generated QL code, with the execution success rate improving from 0.31% to 72.48%, outperforming the original model by a large margin. Meanwhile, CQLLM also enhances the effectiveness of the generated results, achieving a CWE coverage rate of 57.4% in vulnerability detection tasks, demonstrating its practical applicability in real-world vulnerability detection.

In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising approach for automating penetration testing due to its capability to perform sequential decision-making in complex environments. However, in real-world enterprise networks, attack actions are typically characterized by highly coupled multi-dimensional parameter combinations, resulting in an exponentially expanding discrete action space. Such a large action space significantly degrades exploration efficiency and prevents conventional DRL agents from learning effective attack paths under sparse-reward conditions. To address these challenges, this paper proposes a Large Language Model-enhanced Autonomous Reinforcement Learning Penetration Testing framework (L-ARLPT). Specifically, the framework leverages the domain knowledge embedded in a Large Language Model (LLM) to perform tactical planning, thereby pruning the original action space into a compact set of candidate actions. Subsequently, an experience-driven layer employs the optimization mechanism of a Deep Q-Network (DQN) to conduct value estimation and policy learning within the reduced candidate set. To validate the effectiveness of the proposed framework, a high-fidelity enterprise penetration-testing simulation environment was constructed based on realistic enterprise attack scenarios. Experimental results demonstrate that, in a high-fidelity enterprise penetration-testing environment with a raw theoretical parameter-combination space containing $6\times {10}^7$ combinations, the proposed L-ARLPT framework achieves an average penetration depth of 3.33 out of 4.00, substantially outperforming both reinforcement learning baselines (all ≤1.27) and LLM-based baselines (1.12). Moreover, successful episodes require only 128.43 decision steps on average, enabling long-horizon, cross-domain lateral penetration in high-dimensional discrete action spaces.