Large language models (LLMs) have revolutionized AI applications across diverse domains. However, their widespread deployment has introduced critical security vulnerabilities, particularly prompt injection attacks that manipulate model behavior through malicious instructions. Following Kitchenham’s guidelines, this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape. Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks, achieving over 90% success rates against unprotected systems. In response, defense mechanisms show varying effectiveness: input preprocessing achieves 60%–80% detection rates and advanced architectural defenses demonstrate up to 95% protection against known patterns, though significant gaps persist against novel attack vectors. We identified 37 distinct defense approaches across three categories, but standardized evaluation frameworks remain limited. Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations, such as the inability to distinguish instructions from data and attention mechanism vulnerabilities. This highlights critical research directions such as formal verification methods, standardized evaluation protocols, and architectural innovations for inherently secure LLM designs.

In the era of Large Language Models (LLMs), the code summarization technique boosts a lot, along with the emergence of many new significant works. However, the potential of code summarization in the Computer Security Area still remains explored. Can we generate a code summary of a code snippet for its security intention? Thus, this work proposes an innovative large-scale multi-perspective Code Intent Summary Dataset named BADS , aiming to increase the understanding of a given code snippet and reduce the risk in the code developing process. The procedure of establishing a dataset can be divided into four steps: First, we collect samples of codes with known vulnerabilities as well as code generated by AI from multiple sources. Second, we do the data clean and format unification, then do the data combination. Third, we utilize the LLM to automatically Annotate the code snippet. Last, We do the human evaluation to double-check. The dataset contains X code examples which cover Y categories of vulnerability. Our data are from Z open-source projects and CVE entries, and compared to existing work, our dataset not only contains original code but also code function summary and security intent summary, providing context information for research in code security analysis. All information is in CSV format. The contributions of this paper are four-fold: the establishment of a high-quality, multi-perspective Code Intent Summary Dataset; an innovative method in data collection and processing; A new multi-perspective code analysis framework that promotes cross-disciplinary research in the fields of software engineering and cybersecurity; improving the practicality and scalability of the research outcomes by considering the code length limitations in real-world applications. Our dataset and related tools have been publicly released on GitHub.

This paper proposes a programming education evaluation method based on few-shot prompt learning for object-oriented programming education. The method consists of three core components: establishing multi-dimensional evaluation criteria, designing a few-shot learning-based example code library, and developing intelligent evaluation prompt templates. Through a 16-week controlled experiment conducted in a university computer science program, results demonstrate significant improvements in both learning outcomes and teaching efficiency: the experimental group showed a 9.2% to 15.4% improvement in key metrics including final exam scores and code quality, while reducing grading time by 80% and shortening feedback time from 48 hours to 0.5 hours. This research provides a new paradigm for educational evaluation in computer science education.

Recent studies have shown that code models are susceptible to backdoor attacks. When injected with a backdoor, the victim code model can function normally on benign samples but may produce predetermined malicious outputs when triggers are activated. However, previous backdoor attacks on code models have used explicit triggers, and we aim to investigate the vulnerability of code models to stealthy backdoor attacks in this study. To this end, we propose a backdoor attack approach using Abstract Syntax Tree-based Triggers (ASTT) to obtain stealthiness. We evaluate ASTT on deep learning-based code models and three downstream tasks (i.e., code translation, code repair, and defect detection). With the clustering algorithm, we generated triggers based on abstract syntax trees. We find that the average attack success rate of our ASTT can reach 92.71%. Moreover, our ASTT is stealthy and can effectively bypass state-of-the-art defense approaches. Finally, we verify that the time overhead of our proposed ASTT is small and can meet the needs in real scenarios. Our finding demonstrates security weaknesses in code models under stealthy backdoor attacks.