## 3 Related Works
Analyzing datasets containing 48 instances across 279 vulnerabilities, VuRLE grouped replacements for safer versions, achieving an average repair prediction accuracy of 65.59%. For C++ scenarios, VulD-Transformer [35] extended Deep Learning feature extraction layers with Program Dependency Graphs (PDGs) to analyze control and data dependencies between code segments. The model processed datasets ranging from 22,148 to 291,892 samples, focusing on API function calls, arithmetic expressions, array usage, and pointer operations. VulD-Transformer used a custom multi-attention mechanism and achieved accuracies ranging from 59.34% to 80.44%. Lastly, advancements in LLMs were demonstrated in a study [36] using Generative Pre-trained Transformer (GPT) APIs. The study analyzed the top 25 insecure C++ code examples from SANS, focusing on four critical Common Weakness Enumerations (CWEs): buffer overflow, risky functions, integer overflow, and insecure implementations. GPT models (versions 2-4) predicted over 20 CWEs, detecting 56% of vulnerabilities accurately. Among these, 88% of the identified vulnerabilities were accompanied by secure code recommendations. Despite the substantial progress achieved by the reviewed methodologies, several critical limitations persist. RNN-based approaches, such as SySeVR [27] and VulDeeLocator [28], rely on ASTs and PDGs, which, while effective for capturing syntactic and semantic relationships, are constrained in scalability and language coverage. Transformer-based models, including BBVD [29] and VulDefend [30], enhance efficiency but are primarily focused on C and C++, addressing a limited subset of CWEs, which restricts their applicability to broader, heterogeneous programming environments. Hybrid models, such as SeqTrans [31], VRepair [32], and DB-CBIL [33], exhibit notable precision in addressing specific vulnerabilities but frequently necessitate extensive labeled data and are often restricted in their ability to generalize across programming languages and contexts. Clustering-based approaches, including VuRLE [34], and dependency graph-based models, such as VulD-Transformer [35], encounter limitations in processing large-scale datasets and adapting to diverse programming constructs. Furthermore, LLM-driven solutions, such as the GPT-based study [36], demonstrate potential for integrating natural language interfaces but are hindered by inconsistent predictions and limited support for underrepresented languages, such as PHP. These observations form the basis for the comprehensive analysis in Section 6, titled _Discussions_, where Proto-MAML is evaluated against state-of-the-art approaches through comparative results. The discussion emphasizes how Proto-MAML addresses limitations in vulnerability detection and remediation across PHP, Java, C, and C++ while covering a wide range of CWEs. By leveraging structured meta-learning with FSL, it showcases adaptability to emerging vulnerabilities using minimal data. Furthermore, its integration into CI/CD pipelines facilitates continuous security assessments, and its QA-based framework promotes interdisciplinary collaboration, aligning with the requirements of modern security-centric workflows.