PVDetector: Pretrained Vulnerability Detection on Vulnerability-enriched Code Semantic Graph

Prompting techniques unlock valuable insights from pre-trained models, leading to a remarkable performance in Natural Language Processing (NLP). Inspired by NLP advancements, various graph prompt methods have emerged to transfer knowledge from pre-trained graph models to diverse downstream graph tasks. However, designing appropriate prompts for different downstream tasks remains a significant challenge due to substantial differences between pre-training graph tasks and downstream graph tasks. Moreover, existing graph prompt methods prevent transferability across different domains as they independently pre-train a model for each dataset. To address these challenges, we propose Graph Pretraining and Task-Specific Prompting (GTSP). To the best of our knowledge, GTSP is the first unified pre-training framework to handle cross-domain graph data and multiple types of graph tasks simultaneously. The key idea of our framework is to utilize similar label features between pre-training tasks and downstream tasks to seamlessly integrate the downstream task prompt into the pretrained model. We then propose a task-specific prompting architecture and design a label similarity matrix as prompt input. With GTSP, the pre-trained model can adapt to various types of downstream graph tasks, effectively avoiding the challenges of forcibly altering graph task forms. Experimental analysis conducted on benchmark datasets demonstrates the superiority of our method.

Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a largescale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.

A transformer-based framework for software vulnerability detection using attention-driven convolutional neural networks

Survey of source code vulnerability analysis based on deep learning

With the rapid growth in the scale and complexity of software systems, automated vulnerability detection has become increasingly important. Although Large Language Models (LLMs) demonstrate strong code comprehension capabilities, their abilities in vulnerability detection are still limited by issues such as hallucinations, high fine-tuning costs, and difficulties in effectively leveraging fine-grained historical vulnerability patterns and domain knowledge. To address these challenges, we propose Retrieval-Augmented Semantic Mapping for Vulnerability Detection (RASM-Vul), a retrieval-augmented framework that enhances LLM detection capability through multi-perspective semantic mapping. The core of our approach is the construction of a comprehensive knowledge base composed of vulnerability–fix pairs and structured knowledge. We leverage multi-view (e.g., code, AST, knowledge) similarity retrieval to accurately match the most relevant vulnerability patterns with repair examples for the code under analysis. Our designed Weighted Reciprocal Ranking Fusion (WRRF) algorithm adaptively integrates contributions from different retrieval channels according to the problem type, significantly improving the relevance and accuracy of retrieval. Experiments show that RASM-Vul achieves an F1-score of 66.79%, outperforming existing baselines on the PrimeVul paired dataset. Our study demonstrates that knowledge-enhanced semantic mapping and retrieval can improve the robustness and reliability of automated vulnerability detection.

Automated vulnerability detection in source code is essential for modern software security, yet existing approaches suffer from high false positive rates, weak contextual understanding, and limited recall. This work presents the most comprehensive empirical evaluation to date of Small Language Model (SLM) based vulnerability detection, spanning more than twenty system configurations ranging from simple prompting baselines to advanced retrieval-augmented ensembles. Our methodology systematically examines how small LLMs (1–3B parameters) behave under different prompting strategies, static analysis integrations, ensembling techniques, and retrieval mechanisms. Through extensive ablation studies on the DiverseVul dataset, we find that many commonly assumed improvements such as complex Chain-of-Thought prompting, multi-model aggregation, or post-processing verification frequently degrade performance, largely due to overfitting and the inherent reasoning limitations of small models. In contrast, retrieval-augmented generation (RAG) consistently enhances both recall and stability, providing an average F1 gain of 4–5% by grounding model predictions in relevant vulnerability examples. Our best-performing system, a RAG-enhanced stacking ensemble with structured reasoning, achieves a 71.2% F1-score, surpassing the state-of-the-art LLaMA 3.2 model by 5.2 percentage points. our findings demonstrate that effective vulnerability detection does not arise from increased architectural complexity but from combining lightweight models with robust retrieval and context-aware reasoning. This study establishes RAG-driven small LLMs as a practical, cost-efficient, and empirically validated direction for secure software analysis. our source code at https://github.com/rafi79/RAG-Enhanced-Multi-Model-Ensemble-for-Automated-Vulnerability-Detection-Using-SLMs.

Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects. However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap. After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap. To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all. Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks. Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.

Because of the language’s simplicity, flexibility and massive libraries supporting artificial intelligence, web development and data science python has become one of the widely used programming language. But this speedy growth has led to software security and vulnerability concerns. This paper examines the security provisions available in python programming and their role in developing secure applications. Herein the analysis is done based on security mechanisms such as validation of inputs, authentication and authorization, libraries available for cryptography, management of dependency tools, static code analysis tools and secure web frameworks. The paper highlights the common security challenges faced in python programming and emphasis on the importance of secure coding practices and comparison of vulnerability detection tools implemented in the past research works. The finding proclaims that python provides several built-in tools and mechanisms which when properly implemented can notably improve reliability and security of software systems. Keywords: Python Security, Secure Coding Practices, Vulnerability Detection, Machine Learning Based Security Analysis, Static Code Analysis, Cryptography in Python, Software Security

Emotion detection, hence, is an important step toward making human-computer interaction a more enhanced process, where systems are made capable of identifying and responding to the emotional state of users. In fact, multimodal emotion detection systems in which both auditory and visual information are fused are emerging, and these approaches toward expressive emotional states are complementary and robust. Multimodal systems enhance the quality of interacting and, through many applications, can diagnose emotional disorders, monitor automotive safety, and improve human-robot interactions. In nature, the high-dimensional space and dynamic threats have resulted in obtaining low accuracy and high computational cost in applying the traditional models based on single-modality data. On the other hand, multimodal systems explore the synergy between audio and visual data, giving better performance and higher accuracy in inferring subtle emotional expressions. The latest improvement was done on these systems using recent advancements in transfer learning and deep learning techniques.That being said, this research Proposal devises a multimodal emotion recognition system integrating speech and face information through transfer learning for improved accuracy and robustness. Serving this purpose, the objectives of this research entail the effective comparison among different transfer-learning strategies, including the impact of pre-trained models in speech-based emotion recognition, and to introduce the role of voice activity detection in the process. Advanced neural network architectures like Spatial Transformer Networks and bidirectional LSTM in facial emotion recognition will also be tested. Early and late fusion strategies will also be used to find the best strategy for combining speech and facial data.This research will target several challenges that involve the complexity of data, balancing of the model performance-robustness balance, computational limitations, and standardization of evaluations in developing a working and robust emotion recognition system to enhance digital interaction and apply in practical areas. The goal is to create a system that oversteps the limitation of single-modality models through state-of-the-art advances in deep learning, as well as front-line improvements in transfer learning, in the manner of emotion detection performance.

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoption of data-driven methods. In this survey, we present a comprehensive review of Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) techniques for vulnerability detection. We analyze recent advances in feature representation, fine-tuning strategies, generative approaches, and prompt engineering, while highlighting their ability to capture both syntactic and semantic properties of source code. Furthermore, we examine commonly used evaluation metrics and provide a critical discussion of key challenges, including the lack of large-scale real-world datasets, limited vulnerability coverage, class imbalance, interpretability gaps, hallucination, and high computational costs. To address these issues, we outline promising future research directions, such as neuro-symbolic hybrid methods, parameter-efficient fine-tuning, continual learning, cross-language generalization, and explainable AI for vulnerability detection. Unlike previous studies, the present work explores learning paradigms from ML to LLMs using comprehensive evaluation criteria that highlight analytical capability, feature interpretability, and code-context comprehension. By combining these factors, our study addresses the methodological gap between classic feature-based approaches and current LLM-driven reasoning frameworks, providing beneficial insights to develop robust, scalable, and trustworthy software vulnerability detection systems.

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

Deep learning (DL)-based models have been widely applied in wireless communication systems with excellent performance. However, most of these models are task- and scenario-specific, exhibiting limited generalization and contributing to increasing complexity and overhead with their deployment in systems. Inspired by the emergent capabilities and strong generalization exhibited by large models (LMs), represented by large language models (LLMs), this paper analyzes the differences between existing DL-based wireless communication models and LLMs, proposing a framework for designing LMs tailored to wireless communications. Building upon this framework, we integrate channel-related tasks of the physical layer into a unified pre-training task, i.e., channel completion, and propose a pre-trained masked channel model (MCM) with different parameter scales ranging from 5 million to 1 billion (B), enabling simultaneous solving of channel state information (CSI) feedback, prediction, and estimation. Additionally, scaling laws on these downstream tasks are derived to guide the design and deployment of MCMs. The formulated scaling laws indicate that the proposed MCM with 1B parameter not only shows no sign of performance saturation on the pre-trained task but also has the potential to enhance performance at larger model sizes. Simulation results demonstrate that the proposed MCM outperforms the existing algorithms across various downstream tasks while exhibiting superior cross-task and cross-scenario generalization capabilities in both simulated and realistic scenarios.

Quantization-aware training for low precision photonic neural networks

Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur a high false positive rate. The intelligent vulnerability detection methods suffer from the problems of long-term dependence, out of vocabulary, coarse detection granularity and lack of vulnerable samples. This paper proposes an automated and intelligent vulnerability detection method in source code based on the minimum intermediate representation learning. First, the sample in the form of source code is transformed into a minimum intermediate representation to exclude the irrelevant items and reduce the length of the dependency. Next, the intermediate representation is transformed into a real value vector through pre-training on an extended corpus, and the structure and semantic information are retained. Then, the vector is fed to three concatenated convolutional neural networks to obtain high-level features of vulnerability. Last, a classifier is trained using the learned features. To validate this vulnerability detection method, an experiment was performed. The empirical results confirmed that compared with the traditional methods and the state-of-the-art intelligent methods, our method has a better performance with fine granularity.

BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection