Large language models have gained widespread attention recently, but their potential security vulnerabilities, especially privacy leakage, are also becoming apparent. To test and evaluate for data extraction risks in LLMs, we propose CoSPED, short for Consistent Soft Prompt Targeted Data Extraction and Defense. We introduce several innovative components, including Dynamic Loss, Additive Loss, Common Loss, and Self Consistency Decoding Strategy, and tested to enhance the consistency of the soft prompt tuning process. Through extensive experimentation with various combinations, we achieved an extraction rate of 65.2% at a 50-token prefix comparison. Our comparisons of CoSPED with other reference works confirm our superior extraction rates. We evaluate CoSPED on more scenarios, achieving Pythia model extraction rate of 51.7% and introducing cross-model comparison. Finally, we explore defense through Rank-One Model Editing and achieve a reduction in the extraction rate to 1.6%, which proves that our analysis of extraction mechanisms can directly inform effective mitigation strategies against soft prompt-based attacks.

Abstract Static malware analysis remains a core technique in cybersecurity due to its ability to assess potentially malicious software without execution. Nevertheless, many existing static approaches rely on handcrafted features or curated datasets that may not generalize well to evolving malware distributions. In this work, we investigate an alternative representation that operates directly on raw binary content. Executable files are transformed into visual encodings that preserve local structural relationships, enabling the use of deep learning models without requiring semantic disassembly or dynamic behavior profiling. This study explores the use of a Consistency Bi-directional Generative Adversarial Network (CBiGAN) as an anomaly detection framework rather than as a generative model. The method enforces consistency between latent encodings and reconstructions, allowing deviations from learned benign structure to be quantified through reconstruction discrepancies. Importantly, the approach does not introduce a new generative architecture, instead, it evaluates how consistency based generative modeling can be applied at scale to heterogeneous malware data. The proposed framework is evaluated across multiple datasets comprising both Portable Executable (PE) and Object Linking and Embedding (OLE) files, including a large self-collected corpus spanning 214 malware families. Results demonstrate stable detection performance in terms of Area Under the Curve (AUC) while maintaining a unified and computationally lightweight processing pipeline. These findings suggest that consistency based generative modeling provides a practical and scalable direction for malware anomaly detection across diverse file formats and threat families.

Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques. Artificial Intelligence (AI) has been used by the cyber security community in the past decade to automatically identify such attacks. However, deep learning methods have yet to be extensively explored for Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are based on time sequential models like LSTM and there is short of research in CNNs as they are not naturally suited for this problem. In this article, we propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time that are useful for attacks detection by CNNs. The proposed method is integrated with two classical CNNs: ResNet and EfficientNet, where the detection performance is evaluated. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.

Malwares are the key means leveraged by threat actors in the cyber space for their attacks. There is a large array of commercial solutions in the market and significant scientific research to tackle the challenge of the detection and defense against malwares. At the same time, attackers also advance their capabilities in creating polymorphic and metamorphic malwares to make it increasingly challenging for existing solutions. To tackle this issue, we propose a methodology to perform malware detection and family attribution. The proposed methodology first performs the extraction of opcodes from malwares in each family and constructs their respective opcode graphs. We explore the use of clustering algorithms on the opcode graphs to detect clusters of malwares within the same malware family. Such clusters can be seen as belonging to different sub-family groups. Opcode graph signatures are built from each detected cluster. Hence, for each malware family, a group of signatures is generated to represent the family. These signatures are used to classify an unknown sample as benign or belonging to one the malware families. We evaluate our methodology by performing experiments on a dataset consisting of both benign files and malware samples belonging to a number of different malware families and comparing the results to existing approach.

Connected machines become more vulnerable to malware infections which potentially cause them to be controlled as part of a botnet for cybercrime activities. Prompt detection of infected machines is required for protecting local networks and infrastructure as well as reducing the impact of botnets. In this paper, we propose the use of machine learning techniques involving multi-layer perceptrons and decision trees on network traffic analysis for the detection of botnet traffic. We enhance components of an existing detection framework with these techniques to automate its processes and improve performance at the same time. Our experiments indicate that the modifications successfully improved the overall performance of botnet traffic detection in both supervised and semi-supervised manners.