LAMDA: a Longitudinal Android Malware Benchmark for Concept Drift Analysis

Machine learning ML-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the...

CVE-2024-34075

kurwov is a fast, dependency-free library for creating Markov Chains. An unsafe sanitization of dataset contents on the MarkovDatagetNext method used in Markovgenerate and Markovchoose allows a maliciously crafted string on the dataset to throw and stop the function from running properly. If a...

CVE-2024-41803

Xibo is a content management system CMS. An SQL injection vulnerability was discovered in the API routes inside the CMS responsible for Filtering DataSets. This allows an authenticated user to to obtain arbitrary data from the Xibo database by injecting specially crafted values in to the API for...

CVE-2024-5389

In lunary-ai/lunary version 1.2.13, an insufficient granularity of access control vulnerability allows users to create, update, get, and delete prompt variations for datasets not owned by their organization. This issue arises due to the application not properly validating the ownership of dataset...

CVE-2023-22834

The Contour Service was not checking that users had permission to create an analysis for a given dataset. This could allow an attacker to clutter up Compass folders with extraneous analyses, that the attacker would otherwise not have permission to create...

CVE-2023-1573

A vulnerability was found in DataGear up to 1.11.1 and classified as problematic. This issue affects some unknown processing of the component Graph Dataset Handler. The manipulation leads to cross site scripting. The attack may be initiated remotely. The exploit has been disclosed to the public a...

CVE-2022-43721

An authenticated attacker with update datasets permission could change a dataset link to an untrusted site, users could be redirected to this site when clicking on that specific dataset. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0...

Sec5GLoc: Securing 5G Indoor Localization Via Adversary-Resilient Deep Learning Architecture

Emerging 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges. In this paper, we identify and address threats including location spoofing and adversarial signal manipulation against 5G-based indoor...

EtherBee: a Global Dataset of Ethereum Node Performance Measurements Coupled with Honeypot Interactions and Full Network Sessions

We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events,...

CVE-2021-26702

EPrints 3.4.2 exposes a reflected XSS opportunity in the dataset parameter to the cgi/datasetdictionary URI...

CVE-2021-37839

Apache Superset up to 1.5.1 allowed for authenticated users to access metadata information related to datasets they have no permission on. This metadata included the dataset name, columns and metrics...

Malicious code in @confluent-cfet-medusa/dataset-designer (npm)

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MAL-2025-4148 Malicious code in @confluent-cfet-medusa/dataset-designer (npm)

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Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images

Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having...

A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles

Due to its nature of dynamic, mobility, and wireless data transfer, the Internet of Vehicles IoV is prone to various cyber threats, ranging from spoofing and Distributed Denial of Services DDoS attacks to malware. To safeguard the IoV ecosystem from intrusions, malicious activities, policy...

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models LLMs perform well in programming analysis on source code, while...

FragFake: a Dataset for Fine-Grained Detection of Edited Images with Vision Language Models

Fine-grained edited image detection of localized edits in images is crucial for assessing content authenticity, especially given that modern diffusion models and image editing methods can produce highly realistic manipulations. However, this domain faces three challenges: 1 Binary classifiers yie...

Hybrid Audio Detection Using Fine-Tuned Audio Spectrogram Transformers: a Dataset-Driven Evaluation of Mixed AI-Human Speech

The rapid advancement of artificial intelligence AI has enabled sophisticated audio generation and voice cloning technologies, posing significant security risks for applications reliant on voice authentication. While existing datasets and models primarily focus on distinguishing between human and...

Evaluating the Efficacy of LLM Safety Solutions : the Palit Benchmark Dataset

Large Language Models LLMs are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives...

Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection...