Detecting Prompt Injection Attacks against Application Using Classifiers

Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM,...

ByteShield: Adversarially Robust End-To-End Malware Detection through Byte Masking

Research has proven that end-to-end malware detectors are vulnerable to adversarial attacks. In response, the research community has proposed defenses based on randomized and derandomized smoothing. However, these techniques remain susceptible to attacks that insert large adversarial payloads. To...

LLM-Based Vulnerable Code Augmentation: Generate or Refactor?

Vulnerability code-bases often suffer from severe imbalance, limiting the effectiveness of Deep Learning-based vulnerability classifiers. Data Augmentation could help solve this by mitigating the scarcity of under-represented CWEs. In this context, we investigate LLM-based augmentation for...

Privacy Practices of Browser Agents

This paper presents a systematic evaluation of the privacy behaviors and attributes of eight recent, popular browser agents. Browser agents are software that automate Web browsing using large language models and ancillary tooling. However, the automated capabilities that make browser agents...

Towards Small Language Models for Security Query Generation in SOC Workflows

Analysts in Security Operations Centers routinely query massive telemetry streams using Kusto Query Language KQL. Writing correct KQL requires specialized expertise, and this dependency creates a bottleneck as security teams scale. This paper investigates whether Small Language Models SLMs can...

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features

Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...

Malicious code in dataset-view (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 92f4e8552a727b5aac457a7d16a99d4e0b3a99342044aee673aba036abbcd9e0 The package dataset-view was found to contain malicious code...

EUVD-2025-200061

Malicious code in dataset-view npm...

MAL-2025-191508 Malicious code in dataset-view (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 92f4e8552a727b5aac457a7d16a99d4e0b3a99342044aee673aba036abbcd9e0 The package dataset-view was found to contain malicious code...

CVE Breadcrumbs: Tracking Vulnerabilities through Versioned Apache Libraries

The Apache Software Foundation ASF ecosystem underpins a vast portion of modern software infrastructure, powering widely used components such as Log4j, Tomcat, and Struts. However, the ubiquity of these libraries has made them prime targets for high-impact security vulnerabilities, as illustrated...

MASCOT: Analyzing Malware Evolution through a Well-Curated Source Code Dataset

In recent years, the explosion of malware and extensive code reuse have formed complex evolutionary connections among malware specimens. The rapid pace of development makes it challenging for existing studies to characterize recent evolutionary trends. In addition, intuitive tools to untangle the...

Clustering Malware at Scale: A First Full-Benchmark Study

Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware...

SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks

The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service DoS attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional...

An Efficient Privacy-Preserving Intrusion Detection Scheme for UAV Swarm Networks

The rapid proliferation of unmanned aerial vehicles UAVs and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they...

Exploring Hidden Geographic Disparities in Android Apps

While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based Android app differentiation, uncovering two important and underexamined phenomena with security and fairness implication...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

Constructing and Benchmarking: A Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework

Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models LLMs to craft highly deceptive content. This study presents a comprehensive email dataset containing phishing, spam, and legitimate messages, explicitly distinguishing betwee...

CVE-2025-64067

Primakon Pi Portal 1.0.18 API endpoints responsible for retrieving object-specific or filtered data e.g., user profiles, project records fail to implement sufficient server-side validation to confirm that the requesting user is authorized to access the requested object or dataset. This...

Semantic Superiority Vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection

Business Email Compromise BEC is a sophisticated social engineering threat that manipulates organizational hierarchies and exploits psychological vulnerabilities, leading to significant financial damage. According to the 2024 FBI Internet Crime Report, BEC accounts for over $2.9 billion in annual...

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...