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...

ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications

AI agent-based systems are becoming increasingly integral to modern software architectures, enabling autonomous decision-making, dynamic task execution, and multimodal interactions through large language models LLMs. However, these systems introduce novel and evolving security challenges, includi...

Future-Back Threat Modeling: A Foresight-Driven Security Framework

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise...

Human-Centered Threat Modeling in Practice: Lessons, Challenges, and Paths Forward

Human-centered threat modeling HCTM is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective,...

Threat Landscape of the Building and Construction Sector, Part One: Initial Access, Supply Chain, and the Internet of Things

In 2025, the construction industry stands at the crossroads of digital transformation and evolving cybersecurity risks, making it a prime target for threat actors. Cyber adversaries, including ransomware operators, organized cybercriminal networks, and state-sponsored APT groups from countries su...

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a...

AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI

This paper introduces the Agentic AI Governance Assurance & Trust Engine AAGATE, a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional...

AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training and Experimentation Scenarios

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models LLMs show promise for automated synthesis, unconstrained generation often yields configurations that...

appsec-sentinel

AppSec-Sentinel AI-powered security scanner with cross-file...

Advancing Honeywords for Real-World Authentication Security

Introduced by Juels and Rivest in 2013, Honeywords, which are decoy passwords stored alongside a real password, appear to be a proactive method to help detect password credentials misuse. However, despite over a decade of research, this technique has not been adopted by major authentication...

Intermittent File Encryption in Ransomware: Measurement, Modeling, and Detection

File encrypting ransomware increasingly employs intermittent encryption techniques, encrypting only parts of files to evade classical detection methods. These strategies, exemplified by ransomware families like BlackCat, complicate file structure based detection techniques due to diverse file...

A week in security (October 6 – October 12)

Last week on Malwarebytes Labs: Apple voices concerns over age-check law that could put user privacy at risk Your passwords don’t need so many fiddly characters, NIST says Millions of very private chats exposed by two AI companion apps Fake VPN and streaming app drops malware that drains your ban...

Modeling scams see mature models as attractive new prospects

The BBC reported on modeling scams targeting older models. Modeling scams aren't new, but it’s worth looking at how they spread today, how to spot them, and—most importantly—how to avoid falling victim to them. The classic pitch goes like this: Someone walks up to you in the street and says, "You...

EUVD-2011-0804

Malware in sbrugna...

EUVD-2020-18145

Malware in sbrugna...

EUVD-2023-24333

Malicious code in bioql PyPI...

EUVD-2021-6998

Malicious code in bioql PyPI...

Digital Threat Modeling Under Authoritarianism

Today's world requires us to make complex and nuanced decisions about our digital security. Evaluating when to use a secure messaging app like Signal or WhatsApp, which passwords to store on your smartphone, or what to share on social media requires us to assess risks and make judgments...

Ashlar-Vellum Cobalt Type Obfuscation Vulnerability (CNVD-2025-23022)

Ashlar-Vellum Cobalt is a 3D modeling software developed by Ashlar Vellum, which supports Windows and Mac systems, and is mainly used for 3D modeling and CAD drawing in industrial product design, architectural design and other fields. A type confusion vulnerability exists in Ashlar-Vellum Cobalt,...

Ashlar-Vellum Cobalt integer overflow vulnerability (CNVD-2025-22942)

Ashlar-Vellum Cobalt is a 3D modeling software developed by Ashlar Vellum, which supports Windows and Mac systems, and is mainly used for 3D modeling and CAD drawing in industrial product design, architectural design and other fields. An integer overflow vulnerability exists in Ashlar-Vellum...