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...

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...

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...

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 Out-of-Bounds Read Vulnerability (CNVD-2025-22913)

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. Ashlar-Vellum Cobalt suffers from an out-of-bounds read...

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...

Ashlar-Vellum Cobalt Memory Corruption Vulnerability

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 memory corruption vulnerability exists in Ashlar-Vellum Coba...

Ashlar-Vellum Cobalt Out-of-Bounds Read Vulnerability (CNVD-2025-22916)

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. Ashlar-Vellum Cobalt suffers from an out-of-bounds read...

Ashlar-Vellum Cobalt Integer Overflow Vulnerability

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. Ashlar-Vellum Cobalt suffers from an integer overflow...

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 安全漏洞

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,...