CVE-2026-11244

creationtimestamp| type| source ---|---|--- 2026-06-05 13:24:34+00:00| seen| https://infosec.exchange/users/cR0w/statuses/116697713800926918 2026-06-07 18:00:00+00:00| seen| https://www.hkcert.org/security-bulletin/google-chrome-multiple-vulnerabilities20260608 2026-06-07 18:00:00+00:00| seen|...

FDM: A Framework for Decision-Making to Build ML-Based Malware Detection Systems

Selecting appropriate machine learning ML configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for...

The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis Via Audio Signals

Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats employ advanced evasion strategies such as code obfuscation, dynamic loading, packing, and even steganographic manipulation of traditional static and...

Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features

Machine learning-based intrusion detection systems IDS for RPL-based IoT networks often rely solely on routing layer features, which provide only a partial view of network behaviour. In this work, we investigate whether incorporating Transmit TX and Receive RX radio features alongside the standar...

From Operating Model to Product: How We Built the ROC for Detection-Speed Remediation

In the first article in this series, we made the case for a prevention-led operating model. This article is about what happened next: the decision to build something that did not exist, and what it took to make it real. Turning an operating model into a product sounds straightforward until you ar...

Quarterly WordPress Threat Intelligence Report – Q1 2026

As the leader in WordPress security, Wordfence provides unparalleled security coverage that fully encompasses protection, active monitoring, detection, and response all built around our threat intelligence, demonstrating a strong commitment to security. Our mission is to ensure comprehensive...

Strawberry GraphQL has a Circular Fragment Reference DOS

Summary The QueryDepthLimiter extension is vulnerable to an Application-level DOS due to a lack of cycle detection in fragment spreads. When a query contains circular fragment references the determinedepth function enters an infinite recursion, leading to a RecursionError and crashing the...

Hypotheses, telemetry, and human judgment: Inside Cisco Talos Threat Hunting

By Ron Scott-Adams Most security tools operate on a simple principle: If a known-bad pattern appears, fire an alert. This works well enough for many threats, but it fails against adversaries who closely study detection thresholds and deliberately stay under them. Cisco Talos Threat Hunting operat...

An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification,...

The vulnerability of the virus detection mechanism of the anti-virus software Trend Micro Apex One allows a hacker to elevate their privileges and execute arbitrary code.

The vulnerability of the antivirus software Trend Micro Apex One’s virus detection mechanism is related to the improper handling of symbolic links before accessing a file. Exploiting this vulnerability can allow an attacker to enhance their privileges and execute arbitrary code...

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

FOSSBilling 安全漏洞

FOSSBilling is an open-source billing and customer management platform for hosting service providers and digital service providers. Versions of FOSSBilling prior to 0.8.0 contained security vulnerabilities. These vulnerabilities stemmed from the password reset confirmation endpoint being...

Securing the Sandbox: A Rootless Containerized Framework for Process-Oriented Monitoring in Computer Graphics Education

Computer Science education fundamentally depends on intensive laboratory hours to foster true programming mastery and logical reasoning. However, the widespread adoption of Generative Artificial Intelligence AI has made it virtually impossible to distinguish authentic student effort from instant ...

Steering LLM Viewpoints through Fabricated Evidence Injection

As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated...

Next.js Concurrent Version Exposure / Vulnerability Audit Tool

This Python script is a lightweight defensive auditing utility designed to identify websites running Next.js and determine whether their detected version falls within predefined potentially vulnerable version ranges...

TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection

Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats. Using the SPARTA attack model, we analyze the latency-accuracy trade-offs of TinyML-compatible classical models -- Random Forest, Logistic Regression, SVM, and MLP -- for detecting uplink jamming...

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Rule-based Intrusion Detection and Prevention Systems IDPS offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets e.g., CICIDS2017, UNSW-NB15 focus on...

NLLog: Lightweight, Explainable SOC Anomaly Detection Via Log-To-Language Rewriting

System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog Natural-Language Log, a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools...

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-Based Vulnerability Detection with Open-Weight Models

Large language models LLMs have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation RAG settings. However, for approaches relying on proprietary models and APIs, reproducibility and replicability remain largely unexplored, raising...

Linux Kernel FD-Race Monitoring

This C program is a defensive process-monitoring utility, not a privilege-escalation exploit. It continuously inspects /proc/pid/fd to detect suspicious activity related to excessive file descriptor usage, activity from sensitive SUID binaries, and potential file descriptor leakage or theft...