CVE-2026-9749

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer that is, many results are routed to the same consumer,...

CVE-2026-9749

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer that is, many results are routed to the same consumer,...

CVE-2026-9749 Using MaxKey() may crash the server

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer that is, many results are routed to the same consumer,...

CVE-2026-9749 Using MaxKey() may crash the server

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer that is, many results are routed to the same consumer,...

CVE-2026-9749

The CVE-2026-9749 entry describes a bug in MongoDB where an aggregation pipeline using the internal $exchange stage with key-range partitioning and order-preserving delivery can cause a server crash. When a single key range produces many results that fill its exchange buffer, the code path detect...

Using MaxKey() may crash the server

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer that is, many results are routed to the same consumer,...

PT-2026-50570

Name of the Vulnerable Software and Affected Versions vantage6 versions prior to 5.0.0 Description Malicious algorithms can potentially access input and output files belonging to other algorithms. Recommendations Update to version 5.0.0. As a temporary workaround, verify and restrict the algorith...

OpenSSH: OpenSSH: Privilege escalation via scp legacy protocol when not preserving file mode

A flaw was found in OpenSSH. When the scp command is used by a root user to download a file with the legacy protocol option -O and without preserving original file permissions -p, the downloaded file can be installed with elevated privileges setuid or setgid. This unexpected behavior could allow ...

CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering

Large language models LLMs are increasingly applied to cybersecurity question answering QA for critical tasks such as incident response and vulnerability analysis. However, real-world operational contexts, including system logs and network configurations, inherently contain sensitive identifiers,...

XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

An Intrusion Detection System IDS is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering ...

AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics

Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...

Analyzing Unsolicited Internet Traffic: Measuring IoT Security Threats Via Network Telescopes

Network telescopes serve as a critical passive monitoring tool for capturing unsolicited Internet traffic, providing insights into global scanning and reconnaissance behavior. This study analyzes a 10-day dataset during January 2025 consisting of approximately 22 million packets collected by the...

CVE-2026-35365

The mv utility in uutils coreutils improperly handles directory trees containing symbolic links during moves across filesystem boundaries. Instead of preserving symlinks, the implementation expands them, copying the linked targets as real files or directories at the destination. This can lead to...

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

Cross-site scripting XSS remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious payload while preserving its behavior. These transformations make it difficult for traditional and machine learning-based detection systems to reliab...

QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection

Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device HID emulation attacks, such as those enabled by the USB Rubber Ducky, exploit this assumption to inject arbitrary keystroke sequences while...

Towards Automated Pentesting with Large Language Models

Large Language Models LLMs are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human intervention. While attackers take advantage of dark LLMs such as XXXGPT and WolfGPT to produce malicious code, ethical hackers can follow similar approaches to...

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

Malware and Ransomware Detection in M365

Availability Requirement Threat Detection is available to Veeam Data Cloud for Microsoft 365 customers with Premium or Advanced plans. Customers must opt in to AI settings to enable this feature. Contact your Veeam account team or see your plan details to confirm availability. Supported Workloads...

Security Awareness in LLM Agents: The NDAI Zone Case

NDAI zones let inventor and investor agents negotiate inside a Trusted Execution Environment TEE where any disclosed information is deleted if no deal is reached. This makes full IP disclosure the rational strategy for the inventor's agent. Leveraging this infrastructure, however, requires agents...

CLIOPATRA: Extracting Private Information from LLM Insights

As AI assistants become widely used, privacy-aware platforms like Anthropic's Clio have been introduced to generate insights from real-world AI use. Clio's privacy protections rely on layering multiple heuristic techniques together, including PII redaction, clustering, filtering, and LLM-based...