PT-2026-44004

Name of the Vulnerable Software and Affected Versions LibVNCClient versions prior to 0.9.16 Description The Tight encoding decoder in LibVNCClient uses fixed-size 2048-pixel scratch buffers for the Gradient filter but fails to reject Tight rectangles with a width exceeding 2048 pixels. A maliciou...

LibVNCServer 缓冲区错误漏洞

LibVNCServer is a cross-platform C language library developed by LibVNC, which supports implementing VNC Virtual Network Computing server or client functions within programs. Versions of LibVNCServer prior to 0.9.15 contained a buffer error vulnerability. This vulnerability stemmed from the Tight...

A Surveillance Evasion Game with Continuous Sensor Redeployment Via Bilevel Optimization

Uncrewed Aerial Systems UASs have become a growing threat to the security of critical infrastructure, exploiting spatiotemporal gaps in sensor perimeters to infiltrate restricted airspace undetected. We formulate this interaction as a two-player zero-sum differential game between an adversarial U...

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 ...

Learning to Look Benign: Targeted Evasion of Malware Detectors Via API Import Injection

Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes them vulnerable to adversarial manipulation. This paper investigates whether a malware sample can be intentionally misclassified as a specific beni...

A No-Defense Defense against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?

Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning ML models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently robust Deep Neural Network DNN-based Network Intrusion Detection Systems NIDS,...

CVE-2026-44592

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENTDISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

CVE-2026-44592

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENTDISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

CVE-2026-44592 Gradient: Unauthenticated worker on /proto → arbitrary NAR write / cache poisoning

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENTDISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

CVE-2026-44592 Gradient: Unauthenticated worker on /proto → arbitrary NAR write / cache poisoning

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENTDISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

CVE-2026-44592 Gradient: Unauthenticated worker on /proto → arbitrary NAR write / cache poisoning

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENTDISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

CVE-2026-44592

Gradient is a nix-based CI system. In version 1.1.0, when GRADIENT_DISCOVERABLE=true (default), an unauthenticated actor that can reach /proto can register as a worker using a fresh UUID. The resulting session is PeerAuth::Open, allowing access to jobs from any organization, and can immediately N...

PT-2026-41018

Gradient is a nix-based continuous integration system. In 1.1.0, when GRADIENT DISCOVERABLE=true the default, and the NixOS module default, anyone who can reach /proto can register as a worker without any credentials by sending a fresh, never-registered worker UUID. The resulting session has...

Gradient 访问控制错误漏洞

Gradient is a modern Nix continuous integration system developed by Wavelens. Version 1.1.0 of Gradient contains an access control vulnerability caused by unvalidated registration credentials. This vulnerability allows attackers to register as working nodes and access arbitrary storage paths...

Context-Aware Web Attack Detection in Open-Source SIEM Systems Via MITRE ATT&CK-Enriched Behavioral Profiling

Security Information and Event Management SIEM systems aggregate log data from heterogeneous sources to detect coordinated attacks. Traditional rule-based correlation engines struggle to classify multi-step web application attacks because they examine each event without reference to the behaviour...

Gray-Box Poisoning of Continuous Malware Ingestion Pipelines

Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work investigates a realistic gray-box poisoning threat model targeting these pipelines. Using the secmlmalware framework, we generate problem-space...

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, highlighting the urgent need for early-stage detection before encryption is...

GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance

Intrusion Detection System IDS is often calibrated to known attacks and generalizes poorly to unknown threats. This paper proposes GMA-SAWGAN-GP, a novel generative augmentation framework built on a Self-Attention-enhanced Wasserstein GAN with Gradient Penalty WGAN-GP. The generator employs...

A Novel Solution for Zero-Day Attack Detection in IDS Using Self-Attention and Jensen-Shannon Divergence in WGAN-GP

The increasing sophistication of cyber threats, especially zero-day attacks, poses a significant challenge to cybersecurity. Zero-day attacks exploit unknown vulnerabilities, making them difficult to detect and defend against. Existing approaches patch flaws and deploy an Intrusion Detection Syst...

PT-2026-25647

An out‑of‑bounds write vulnerability exists in the EMF functionality of Canva Affinity. By using a specially crafted EMF file, an attacker could exploit this vulnerability to perform an out‑of‑bounds write, potentially leading to code execution...