Introducing EvidenceForge: Synthetic security logs that don’t look (as) fake

Security teams need high-quality, labeled datasets to train threat hunters and incident responders, validate detection logic, and develop robust analytic models. EvidenceForge helps teams overcome the limitations of anonymized or stale public datasets, while avoiding the cost and complexity of...

Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems

Cyber-Physical Systems CPS integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility domain, various controllers are used in CPS to ensure the system detects and...

EUVD-2026-31642

A vulnerability in MLflow versions =3.10.1.dev0 allows unauthorized access to multipart upload MPU endpoints when the --serve-artifacts mode is enabled. The authorization logic does not enforce resource-level permission checks for /mlflow-artifacts/mpu/ endpoints, enabling attackers to overwrite...

"What Is the Problem Space?" Defining Host-Space Adversarial Perturbations against Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS are now increasingly leveraging Machine Learning ML techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS ML-NIDS by testing them against various attacks involving adversarial perturbations. The...

Malicious code in sklern (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 1495d93dccc77a422f70d192ef4d8dcd53b0c990fff43e68bc2a0eca301e5d10 Package name 'sklern' is a one-character deletion from the top-tier ML package 'sklearn', and its public API linearregression, logisticregression,...

Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions

Electric Vehicles EVs have emerged as significant disruptors in the transportation sector over the past decade. Their growing popularity and adoption are accompanied by capital expenditures to deploy charging infrastructure. EV charging infrastructure sits at the intersection of the power grid, t...

Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation Using Machine Learning Algorithms

The advent of cardless artificial intelligence AI banking heralds a paradigm shift in the financial landscape, offering users unprecedented security and convenience. This paper outlines a comprehensive framework designed to enhance cybersecurity, introduce auto-generated virtual cards, and mitiga...

MLflow: Improper Origin Validation in MLflow Assistant /ajax-api Endpoints Enables Browser-Mediated Local Command Execution

In MLflow version 3.9.0, the MLflow Assistant feature introduced improper origin validation in its /ajax-api endpoints. This vulnerability allows a remote attacker to exploit cross-origin requests from a malicious webpage to interact with the MLflow Assistant running on a victim's local machine. ...

Agent Security Is a Systems Problem

We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness the dominant viewpoint...

Advance_WAF_project_CS

WAFinity - Infinite Protection, Intelligent Detection WAFin...

Chromium: CVE-2026-8531 Heap buffer overflow in WebML

This CVE was assigned by Chrome. Microsoft Edge Chromium-based ingests Chromium, which addresses this vulnerability. Please see Google Chrome Releases for more information...

CVE-2026-33833

Improper neutralization of special elements in output used by a downstream component 'injection' in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network...

EUVD-2026-29580

Improper neutralization of special elements in output used by a downstream component 'injection' in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network...

CVE-2026-33833

Improper neutralization of special elements in output used by a downstream component 'injection' in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network...

CVE-2026-33833 Azure Machine Learning Notebook Spoofing Vulnerability

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CVE-2026-33833 Azure Machine Learning Notebook Spoofing Vulnerability

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CVE-2026-33833

Azure Machine Learning is affected where the issue occurs in the downstream component’s output handling, described as an improper neutralization of special elements that enables network spoofing. The CVE-2026-33833 entry notes an attacker could exploit this via a network vector with no user inter...

Azure Machine Learning Notebook Spoofing Vulnerability

Improper neutralization of special elements in output used by a downstream component 'injection' in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network...

Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

This study investigates the potential for deanonymizing services within the Invisible Internet Project I2P network through passive traffic analysis and machine learning techniques. The primary objective is to identify distinctive patterns in I2P traffic despite the encryption of its payload. To...

Microsoft Azure Machine Learning 注入漏洞

Microsoft Azure Machine Learning is a machine learning service provided by Microsoft Corporation in the United States. There is an injection vulnerability present in Microsoft Azure Machine Learning. Attackers utilize this vulnerability to carry out phishing attacks...