"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...

The vulnerability of the software for working with Azure Machine Learning algorithms lies in the lack of measures taken to neutralize special elements in the output data, allowing attackers to perform spoofing attacks.

The vulnerability of the software for working with Azure Machine Learning algorithms is related to the lack of measures taken to neutralize special elements in the output data. Exploiting this vulnerability allows a remote attacker to perform spearishing attacks...

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...

The vulnerability of the REST API interface of the H2O machine learning platform allows a perpetrator to execute arbitrary code.

The vulnerability of the REST API interface of the H2O machine learning platform is related to deficiencies in the deserialization mechanism. Exploiting this vulnerability allows a remote attacker to execute arbitrary code...

The vulnerability of the H2O machine learning platform, related to deficiencies in the deserialization mechanism, allows attackers to bypass security restrictions, disclose sensitive information, and execute arbitrary code.

The vulnerability of the H2O machine learning platform is related to deficiencies in the deserialization mechanism. Exploiting this vulnerability allows a remote attacker to circumvent security restrictions, disclose sensitive information, and execute arbitrary code...

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 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...

CVE-2026-33833 Azure Machine Learning Notebook Spoofing Vulnerability

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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...

PT-2026-40141

Name of the Vulnerable Software and Affected Versions Azure Machine Learning affected versions not specified Description Improper neutralization of special elements in output used by a downstream component allows an unauthorized attacker to perform spoofing over a network. This issue can lead to...

CVE-2026-33833: Improper Neutralization of Special Elements in Output Used by a Downstream Component

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...