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...

FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis

As over 200 million new malware samples are identified each year, antivirus systems must continuously adapt to the evolving threat landscape. However, retraining solely on new samples leads to catastrophic forgetting and exploitable blind spots, while retraining on the entire dataset incurs...

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...

Machine Learning Engineering Open Book security vulnerabilities

Machine Learning Engineering Open Book is a collection of methodologies for training and fine-tuning large language models developed by Stas Bekman. There is a security vulnerability in Machine Learning Engineering Open Book. This vulnerability arises from the use of the torch-checkpoint-shrink.p...

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...

Microsoft Azure Machine Learning injection vulnerability

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...

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...

Azure Machine Learning Website Structure Spear Phishing

The vulnerability of the software for working with Azure Machine Learning algorithms is related to the lack of security measures taken to protect the website structure. Exploiting this vulnerability allows a malicious actor to perform spear-phishing attacks remotely...

KLA91034 Multiple vulnerabilities in Microsoft Azure

Multiple vulnerabilities were found in Microsoft Azure. Malicious users can exploit these vulnerabilities to spoof user interface, bypass security restrictions, gain privileges. Below is a complete list of vulnerabilities: 1. A spoofing vulnerability in Azure Machine Learning Notebook can be...

strix-advanced

⚡ Strix-Advanced AI-Powered Security Testing Platform An...

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

The integration of Large Language Models LLMs into Electronic Design Automation EDA and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level RTL code, automating testbenches, and bridging the semantic...

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection

Software vulnerability detection is critical for ensuring software security and reliability. Despite recent advances in deep learning, real-world vulnerability datasets suffer from two severe challenges: frequency imbalance and difficulty imbalance. We reinterpret these challenges from an embeddi...

CVE-2026-3007

Successful exploitation of the stored cross-site scripting XSS vulnerability could allow an attacker to execute arbitrary JavaScript on any user account that has access to Koollab LMS’ courselet feature...

UBUNTU-CVE-2022-50943

Moodle LMS 4.0 contains a cross-site scripting vulnerability that allows unauthenticated attackers to inject malicious scripts by submitting payloads through the search parameter. Attackers can inject JavaScript code via the search field in course/search.php to execute arbitrary scripts in users'...

CVE-2021-47907

Rocket LMS 1.1 contains a persistent cross-site scripting vulnerability in the support ticket module that allows authenticated users to inject malicious script code through the title parameter. Attackers can submit support tickets with embedded HTML/JavaScript payloads that execute in the browser...

PT-2026-39496

Rocket LMS 1.1 contains a persistent cross-site scripting vulnerability in the support ticket module that allows authenticated users to inject malicious script code through the title parameter. Attackers can submit support tickets with embedded HTML/JavaScript payloads that execute in the browser...

Strategic Commitments Shape Collective Cybersecurity under AI Inequality

The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence tools is uneven, resource-limited defenders may be unable to adopt effective protection, creating persistent system vulnerabilities. We study the...

Operationalizing Cybersecurity Governance for Mitigation Planning with Attack-Path Modeling and Reinforcement Learning

We address a fundamental challenge in cybersecurity operations of translating governance frameworks into actionable mitigation decisions under realistic resource constraints. Frameworks such as the NIST Cybersecurity Framework CSF provide widely adopted measures of organizational maturity, but do...