EUVD-2021-0389

Malware in sbrugna...

EUVD-2021-0263

Malware in sbrugna...

EUVD-2021-0360

Malware in sbrugna...

EUVD-2021-12112

Malware in sbrugna...

EUVD-2021-0366

Malware in sbrugna...

EUVD-2010-0979

Malware in sbrugna...

EUVD-2021-0282

Malware in sbrugna...

EUVD-2021-0280

Malware in sbrugna...

EUVD-2005-4334

Malware in sbrugna...

EUVD-2021-0348

Malware in sbrugna...

EUVD-2021-0315

Malware in sbrugna...

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection

Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not only incurs substantial annotation costs but also limits...

Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...

How we trained an ML model to detect DLL hijacking

DLL hijacking is a common technique in which attackers replace a library called by a legitimate process with a malicious one. It is used by both creators of mass-impact malware, like stealers and banking Trojans, and by APT and cybercrime groups behind targeted attacks. In recent years, the numbe...

Detecting DLL hijacking with machine learning: real-world cases

Introduction Our colleagues from the AI expertise center recently developed a machine-learning model that detects DLL-hijacking attacks. We then integrated this model into the Kaspersky Unified Monitoring and Analysis Platform SIEM system. In a separate article, our colleagues shared how the mode...

P2P: A Poison-To-Poison Remedy for Reliable Backdoor Defense in LLMs

During fine-tuning, large language models LLMs are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings...

EUVD-2025-32445

A vulnerability has been found in Frappe LMS 2.35.0. The affected element is an unknown function of the file /courses/ of the component Unpublished Course Handler. Such manipulation leads to improper access controls. The attack may be launched remotely. This attack is characterized by high...

CVE-2025-11283 Frappe LMS Course cross site scripting

A vulnerability was determined in Frappe LMS 2.35.0. This affects an unknown function of the component Course Handler. Executing manipulation of the argument Description can lead to cross site scripting. The attack can be executed remotely. The exploit has been publicly disclosed and may be...

CVE-2025-11283 Frappe LMS Course cross site scripting

A vulnerability was determined in Frappe LMS 2.35.0. This affects an unknown function of the component Course Handler. Executing manipulation of the argument Description can lead to cross site scripting. The attack can be executed remotely. The exploit has been publicly disclosed and may be...

CVE-2025-11282

CVE-2025-11282 affects Frappe LMS 2.34.x/2.35.0 due to an incomplete fix for CVE-2025-55006, enabling cross-site scripting via manipulated input. The vulnerability allows remote exploitation and an exploit has been publicized. The issue is linked to an unknown function in the affected component; ...