319 matches found
EUVD-2025-32818
In the Linux kernel, the following vulnerability has been resolved: btrfs: reject invalid reloc tree root keys with stack dump BUG Syzbot reported a crash that an ASSERT got triggered inside preparetomerge. That ASSERT makes sure the reloc tree is properly pointed back by its subvolume tree. CAUS...
EUVD-2021-0388
Malware in sbrugna...
EUVD-2017-9327
Malware in sbrugna...
EUVD-2009-1785
Malware in sbrugna...
EUVD-2021-0430
Malware in sbrugna...
EUVD-2023-1907
Malicious code in bioql PyPI...
EUVD-2025-25079
Malicious code in bioql PyPI...
BehaviorTree.CPP 代码问题漏洞
BehaviorTree.CPP is a library for behavior trees in C++ open-sourced by BehaviorTree. A code issue vulnerability exists in BehaviorTree.CPP version 4.7.0 and earlier, which stems from incorrect manipulation of the parameter Source of the function JsonExporter::fromJson in the file...
STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generation
In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive,...
A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks
The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network-based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently...
Bridging Threat Models and Detections: Formal Verification Via CADP
Threat detection systems rely on rule-based logic to identify adversarial behaviors, yet the conformance of these rules to high-level threat models is rarely verified formally. We present a formal verification framework that models both detection logic and attack trees as labeled transition syste...
CVE-2025-38519
In the Linux kernel, the following vulnerability has been resolved: mm/damon: fix divide by zero in damongetintervalsscore The current implementation allows having zero size regions with no special reasons, but damongetintervalsscore gets crashed by divide by zero when the region size is zero...
CVE-2025-38519
In the Linux kernel, the following vulnerability has been resolved: mm/damon: fix divide by zero in damongetintervalsscore The current implementation allows having zero size regions with no special reasons, but damongetintervalsscore gets crashed by divide by zero when the region size is zero...
CVE-2025-38519 mm/damon: fix divide by zero in damon_get_intervals_score()
In the Linux kernel, the following vulnerability has been resolved: mm/damon: fix divide by zero in damongetintervalsscore The current implementation allows having zero size regions with no special reasons, but damongetintervalsscore gets crashed by divide by zero when the region size is zero...
CVE-2025-38519
The CVE-2025-38519 entry pertains to the Linux kernel (mm/damon) and is supported by multiple sources in the connected documents. The root cause is a divide-by-zero crash in damon_get_intervals_score() when region size is zero. The current patch fixes the bug without disallowing zero-size regions...
CVE-2025-38519
In the Linux kernel, the following vulnerability has been resolved: mm/damon: fix divide by zero in damongetintervalsscore The current implementation allows having zero size regions with no special reasons, but damongetintervalsscore gets crashed by divide by zero when the region size is zero...
MAL-2025-36374 Malicious code in test-mlw2-surer-trees (npm)
The package test-mlw2-surer-trees was found to contain malicious code...
Malicious code in test-mlw2-surer-trees (npm)
The package test-mlw2-surer-trees was found to contain malicious code...
BarkBeetle: Stealing Decision Tree Models with Fault Injection
Machine learning models, particularly decision trees DTs, are widely adopted across various domains due to their interpretability and efficiency. However, as ML models become increasingly integrated into privacy-sensitive applications, concerns about their confidentiality have grown, particularly...