CVE-2025-59188

CVE-2025-59188 is described as an information-disclosure vulnerability in the Windows Failover Cluster that could allow an authorized local attacker to disclose sensitive information. The available connected MS advisories and vulnerability lists indicate this affects Windows Failover Cluster and ...

GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise...

net.optionfactory.keycloak:optionfactory-keycloak-providers (>=8.1 <=8.9), org.keycloak.testframework:keycloak-test-framework-clustering (>=26.3.0 <=26.3.3) +21 more potentially affected by CVE-2025-9162 via org.keycloak:keycloak-model-storage-services (>=26.3.0 <=26.3.3)

org.keycloak:keycloak-model-storage-services MAVEN version =26.3.0, =8.1, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.3 and more Source cves: CVE-2025-...

EUVD-2016-1820

Malware in sbrugna...

EUVD-2019-13748

Malware in sbrugna...

EUVD-2018-8942

Malware in sbrugna...

EUVD-2020-20335

Malware in sbrugna...

EUVD-2015-4259

Malware in sbrugna...

EUVD-2019-13988

Malware in sbrugna...

Towards Reliable and Practical LLM Security Evaluations Via Bayesian Modelling

Before adopting a new large language model LLM architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fai...

EUVD-2023-1221

Malicious code in bioql PyPI...

EUVD-2023-49484

Malicious code in bioql PyPI...

EUVD-2024-1448

Malicious code in bioql PyPI...

EUVD-2024-3445

Malicious code in bioql PyPI...

EUVD-2024-2814

Malicious code in bioql PyPI...

EUVD-2023-1601

Malicious code in bioql PyPI...

Characterizing Event-Themed Malicious Web Campaigns: A Case Study on War-Themed Websites

Cybercrimes such as online scams and fraud have become prevalent. Cybercriminals often abuse various global or regional events as themes of their fraudulent activities to breach user trust and attain a higher attack success rate. These attacks attempt to manipulate and deceive innocent people int...

Characterizing Phishing Pages by JavaScript Capabilities

In 2024, the Anti-Phishing Work Group identified over one million phishing pages. Phishers achieve this scale by using phishing kits -- ready-to-deploy phishing websites -- to rapidly deploy phishing campaigns with specific data exfiltration, evasion, or mimicry techniques. In contrast, researche...

An Unsupervised Learning Approach for a Reliable Profiling of Cyber Threat Actors Reported Globally Based on Complete Contextual Information of Cyber Attacks

Cyber attacks are rapidly increasing with the advancement of technology and there is no protection for our information. To prevent future cyberattacks it is critical to promptly recognize cyberattacks and establish strong defense mechanisms against them. To respond to cybersecurity threats...

ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection

Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled dat...