EUVD-2013-1182

Malware in sbrugna...

EUVD-2019-10937

Malware in sbrugna...

EUVD-2016-10037

Malware in sbrugna...

EUVD-2020-18776

Malware in sbrugna...

EUVD-2021-1451

Malware in sbrugna...

EUVD-2024-2854

Malicious code in bioql PyPI...

EUVD-2025-0141

Malicious code in bioql PyPI...

EUVD-2023-1604

Malicious code in bioql PyPI...

[SECURITY] Fedora 43 Update: dnsdist-2.0.1-1.fc43

dnsdist is a highly DNS-, DoS- and abuse-aware loadbalancer. Its goal in life is to route traffic to the best server, delivering top performance to legitimate users while shunting or blocking abusive traffic...

Breaking the Code: Security Assessment of AI Code Agents through Systematic Jailbreaking Attacks

Code-capable large language model LLM agents are increasingly embedded into software engineering workflows where they can read, write, and execute code, raising the stakes of safety-bypass "jailbreak" attacks beyond text-only settings. Prior evaluations emphasize refusal or harmful-text detection...

KuppingerCole 2025: Why Thales is a Market Leader in API Security

APIs are the backbone of modern applications connecting critical microservices and enabling enterprises to turn data into context-aware business logic via AI across their digital services. As applications become more contextual, APIs expose the data, workflows, and model interactions attackers...

MoPE: a Mixture of Password Experts for Improving Password Guessing

Textual passwords remain a predominant authentication mechanism in web security. To evaluate their strength, existing research has proposed several data-driven models across various scenarios. However, these models generally treat passwords uniformly, neglecting the structural differences among...

vxscan

VXScan+ VXScan+ is an advanced Python-based web vulnerabili...

Schrodinger'S Toolbox: Exploring the Quantum Rowhammer Attack

Residual cross-talk in superconducting qubit devices creates a security vulnerability for emerging quantum cloud services. We demonstrate a Clifford-only Quantum Rowhammer attack-using just X and CNOT gates-that injects faults on IBM's 127-qubit Eagle processors without requiring pulse-level...

Between a Rock and a Hard Place: Exploiting Ethical Reasoning to Jailbreak LLMs

Large language models LLMs have undergone safety alignment efforts to mitigate harmful outputs. However, as LLMs become more sophisticated in reasoning, their intelligence may introduce new security risks. While traditional jailbreak attacks relied on singlestep attacks, multi-turn jailbreak...

Linux Distros Unpatched Vulnerability : CVE-2022-4289

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - An issue has been discovered in GitLab affecting all versions starting from 15.3 before 15.7.8, versions of 15.8 before 15.8.4, and version 15.9 before 15.9.2...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation...

LLM-GUARD: Large Language Model-Based Detection and Repair of Bugs and Security Vulnerabilities in C++ and Python

Large Language Models LLMs such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugging. Yet, their real-world effectiveness in detecting diverse software bugs, particularly complex, security-relevant...

KillChainGraph: ML Framework for Predicting and Mapping ATT&CK Techniques

The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain...