Hitachi Energy AFS, AFR and AFF Series

RISK EVALUATION Successful exploitation of this vulnerability could compromise the integrity of the product data and disrupt its availability. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability, such as: Minimize...

PentestEval: Benchmarking LLM-Based Penetration Testing with Modular and Stage-Level Design

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models LLMs offer promising opportunities for...

PT-2025-51779

Name of the Vulnerable Software and Affected Versions Expr versions prior to 1.17.7 Description The Expr library, used for expression language and evaluation in Go, contains a flaw where certain builtin functions – including flatten, min, max, mean, and median – can cause a denial of service. The...

Behavior-Aware and Generalizable Defense against Black-Box Adversarial Attacks for ML-Based IDS

Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed...

📄 FoxCMS 1.0 Code Injection

FoxCMS version 1.0 proof of concept remote code injection exploit. ============================================================================================================================================= | Title : FoxCMS v1.0 php code innjection | | Author : indoushka | | Tested on : windows...

Arbitrary Command Injection

Overview Affected versions of this package are vulnerable to Arbitrary Command Injection via the evaluation of credential values in non-POSIX shell environments. An attacker can execute arbitrary commands on the operator's device by crafting malicious credential values in infrastructure Secret...

Arbitrary Command Injection

Overview Affected versions of this package are vulnerable to Arbitrary Command Injection via the evaluation of credential values in non-POSIX shell environments. An attacker can execute arbitrary commands on the operator's device by crafting malicious credential values in infrastructure Secret...

LLM-Assisted AHP for Explainable Cyber Range Evaluation

Cyber Ranges CRs have emerged as prominent platforms for cybersecurity training and education, especially for Critical Infrastructure CI sectors that face rising cyber threats. One way to address these threats is through hands-on exercises that bridge IT and OT domains to improve defensive...

LLM-PEA: Leveraging Large Language Models against Phishing Email Attacks

Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language Models LLMs applications, these systems face evolving phishing email threats that exploit their fundamental architectures. Current LLMs require...

Chasing Shadows: Pitfalls in LLM Security Research

Large language models LLMs are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of reproducibility, rigor, and evaluation. Prior work has identified common pitfalls in traditional machine learning researc...

How to Streamline Zero Trust Using the Shared Signals Framework

Zero Trust helps organizations shrink their attack surface and respond to threats faster, but many still struggle to implement it because their security tools don't share signals reliably. 88% of organizations admit they've suffered significant challenges in trying to implement such approaches,...

Integrating Public Input and Technical Expertise for Effective Cybersecurity Policy Formulation

The evolving of digital transformation and increased use of technology comes with increased cyber vulnerabilities, which compromise national security. Cyber-threats become more sophisticated as the technology advances. This emphasises the need for strong risk mitigation strategies. To define stro...

A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities across Clinical Specialties

Medical Large Language Models LLMs are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU cluster...

Towards Small Language Models for Security Query Generation in SOC Workflows

Analysts in Security Operations Centers routinely query massive telemetry streams using Kusto Query Language KQL. Writing correct KQL requires specialized expertise, and this dependency creates a bottleneck as security teams scale. This paper investigates whether Small Language Models SLMs can...

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features

Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...

OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation

Recent advances in multi-modal large language models MLLMs have enabled unified perception-reasoning capabilities, yet these systems remain highly vulnerable to jailbreak attacks that bypass safety alignment and induce harmful behaviors. Existing benchmarks such as JailBreakV-28K, MM-SafetyBench,...

TeleAI-Safety: A Comprehensive LLM Jailbreaking Benchmark Towards Attacks, Defenses, and Evaluations

While the deployment of large language models LLMs in high-value industries continues to expand, the systematic assessment of their safety against jailbreak and prompt-based attacks remains insufficient. Existing safety evaluation benchmarks and frameworks are often limited by an imbalanced...

Johnson Controls OpenBlue Mobile Web Application for OpenBlue Workplace

RISK EVALUATION Successful exploitation of this vulnerability could allow an attacker to gain unauthorized access to sensitive information. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability, such as: Minimize network...

Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical HQC autoencoders for this task. We construct a unified experimental framework that...

Safe2Harm: Semantic Isomorphism Attacks for Jailbreaking Large Language Models

Large Language Models LLMs have demonstrated exceptional performance across various tasks, but their security vulnerabilities can be exploited by attackers to generate harmful content, causing adverse impacts across various societal domains. Most existing jailbreak methods revolve around Prompt...