trivy

📖 Documentation Trivy pronunciation is a comprehensive and versatile security scanner. Trivy has scanners that look for security issues, and targets where it can find those issues. Targets what Trivy can scan: Container Image Filesystem Git Repository remote Virtual Machine Image Kubernetes...

seeker

Available in Concept behind Seeker is simple, just like we host phishing pages to get credentials why not host a fake page that requests your location like many popular location based websites. Read more on thewhiteh4t's Blog .Seeker Hosts a fake website which asks for Location Permission and if...

socialscan

socialscan socialscan offers accurate and fast checks for email address and username usage on online platforms. Given an email address or username, socialscan returns whether it is available, taken or invalid on online platforms. Features that differentiate socialscan from similar tools e.g...

CVE-2026-53370

A flaw was found in the Linux kernel's performance monitoring unit perf/x86/intel related to the validation and configuration of Auto Counter Reload ACR masks. Incomplete validation of user-space ACR masks could lead to incorrect configurations. This issue could result in inaccurate performance...

Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security

Agent skills let LLM agents reuse instructions, resources, tools, and workflows, but they also create a new place for malicious behavior to hide. A skill may look benign in its documentation or code while becoming harmful only when it is invoked with particular user requests, local assets,...

MGASA-2026-0181 Updated suricata packages fix security vulnerabilities

Various security, performance, accuracy, and stability issues have been fixed, plus we have moved to a supported version...

Updated suricata packages fix security vulnerabilities

Various security, performance, accuracy, and stability issues have been fixed, plus we have moved to a supported version...

A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems

The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and availability of advanced features. Yet, on the other hand the increasing number of interconnecting components and moving parts requires maintaining an...

On the Study of Biometric Spoofing Detection Using Deep Learning

Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models,...

TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection

Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats. Using the SPARTA attack model, we analyze the latency-accuracy trade-offs of TinyML-compatible classical models -- Random Forest, Logistic Regression, SVM, and MLP -- for detecting uplink jamming...

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-Based Vulnerability Detection with Open-Weight Models

Large language models LLMs have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation RAG settings. However, for approaches relying on proprietary models and APIs, reproducibility and replicability remain largely unexplored, raising...

Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems. However, they increase computational complexity and may hinder deployment in resource-constrained environments. In this paper, we investigate feature compression techniques for...

High-Precision APT Malware Attribution with Out-Of-Scope Resilience

Early attribution of Advanced Persistent Threat APT activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion. Malware provides useful attribution evidence, but automated APT malware attribution remains difficult in practice. Existing...

Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection

We show that LoRA adapters, the dominant distribution format for fine-tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a...

Adversarial Vulnerability under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection

We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extracted from emulator and real-device executions. The dataset is organized into yearly slices and evaluated under three...

When the Ruler Is Broken: Parsing-Induced Suppression in LLM-Based Security Log Evaluation

LLM-based SOC log classifiers are commonly evaluated using regular-expression pipelines that extract structured fields from free-form model output. We demonstrate that this practice introduces a class of silent, systematic evaluation errors, which we term parsing-induced suppression that can caus...

On the Security of Research Artifacts

Research artifacts are widely shared to support reproducibility, and artifact evaluation AE has become common at many leading conferences. However, AE mainly checks whether artifacts work as claimed and can be reproduced. It largely overlooks potential security risks. Since these artifacts are...

Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents

Large Language Models LLMs have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion: the automated and in-depth personal profiling; this engenders a chilling effect of "peepers everywhere". Existing...

Formulating Subgroup Discovery As a Quantum Optimization Problem for Network Security

While current network intrusion detection systems achieve satisfactory accuracy, they often lack explainability. Subgroup Discovery SD addresses this by building interpretable rules that characterize feature interactions associated with attack traffic. With large datasets, classical heuristic bea...

Towards Agentic Investigation of Security Alerts

Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an experimental, agentic...