Gepetto

Gepetto Gepetto is a Python plugin which uses various large language models to provide meaning to functions decompiled by IDA Pro ≥ 7.6. It can leverage them to explain what a function does, and to automatically rename its variables. Here is a simple example of what results it can provide in mere...

CVE-2026-105397

LearnPress plugin for WordPress through 4.4.9.1 contains a stored cross-site scripting vulnerability that allows authenticated instructors to inject scripts via quiz question hint and explanation fields. Attackers with the Instructor role can submit unsanitized payloads through the updatequestion...

EUVD-2026-92444

LearnPress plugin for WordPress through 4.4.9.1 contains a stored cross-site scripting vulnerability that allows authenticated instructors to inject scripts via quiz question hint and explanation fields. Attackers with the Instructor role can submit unsanitized payloads through the updatequestion...

CVE-2026-105397 LearnPress WordPress Plugin through 4.4.9.1 Stored XSS via Quiz Question Hint and Explanation

LearnPress plugin for WordPress through 4.4.9.1 contains a stored cross-site scripting vulnerability that allows authenticated instructors to inject scripts via quiz question hint and explanation fields. Attackers with the Instructor role can submit unsanitized payloads through the updatequestion...

CVE-2026-105397: Improper Neutralization of Input During Web Page Generation

LearnPress plugin for WordPress through 4.4.9.1 contains a stored cross-site scripting vulnerability that allows authenticated instructors to inject scripts via quiz question hint and explanation fields. Attackers with the Instructor role can submit unsanitized payloads through the updatequestion...

Dirty-Cow-Explanation-CVE-2016-5195-

Dirty-Cow-Explicación-CVE-2016-5195-...

HowCVE-2019-1083Works

This repository does not have a README...

TheDefendersGuide

La Guía del Defensor: Es peligroso defender solo, ¡toma esto! ¿Qué es la Guía del Defensor? La Guía del Defensor es un proyecto de Luke Paine y Jonathan Johnson para reunir en un solo lugar todos los mejores recursos de defensa para un aspecto particular de un sistema operativo. Con demasiada...

DockSec v2026.8.19

!CREATEDhttps://img.shields.io/badge/created-feb,%202025-blue?style=for-the-b...

Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection

Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free post-processing step. This study jointly evaluates predictive effectiveness, explanation cost, local explanation stability, and selective explanation...

Explaining Intrusion Alert Decisions of Deep Learning-Based Network Intrusion Detection Systems for Security Analysts

In this paper, we present EXP-SEC, a novel framework which can explain the intrusion detection decisions of DL-based NIDS which lead to security alerts in a way that is aligned with the domain knowledge of analysts working in Security Operations Center SOC. We highlight the following features of...

OPENSUSE-SU-2026:21277-1 Security update for go-sendxmpp

This update for go-sendxmpp fixes the following issues: Changes in go-sendxmpp: - Update to 0.16.0: Added: Add Ox support to http-upload. Add Ox support for private group chats. Show error cause if joining MUCs failedi requires go-xmpp = v0.3.5. Changed: Fix --ox-delete-nodes. Fix receiving of 1-...

MOLOT System Card: Malicious Operational Logic Observation Transformer

MOLOT Malicious Operational Logic Observation Transformer is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable. The system represents source code as behavior sequences derived...

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection IDS: multicollinearity-induced instability. Despite extensive reliance on post-hoc explainability tools such as SHAP or LIME, the impact of correlated features on explanation robustne...

Evaluating Retrieval-Augmented Generation for Explainable Malware Analysis

Large Language Models LLMs are increasingly being used as security engineering tools to summarize and explain malware behavior to analysts. A common assumption is that Retrieval-Augmented Generation RAG improves explanation quality by injecting external security knowledge. In this work, we...

Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection

Mixture-of-Experts MoE offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware explanations for MoE graph models in malware detection using control flow graphs CFGs. Our architecture builds diversity at two levels. At the node...

Exploit for CVE-2025-4517

CVE-2025-4517-P...

Human-Centered Explainability in AI-Enhanced UI Security Interfaces: Designing Trustworthy Copilots for Cybersecurity Analysts

Artificial intelligence AI copilots are increasingly integrated into enterprise cybersecurity platforms to assist analysts in threat detection, triage, and remediation. However, the effectiveness of these systems depends not only on the accuracy of underlying models but also on the degree to whic...

CAFE-GB: Scalable and Stable Feature Selection for Malware Detection Via Chunk-Wise Aggregated Gradient Boosting

High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learning-based malware detection systems. Although feature selection is commonly employed to mitigate these issues, many...

Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning Vs. Full Fine-Tuning

This study examines whether Low-Rank Adaptation LoRA fine-tuned Large Language Models LLMs can approximate the performance of fully fine-tuned models in generating human-interpretable decisions and explanations for malware classification. Achieving trustworthy malware detection, particularly when...