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...

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

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

HowCVE-2019-1083Works

このリポジトリには README がありません。...

TheDefendersGuide

The Defender's Guide - 一人で防御するのは危険だ。これを手に取れ！ The Defender's Guideとは？ The Defender's Guideは、Luke PaineとJonathan Johnsonによるプロジェクトで、オペレーティングシステムの特定の側面に関する最高の防御リソースをすべて1つの場所にまとめることを目的としています。 防御者である私たちは、防御しようとしているトピックを理解するために、ブログ記事、ドキュメント、フォーラムの投稿を載せたタブを900個も開いていなければならないことがあまりにも多いです。もうそんな必要はありません。...

DockSec v2026.8.19

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

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...

Microsoft raises the bar: A smarter way to measure AI for cybersecurity

ExCyTIn-Bench is Microsoft’s newest open-source benchmarking tool designed to evaluate how well AI systems perform real-world cybersecurity investigations.1 It helps business leaders assess language models by simulating realistic cyberthreat scenarios and providing clear, actionable insights into...

EUVD-2022-54493

Malicious code in bioql PyPI...

Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications

Explainable Artificial Intelligence XAI has aided machine learning ML researchers with the power of scrutinizing the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency...

ExpIDS: a Drift-Adaptable Network Intrusion Detection System with Improved Explainability

Despite all the advantages associated with Network Intrusion Detection Systems NIDSs that utilize machine learning ML models, there is a significant reluctance among cyber security experts to implement these models in real-world production settings. This is primarily because of their opaque natur...

Adversarial Attacks on VQA-NLE: Exposing and Alleviating Inconsistencies in Visual Question Answering Explanations

Natural language explanations in visual question answering VQA-NLE aim to make black-box models more transparent by elucidating their decision-making processes. However, we find that existing VQA-NLE systems can produce inconsistent explanations and reach conclusions without genuinely understandi...

Interpreting Differential Privacy in Terms of Disclosure Risk

As the use of differential privacy DP becomes widespread, the development of effective tools for reasoning about the privacy guarantee becomes increasingly critical. In pursuit of this goal, we demonstrate novel relationships between DP and measures of statistical disclosure risk. We suggest how...