CVE-2024-43745

creationtimestamp| type| source ---|---|--- 2025-09-16 23:16:38+00:00| seen| MISP/be792712-f638-4d7d-b62d-4f5032e86764 2025-09-18 16:44:33+00:00| seen| MISP/be792712-f638-4d7d-b62d-4f5032e86764...

ExploitRemotingService

This is a .NET Remoting Service exploit tool. It is a proof-of-concept PoC exploit for a vulnerability in the .NET Remoting Service. The tool is designed to demonstrate the exploitation of this vulnerability, which allows an attacker to execute arbitrary code on a remote system. The tool consists...

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...

Bridging the Gap in Phishing Detection: a Comprehensive Phishing Dataset Collector

To combat phishing attacks -- aimed at luring web users to divulge their sensitive information -- various phishing detection approaches have been proposed. As attackers focus on devising new tactics to bypass existing detection solutions, researchers have adapted by integrating machine learning a...

PARROT: Portable Android Reproducible Traffic Observation Tool

The rapid evolution of mobile security protocols and limited availability of current datasets constrains research in app traffic analysis. This paper presents PARROT, a reproducible and portable traffic capture system for systematic app traffic collection using Android Virtual Devices. The system...

Fraud Detection and Risk Assessment of Online Payment Transactions on E-Commerce Platforms Based on LLM and GCN Frameworks

With the rapid growth of e-commerce, online payment fraud has become increasingly complex, posing serious threats to financial security and consumer trust. Traditional detection methods often struggle to capture the intricate relational structures inherent in transactional data. This study presen...

Phish-Blitz: Advancing Phishing Detection with Comprehensive Webpage Resource Collection and Visual Integrity Preservation

Phishing attacks are increasingly prevalent, with adversaries creating deceptive webpages to steal sensitive information. Despite advancements in machine learning and deep learning for phishing detection, attackers constantly develop new tactics to bypass detection models. As a result, phishing...

PatchSeeker: Mapping NVD Records to Their Vulnerability-Fixing Commits with LLM Generated Commits and Embeddings

Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database NVD is the authoritative source for cataloging these vulnerabilities, it often lacks explicit links to the corresponding Vulnerability-Fixing Commits VFCs. VFCs encode precise code...

Backdoor Attacks and Defenses in Computer Vision Domain: a Survey

Backdoor trojan attacks embed hidden, controllable behaviors into machine-learning models so that models behave normally on benign inputs but produce attacker-chosen outputs when a trigger is present. This survey reviews the rapidly growing literature on backdoor attacks and defenses in the...

Signal-Based Malware Classification Using 1D CNNs

Malware classification is a contemporary and ongoing challenge in cyber-security: modern obfuscation techniques are able to evade traditional static analysis, while dynamic analysis is too resource intensive to be deployed at a large scale. One prominent line of research addresses these limitatio...

Contrastive Self-Supervised Network Intrusion Detection Using Augmented Negative Pairs

Network intrusion detection remains a critical challenge in cybersecurity. While supervised machine learning models achieve state-of-the-art performance, their reliance on large labelled datasets makes them impractical for many real-world applications. Anomaly detection methods, which train...

Decoding Latent Attack Surfaces in LLMs: Prompt Injection Via HTML in Web Summarization

Large Language Models LLMs are increasingly integrated into web-based systems for content summarization, yet their susceptibility to prompt injection attacks remains a pressing concern. In this study, we explore how non-visible HTML elements such as , aria-label, and alt attributes can be exploit...

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models

Large Language Models LLMs are increasingly vulnerable to a sophisticated form of adversarial prompting known as camouflaged jailbreaking. This method embeds malicious intent within seemingly benign language to evade existing safety mechanisms. Unlike overt attacks, these subtle prompts exploit...

VulnRepairEval: an Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities

The adoption of Large Language Models LLMs for automated software vulnerability patching has shown promising outcomes on carefully curated evaluation sets. Nevertheless, existing datasets predominantly rely on superficial validation methods rather than exploit-based verification, leading to...

CVE-2025-57140

rsbi-pom 4.7 is vulnerable to SQL Injection in the /bi/service/model/DatasetService path...

Performance Analysis of Common Browser Extensions for Cryptojacking Detection

This paper considers five extensions for Chromium-based browsers in order to determine how effective can browser-based defenses against cryptojacking available to regular users be. We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking,...

E-PhishGen: Unlocking Novel Research in Phishing Email Detection

Every day, our inboxes are flooded with unsolicited emails, ranging between annoying spam to more subtle phishing scams. Unfortunately, despite abundant prior efforts proposing solutions achieving near-perfect accuracy, the reality is that countering malicious emails still remains an unsolved...

An Intrusion Detection System in Internet of Things Using Grasshopper Optimization Algorithm and Machine Learning Algorithms

The Internet of Things IoT has emerged as a foundational paradigm supporting a range of applications, including healthcare, education, agriculture, smart homes, and, more recently, enterprise systems. However, significant advancements in IoT networks have been impeded by security vulnerabilities...

AI Agentic Vulnerability Injection and Transformation with Optimized Reasoning

The increasing complexity of software systems and the sophistication of cyber-attacks have underscored the critical need for effective automated vulnerability detection and repair systems. Traditional methods, such as static program analysis, face significant challenges related to scalability,...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...