CVE-2025-5171

A vulnerability, which was classified as critical, has been found in llisoft MTA Maita Training System 4.5. This issue affects the function this.fileService.download of the file com\llisoft\controller\OpenController.java. The manipulation of the argument url leads to unrestricted upload. The atta...

CVE-2025-5171 llisoft MTA Maita Training System OpenController.java this.fileService.download unrestricted upload

A vulnerability, which was classified as critical, has been found in llisoft MTA Maita Training System 4.5. This issue affects the function this.fileService.download of the file com\llisoft\controller\OpenController.java. The manipulation of the argument url leads to unrestricted upload. The atta...

CVE-2025-5171

The CVE-2025-5171 entry concerns llisoft MTA Maita Training System 4.5. Affected: the file download path through this.fileService.download in com\llisoft\controller\OpenController.java. Root cause: argument url manipulation enables unrestricted upload. Impact: remote attack possible with high sev...

CVE-2025-5170

A vulnerability classified as critical was found in llisoft MTA Maita Training System 4.5. This vulnerability affects the function AdminShitiListRequestVo of the file com\llisoft\controller\admin\shiti\AdminShitiController.java. The manipulation of the argument stTypeIds leads to sql injection. T...

CVE-2025-5170 llisoft MTA Maita Training System AdminShitiController.java AdminShitiListRequestVo sql injection

A vulnerability classified as critical was found in llisoft MTA Maita Training System 4.5. This vulnerability affects the function AdminShitiListRequestVo of the file com\llisoft\controller\admin\shiti\AdminShitiController.java. The manipulation of the argument stTypeIds leads to sql injection. T...

CVE-2025-5170

The CVE-2025-5170 issue affects llisoft MTA Maita Training System version 4.5, specifically the AdminShitiListRequestVo function in com\llisoft\controller\admin\shiti\AdminShitiController.java. The vulnerability arises from improper handling of the stTypeIds argument, enabling SQL injection that ...

Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs

As Artificial Intelligence AI systems, particularly those based on machine learning ML, become integral to high-stakes applications, their probabilistic and opaque nature poses significant challenges to traditional verification and validation methods. These challenges are exacerbated in regulated...

llisoft MTA Maita Training System 代码问题漏洞

The llisoft MTA Maita Training System is a training system from China Dongke llisoft. A code issue exists in version 4.5 of the llisoft MTA Maita Training System, which is caused by a parameter url operation that results in unlimited uploads...

llisoft MTA Maita Training System 注入漏洞

The llisoft MTA Maita Training System is a training system from China Dongke llisoft. An injection vulnerability exists in version 4.5 of the llisoft MTA Maita Training System, which results from an SQL injection due to the operation of the parameter stTypeIds...

Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models

Fully decentralized training of machine learning models offers significant advantages in scalability, robustness, and fault tolerance. However, achieving differential privacy DP in such settings is challenging due to the absence of a central aggregator and varying trust assumptions among nodes. I...

PT-2025-22912 · Llisoft · Llisoft Mta Maita Training System

Name of the Vulnerable Software and Affected Versions: llisoft MTA Maita Training System version 4.5 Description: A critical vulnerability was found in the llisoft MTA Maita Training System, affecting the AdminShitiListRequestVo function of the file...

RADEP: a Resilient Adaptive Defense Framework against Model Extraction Attacks

Machine Learning as a Service MLaaS enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, these services are vulnerable to model extraction attacks, where adversaries repeatedly query the application programming...

MADCAT: Combating Malware Detection under Concept Drift with Test-Time Adaptation

We present MADCAT, a self-supervised approach designed to address the concept drift problem in malware detection. MADCAT employs an encoder-decoder architecture and works by test-time training of the encoder on a small, balanced subset of the test-time data using a self-supervised objective. Duri...

A Linear Approach to Data Poisoning

We investigate the theoretical foundations of data poisoning attacks in machine learning models. Our analysis reveals that the Hessian with respect to the input serves as a diagnostic tool for detecting poisoning, exhibiting spectral signatures that characterize compromised datasets. We use rando...

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems

Autonomous driving systems ADS increasingly rely on deep learning-based perception models, which remain vulnerable to adversarial attacks. In this paper, we revisit adversarial attacks and defense methods, focusing on road sign recognition and lead object detection and prediction e.g., relative...

CVE-2020-25459

An issue was discovered in function synctree in heterodecisiontreeguest.py in WeBank FATE Federated AI Technology Enabler 0.1 through 1.4.2 allows attackers to read sensitive information during the training process of machine learning joint modeling...

CVE-2019-15487

DfE School Experience before v16333-GA has XSS via a teacher training URL...

INE Security Partners with Abadnet Institute for Cybersecurity Training Programs in Saudi Arabia

Cary, North Carolina, 22nd May 2025, CyberNewsWire...

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models LLMs perform well in programming analysis on source code, while...

Covert Attacks on Machine Learning Training in Passively Secure MPC

Secure multiparty computation MPC allows data owners to train machine learning models on combined data while keeping the underlying training data private. The MPC threat model either considers an adversary who passively corrupts some parties without affecting their overall behavior, or an adversa...