CVE-2026-10803

MLflow up to 3.10.0 contains a flaw in mlflow.data.digest_utils (Digest Computation) where manipulation leads to use of a weak hash. This affects the Digest Utils function in the Dataset Digest Computation component and enables a local attack. The reported exploitability is high in complexity wit...

CVE-2026-10803

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digestutils of the file mlflow/data/digestutils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is...

CVE-2026-10803 MLflow Dataset Digest Computation digest_utils.py mlflow.data.digest_utils weak hash

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digestutils of the file mlflow/data/digestutils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is...

CVE-2026-10803 MLflow Dataset Digest Computation digest_utils.py mlflow.data.digest_utils weak hash

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digestutils of the file mlflow/data/digestutils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is...

EUVD-2026-34245

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digestutils of the file mlflow/data/digestutils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is...

CVE-2026-10803 MLflow Dataset Digest Computation digest_utils.py mlflow.data.digest_utils weak hash

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digestutils of the file mlflow/data/digestutils.py of the component Dataset Digest Computation. This manipulation causes use of weak hash. It is possible to launch the attack on the local host. The attack is...

An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification,...

Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

PT-2026-46317

Chartbrew is an open-source web application that can connect directly to databases and APIs and use the data to create charts. In versions 4.9.0 through 5.0.0, an authenticated user with project-editor permissions can store arbitrary HTML/JavaScript in the ChartDatasetConfig.legend field. The...

MLflow 安全漏洞

MLflow is an open-source platform that simplifies machine learning development. It includes features like tracking experiments, packaging code for reproducible runs, and sharing and deploying models. Versions of MLflow 3.10.0 and earlier contain security vulnerabilities. These vulnerabilities ste...

PT-2026-46189

Name of the Vulnerable Software and Affected Versions MLflow versions prior to 3.10.1 Description A flaw in the Dataset Digest Computation component allows the use of a weak hash. This issue occurs within the mlflow.data.digest utils function located in the mlflow/data/digest utils.py file. An...

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Rule-based Intrusion Detection and Prevention Systems IDPS offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets e.g., CICIDS2017, UNSW-NB15 focus on...

TeleHunt: A Framework and Tool for Efficient Cybercriminal Community Discovery on Telegram

This paper presents TeleHunt, a framework and tool for evaluating the effectiveness of different strategies to discover cybercriminal communities on Telegram. TeleHunt employs a set of reference-driven snowballing strategies, integrating message-level classification, contextual filtering, and...

Towards Intrusion Detection Systems for RPL-Based IoT Networks Using Foundation Models

AI-based intrusion detection systems IDS have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network...

The Role of Domain-Specific Features in Malware Detection: A MacOS Case Study

Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem of macOS malware detection relatively unexplored. Indeed, the specificity of the operating system and the unique...

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities proof-of-concept generation, vulnerability prioritization, and detection rule engineering operate largely in isolation. Existing automated exploit generation systems report...

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...

Operationalizing Cyber Attack Prediction: A Gap-Prioritized Framework with Dataset and Model Selection Guidelines

While AI and machine learning for cyber attack prediction have advanced, a critical gap persists between theoretical research and practical operational deployment. Building on Ankalaki et al. 2025, this paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identif...

Bastet: A Fine-Grained Expert-Labeled Dataset for DeFi Smart Contract Vulnerability Detection

Smart contract vulnerabilities in Decentralized Finance DeFi protocols resulted in over 1.49 billion USD in confirmed losses in 2024 alone, across 192 incidents 1. As LLM-based vulnerability detection emerges as a promising approach to address these threats, the quality of evaluation datasets has...

A Hybrid Approach for Malware Classification Using Secondary Features Fusion

The number of malware either variant or novel is rapidly increasing, making malware detection and mitigation a complex problem. One approach to improving malware mitigation is automatic detection and malware family classification. However, traditional malware detection methods cannot classify...