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 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...

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...

PT-2026-46189

A flaw has been found in MLflow up to 3.10.0. This issue affects the function mlflow.data.digest utils of the file mlflow/data/digest utils.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 ...

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...

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...

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...

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...

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...

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...

CVE-2026-44285

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree

Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from both model safety and traditional package-malware detection. ClawHub Security Signals is a sanitized dataset of 67,453 latest public OpenClaw skill...

CVE-2026-44285

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

CVE-2026-44285

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

CVE-2026-44285

FastGPT is affected by an SSRF flaw in the dataset preview API. Before 4.15.0-beta1, an authenticated attacker could bypass isInternalAddress protection and reach internal services by abusing /api/core/dataset/file/getPreviewChunks with the externalFile data import type. The issue is resolved in ...

CVE-2026-44285 FastGPT: SSRF Protection Bypass via `externalFile` in Dataset Preview API

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...

EUVD-2026-33430

FastGPT is an AI Agent building platform. Prior to 4.15.0-beta1, a Server-Side Request Forgery SSRF vulnerability allows an authenticated attacker to bypass the global isInternalAddress network protection and make arbitrary HTTP GET requests to internal network services. This is achieved by...