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TF-MAL-osx.hloader

📛 Threat Title

Malware family: HLOADER

Category: HLOADER First seen: Last updated: Source: ThreatFox Malwares

Description

ThreatFox malware family `osx.hloader`. Printable name: HLOADER.

Indicators of Compromise (1)

Each indicator is enriched from the IOC database, threat-intel feed corroboration (Threat Hunt) and VirusTotal. Click one to expand.

domain osx.hloader VT: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/osx.hloader

IOC database

Type
domain
Value
osx.hloader
First seen
Last seen
Attached to this threat
Appears in
1 threat
Description
Extracted from Threat TF-MAL-osx.hloader

Open the full IOC page →

Threat Hunt — feed corroboration

Not present in any configured threat-intel feed.

Details From VirusTotal

VirusTotal: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/osx.hloader

References (1)

Remediations (10)

  • web:arxiv.org

    Abstract Determining the family to which a malicious file belongs is an es-sential component of cyberattack investigation, attribution, and remediation . Performing this task manually is time consuming and requires expert knowledge. Automated tools using that label mal-ware using antivirus detections lack accuracy and/or scalability, making them insuficient for real-world applications. Three ...

  • web:cybernews.com

    The FBI warns of a surge in ATM jackpotting attacks, with more than 700 incidents in 2025 alone. Hackers use Ploutus malware to force machines to dispense cash.

  • web:malpedia.caad.fkie.fraunhofer.de

    Details for the HLOADER malware family including references, samples and yara signatures.

  • web:marcusbotacin.github.io

    Malware classification is a critical task in computer security since it allows the identification and mitigation of potential cybersecurity threats. Convolutional Neural Networks (CNN) have proven to be an efective tool in malware detection, especially when combined with the transfer learning technique.

  • web:www.cisa.gov

    It highlights technical approaches to uncovering malicious activity and includes mitigation steps according to best practices. The purpose of this report is to enhance incident response among partners and network administrators along with serving as a playbook for incident investigation.

  • web:www.fortinet.com

    FortiGuard Labs discovered new Symbiote and BPFDoor variants exploiting eBPF filters to enhance stealth through IPv6 support, UDP traffic, and dynamic port hopping for covert C2 communication.

  • web:www.microsoft.com

    Users should also follow the mitigation and protection guidance provided in this blog, including disabling auto-updates for Axios npm packages, since the malicious payload includes a hook that will continue to attempt to update.

  • web:www.ncsc.gov.uk

    How to defend organisations against malware or ransomware attacks.

  • web:www.researchgate.net

    Our method uses a rule-based classifier to generate human-readable descriptions of both original and evolved malware samples belonging to the same malware family .

  • web:www.wiz.io

    Learn how the Shai-Hulud npm worm compromised 100+ packages with data-stealing malware . See how it spreads, the risks, and steps to detect and mitigate.

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