TF-MAL-jar.dynamicrat
📛 Threat Title
Malware family: DynamicRAT
Description
ThreatFox malware family `jar.dynamicrat`. Printable name: DynamicRAT. Aliases: DYNARAT.
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
jar.dynamicrat
VT: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/jar.dynamicrat
IOC database
- Type
- domain
- Value
jar.dynamicrat- First seen
- Last seen
- Attached to this threat
- Appears in
- 1 threat
- Description
- Extracted from Threat TF-MAL-jar.dynamicrat
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/jar.dynamicrat
References (1)
- ThreatFox: IOCs for this family ThreatFox Malwares
Remediations (10)
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web:blog.netmanageit.com
Description On Tuesday, 06.06.2023, I was notified by one of my infosec colleagues, Fate, about a strange ".jar" file he had found in his network. While execution had been prevented through the AV, the file did stick out, because when looking at its strings, Fate had noticed several substrings that contained the word "attack" in it.
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web:community.emergingthreats.net
Hi guys! Today there is a joint report from @Gi7w0rm and @tosscoinwitcher I propose a rule for content from the client: alert tcp any any -> any any (msg: "ET MALWARE [ANY.RUN] DynamicRAT ";flow: established, to_serv…
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web:gi7w0rm.medium.com
DynamicRAT — A full-fledged Java Rat Hello everyone, welcome back to one of my sporadical blog posts. Due to some fortunate circumstances, I finally have the honor to name my very first malware …
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web:github.com
DynamicRAT is a functional malware family . This repository contains a reconstructed and buildable version of DynamicRAT's client. Originally discovered in ~2024, DynamicRAT is Java-based Remote Access Trojan targeting governmental agencies through tax-themed phishing campaigns.
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web:ieeexplore.ieee.org
The evolution of IoT malware has ignited interest in the creation of malware family classification models. Nonetheless, these models encounter security concerns stemming from issues related to their interpretability and vulnerabilities exposed within the training pipeline. Recent research highlighted the limitations of learning-based malware classifiers, which are susceptible to backdoor ...
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web:learn.microsoft.com
If your organization is using Defender for Endpoint (or Defender for Business), automated investigation and remediation capabilities can save your security operations team time and effort. As outlined in this blog post, these capabilities mimic the ideal steps that a security analyst takes to investigate and remediate threats.
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web:malpedia.caad.fkie.fraunhofer.de
DynamicRAT is a malware that is spread via email attachments and compromises the security of computer systems. Once running on a device, DynamicRAT establishes a persistent presence and gives attackers complete remote control. Its features include sensitive data exfiltration, hardware control, remote action, and the ability to perform DDoS attacks. In addition, DynamicRAT uses evasion and ...
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web:www.acronis.com
Acronis TRU identified new variants of Chaos RAT, a known malware family , in recent real-world Linux and Windows attacks. Chaos RAT is an open-source remote administration tool (RAT) first seen in 2022. It evolved in 2024, and new samples have been discovered by TRU in 2025. TRU researchers uncovered a critical vulnerability in Chaos RAT's web panel that allows attackers to execute remote ...
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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.
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web:www.sciencedirect.com
The evolution of IoT malware and the effectiveness of defense strategies, e.g., leveraging malware family classification, have driven the development of advanced classification learning models. These models, particularly those that utilize model-extracted features, significantly enhance classification performance while minimizing the need for extensive expert knowledge from developers. However ...
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