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A coordinated campaign of 18 malicious browser extensions infected 2.3 million users across Chrome and Edge. These extensions, including a color picker tool, appeared legitimate with verified badges and high install counts. The RedDirection campaign implemented sophisticated browser hijacking mechanisms, capturing users' browsing data and potentially redirecting them to malicious sites. The malware was introduced through version updates of previously clean extensions, exploiting the auto-update feature of browsers. The campaign demonstrates systemic failures in marketplace security, verification processes, and trust signals, turning productivity tools into surveillance malware. Users are advised to remove affected extensions and monitor their accounts for suspicious activity.

The RedDirection campaign is a coordinated malware operation involving 18 malicious browser extensions that collectively infected approximately 2.3 million users of Google Chrome and Microsoft Edge browsers. These extensions initially appeared legitimate, featuring verified badges and high installation counts, which helped them evade suspicion. The attackers exploited the browser auto-update mechanism by initially publishing clean versions of these extensions and then pushing malicious updates, effectively bypassing marketplace security and verification processes. The malicious extensions implemented sophisticated browser hijacking techniques, including capturing users' browsing data and redirecting them to attacker-controlled or malicious websites. This campaign highlights systemic weaknesses in browser extension marketplaces, particularly in the verification and trust model, where productivity tools can be weaponized into surveillance malware. Indicators of compromise include specific hashes, URLs, and domains associated with the campaign's command and control infrastructure. The campaign leverages multiple attack techniques such as credential access (T1056.001), command execution (T1059.007), persistence (T1133), user execution (T1204.002), and data exfiltration (T1571), among others. Although no CVE identifiers or known exploits in the wild are reported, the scale of infection and the stealthy nature of the campaign make it a significant threat to users and organizations relying on browser extensions for daily operations.

Detailed information about Google and Microsoft Trusted Them. 2.3 Million Users Installed Them. They Were Malware.. Get real-time updates, technical details, an

Google and Microsoft Trusted Them. 2.3 Million Users Installed Them. They Were Malware. - Live Threat Intelligence - Threat Radar | OffSeq.com

Google and Microsoft Trusted Them. 2.3 Million Users Installed Them. They Were Malware.

A VS Code extension for Ethereum smart contract development, ETHcode, was compromised through a GitHub pull request. The attacker, using a newly created account, submitted a PR that introduced a malicious dependency and code to execute it. The compromise was subtle, involving only two lines of code changes among thousands. The malicious code downloads and runs a batch script from a public file-hosting service, potentially to steal crypto assets or compromise Ethereum contracts. The extension, with nearly 6,000 installs, was removed from the marketplace after discovery. This incident highlights the importance of carefully reviewing contributions, especially from new accounts, and scrutinizing package dependencies in software development workflows.

The security threat involves a supply chain attack targeting a Visual Studio Code (VS Code) extension named ETHcode, which is used for Ethereum smart contract development. The attacker exploited the open-source development workflow by submitting a malicious pull request (PR) via a newly created GitHub account. This PR introduced a subtle compromise, modifying only two lines of code among thousands, which added a malicious dependency and code to execute it. The malicious code downloads and executes a batch script hosted on a public file-hosting service. This script likely aims to steal cryptocurrency assets or compromise Ethereum smart contracts managed or developed using the extension. The attack leverages JavaScript obfuscation techniques to evade detection and uses multiple tactics such as supply chain compromise (T1195.001), user execution (T1204.002), code injection (T1059.001 and T1059.003), and obfuscated files or information (T1027). The extension had approximately 6,000 installs before it was removed from the marketplace upon discovery. This incident underscores the risks inherent in open-source software development, especially when contributions from new or unverified accounts are accepted without thorough review. It also highlights the importance of scrutinizing package dependencies and monitoring for unusual behavior in development tools, particularly those related to blockchain and cryptocurrency ecosystems.

Detailed information about Malicious pull request infects VS Code extension. Get real-time updates, technical details, and mitigation strategies.

Malicious pull request infects VS Code extension - Live Threat Intelligence - Threat Radar | OffSeq.com

Malicious pull request infects VS Code extension

Banana Squad, a threat actor first identified in 2023, has launched a new campaign targeting GitHub repositories. The group has created over 60 trojanized repositories masquerading as hacking tools written in Python. These malicious repositories contain hundreds of trojanized Python files that utilize encoding and encryption techniques to hide backdoor code. The campaign primarily uses the domain dieserbenni[.]ru, with a new domain 1312services[.]ru detected recently. The trojanized repositories exploit GitHub's UI feature where long lines of code don't wrap, allowing attackers to hide malicious code off-screen. This sophisticated technique makes visual detection challenging, highlighting the increasing stealth of supply chain attacks on open-source platforms.

The threat actor known as Banana Squad, first identified in 2023, has initiated a new campaign targeting GitHub repositories by creating over 60 trojanized repositories disguised as hacking tools written in Python. These repositories contain hundreds of malicious Python files that employ encoding and encryption techniques to conceal backdoor code. The campaign leverages GitHub's user interface behavior where long lines of code do not wrap, allowing attackers to hide malicious payloads off-screen and evade visual detection by reviewers. The malicious repositories primarily communicate with command and control (C2) infrastructure hosted on domains such as dieserbenni[.]ru and the recently observed 1312services[.]ru. This approach exemplifies a sophisticated supply chain attack on open-source platforms, exploiting trust in publicly available code and the collaborative nature of GitHub. The use of encoding and encryption complicates static analysis and detection by automated tools. The campaign's tactics align with multiple MITRE ATT&CK techniques including T1132.001 (Data Encoding), T1204.002 (User Execution: Malicious File), T1553.002 (Subvert Trust Controls), T1140 (Deobfuscate/Decode Files or Information), T1608.001 (Stage Capabilities), T1027 variants (Obfuscated Files or Information), T1588.001 (Obtain Capabilities), T1078 (Valid Accounts), T1102.002 (Web Service), and T1059.006 (Command and Scripting Interpreter: Python). Although no known exploits in the wild have been reported yet, the campaign's stealth and supply chain nature pose significant risks to organizations relying on open-source Python tools from GitHub.

Detailed information about Threat actor Banana Squad exploits GitHub repos in new campaign. Get real-time updates, technical details, and mitigation strategies.

Threat actor Banana Squad exploits GitHub repos in new campaign - Live Threat Intelligence - Threat Radar | OffSeq.com

Threat actor Banana Squad exploits GitHub repos in new campaign

A new backdoor named Pickai is exploiting ComfyUI vulnerabilities to spread and steal sensitive AI data. Developed in C++, Pickai offers remote command execution and reverse shell capabilities with strong persistence and evasion techniques. It uses multiple C2 servers for redundancy and has infected nearly 700 devices globally. The malware is hosted on Rubick.ai, an AI e-commerce platform serving major brands, posing significant supply chain risks. Pickai employs various obfuscation methods, including string encryption, process disguise, and multiple persistence mechanisms. Its network communication uses a three-tier timing strategy for C2 communication and device information reporting.

Pickai is a sophisticated backdoor malware developed in C++ that exploits vulnerabilities in ComfyUI, a user interface framework used in AI applications. The malware enables remote command execution and reverse shell capabilities, allowing attackers to gain persistent and stealthy control over infected systems. Pickai employs multiple advanced evasion and persistence techniques, including string encryption to obfuscate its code, process disguise to avoid detection by security tools, and multiple mechanisms to maintain persistence across system reboots. It communicates with its command and control (C2) infrastructure using a three-tier timing strategy, which helps evade network-based detection by varying communication intervals. The malware uses multiple C2 servers for redundancy, ensuring continued control even if some servers are taken down. Notably, Pickai has infected nearly 700 devices globally and is hosted on Rubick.ai, an AI e-commerce platform that serves major brands, indicating a significant supply chain risk. This supply chain vector could allow the malware to spread widely through trusted AI software components or updates. The malware's primary objective appears to be the theft of sensitive AI data, which could include proprietary models, training data, or intellectual property. Indicators of compromise include several file hashes, IP addresses, and URLs linked to the malware’s infrastructure. Despite the lack of a specific CVE or known exploits in the wild, the malware’s capabilities and infection scale suggest a well-developed threat targeting AI ecosystems through ComfyUI vulnerabilities.

Detailed information about Beware of AI Pickpockets: Pickai Backdoor Spreading Through ComfyUI Vulnerability. Get real-time updates, technical details, and miti

Beware of AI Pickpockets: Pickai Backdoor Spreading Through ComfyUI Vulnerability - Live Threat Intelligence - Threat Radar | OffSeq.com

Beware of AI Pickpockets: Pickai Backdoor Spreading Through ComfyUI Vulnerability

A new malicious campaign has been discovered targeting the Python Package Index (PyPI) by exploiting the Pickle file format in machine learning models. Three malicious packages posing as an Alibaba AI Labs SDK were detected, containing infostealer payloads hidden inside PyTorch models. The packages exfiltrate information about infected machines and .gitconfig file contents. This attack demonstrates the evolving threat landscape in AI and machine learning, particularly in the software supply chain. The campaign likely targeted developers in China and highlights the need for improved security measures and tools to detect malicious functionality in ML models.

This threat involves a novel malicious campaign targeting the Python Package Index (PyPI) ecosystem by exploiting the Pickle serialization format within machine learning (ML) models, specifically PyTorch models. Attackers have uploaded three malicious packages masquerading as an Alibaba AI Labs SDK, embedding infostealer payloads inside the PyTorch model files. The Pickle format is known for its ability to serialize and deserialize Python objects but is inherently insecure when loading untrusted data, as it can execute arbitrary code during deserialization. By embedding malicious code within these ML models, the attackers leverage the trust developers place in PyPI packages and the opaque nature of ML model files to evade detection. Once installed, these packages exfiltrate sensitive information from infected machines, including details about the system environment and contents of the .gitconfig file, which may contain credentials or configuration details useful for further compromise or lateral movement. This attack represents an evolution in supply chain attacks, combining AI/ML components with traditional malware techniques to target developers and organizations relying on open-source ML tools. Although the campaign appears to have primarily targeted developers in China, the use of PyPI and PyTorch is global, raising concerns about broader exposure. The attack chain exploits the lack of robust security controls around ML model files and the absence of effective scanning tools capable of detecting malicious payloads embedded in serialized ML artifacts. No known exploits are currently reported in the wild beyond this campaign, and no patches or mitigations have been formally released, underscoring the need for heightened vigilance and improved security practices in ML supply chains.

Detailed information about Malicious attack method on hosted ML models now targets PyPI. Get real-time updates, technical details, and mitigation strategies.

Malicious attack method on hosted ML models now targets PyPI - Live Threat Intelligence - Threat Radar | OffSeq.com

Malicious attack method on hosted ML models now targets PyPI

Earth Ammit, a Chinese-linked threat actor, conducted two campaigns targeting drone supply chains in Taiwan and South Korea from 2023 to 2024. The VENOM campaign focused on software service providers using open-source tools, while TIDRONE targeted military industries with custom malware. Their tactics included supply chain attacks, credential theft, and cyberespionage. Victims spanned military, satellite, heavy industry, media, technology, and healthcare sectors. Earth Ammit's goal was to compromise trusted networks for downstream attacks. They employed evolving techniques like fiber-based evasion and custom backdoors CXCLNT and CLNTEND. The campaigns showed progression from broad, low-cost tools to tailored capabilities for sensitive targets.

The threat actor Earth Ammit, linked to Chinese state-sponsored activities, has executed coordinated multi-wave cyber campaigns targeting drone supply chains primarily in Taiwan and South Korea between 2023 and 2024. These campaigns, named VENOM and TIDRONE, represent a strategic effort to disrupt and infiltrate critical supply chains associated with drone manufacturing and military industries. The VENOM campaign leveraged open-source tools to compromise software service providers, enabling broad access to downstream targets through trusted networks. In contrast, the TIDRONE campaign employed custom malware specifically designed to target military sectors, demonstrating an evolution from generic to highly tailored attack methodologies. The threat actor utilized a variety of sophisticated tactics including supply chain attacks, credential theft, and cyberespionage to gain persistent access. Notably, they deployed custom backdoors such as CXCLNT and CLNTEND and employed fiber-based evasion techniques to avoid detection. The victim profile is diverse, spanning military, satellite, heavy industry, media, technology, and healthcare sectors, indicating a wide-reaching impact on critical infrastructure and strategic industries. The campaigns also incorporated multiple MITRE ATT&CK techniques such as T1583 (Acquire Infrastructure), T1003 (Credential Dumping), T1543 (Create or Modify System Process), T1190 (Exploit Public-Facing Application), and T1078 (Valid Accounts), among others, highlighting a comprehensive and multi-faceted approach to compromise and persistence. The progression from broad, low-cost tools to customized malware underscores the threat actor’s adaptability and focus on high-value targets within sensitive sectors.

MD5 of 5235fecd3e1449ba9f78a25ddb89948a638484411a7bf91af3bb4d1b159f255a

MD5 of 19bbc2daa05a0e932d72ecfa4e08282aa4a27becaabad03b8fc18bb85d37743a

MD5 of 8907907a571a90c28ae72c10945f626fd22a6f587f664a6b86ad3a8f344f1aae

MD5 of 40bcd87bcd851c5c2d6e5c901c59312d480eed58b4ebb2981607c0d80c27b529

MD5 of 1b08f1af849f34bd3eaf2c8a97100d1ac4d78ff4f1c82dbea9c618d2fcd7b4c8

MD5 of c3c4443c3fee858e71fb8017288d9f3b79b2ae0f3f37f93d373765261b299d46

MD5 of 2f2d4cc6266fe1671fa03737059622e03466a80d43a0342bff21b73c7aa5419a

SHA1 of 40bcd87bcd851c5c2d6e5c901c59312d480eed58b4ebb2981607c0d80c27b529

SHA1 of 8907907a571a90c28ae72c10945f626fd22a6f587f664a6b86ad3a8f344f1aae

SHA1 of c3c4443c3fee858e71fb8017288d9f3b79b2ae0f3f37f93d373765261b299d46

SHA1 of 19bbc2daa05a0e932d72ecfa4e08282aa4a27becaabad03b8fc18bb85d37743a

SHA1 of 5235fecd3e1449ba9f78a25ddb89948a638484411a7bf91af3bb4d1b159f255a

SHA1 of 1b08f1af849f34bd3eaf2c8a97100d1ac4d78ff4f1c82dbea9c618d2fcd7b4c8

SHA1 of 2f2d4cc6266fe1671fa03737059622e03466a80d43a0342bff21b73c7aa5419a

Detailed information about Disruption of Drone Supply Chains Through Coordinated Multi-Wave Attacks in Taiwan. Get real-time updates, technical details, and mit

Disruption of Drone Supply Chains Through Coordinated Multi-Wave Attacks in Taiwan - Live Threat Intelligence - Threat Radar | OffSeq.com

Disruption of Drone Supply Chains Through Coordinated Multi-Wave Attacks in Taiwan

A sophisticated malicious package named 'dbgpkg' was detected on PyPI, masquerading as a Python debugging utility. The package implants a backdoor on systems, enabling execution of malicious code and data exfiltration. It uses function wrapping techniques to evade detection and is believed to be part of a larger campaign possibly linked to a hacktivist group known as Phoenix Hyena. The campaign also includes other packages like 'discordpydebug' and 'requestsdev'. The attackers' motivation appears to be geopolitical, potentially related to the Russia-Ukraine conflict. The use of specific backdooring techniques and tools like Global Socket Toolkit indicates a high level of sophistication and an intent to establish long-term presence on compromised systems.

A malicious package named 'dbgpkg' was discovered on the Python Package Index (PyPI), masquerading as a legitimate debugging utility. This package implants a backdoor on infected systems, enabling attackers to execute arbitrary code and exfiltrate sensitive data. The backdoor leverages advanced function wrapping techniques to evade detection by static and dynamic analysis tools, making it difficult for defenders to identify malicious behavior during package inspection or runtime. The campaign distributing 'dbgpkg' also includes other similarly disguised packages such as 'discordpydebug' and 'requestsdev', indicating a coordinated supply chain attack targeting Python developers and environments. The attackers, attributed to a hacktivist group known as Phoenix Hyena, appear motivated by geopolitical factors, specifically linked to the Russia-Ukraine conflict. The use of sophisticated tools like the Global Socket Toolkit suggests the attackers aim to maintain persistent, stealthy access to compromised systems for long-term espionage or disruption. No specific affected versions are listed, implying the threat targets any environment where these packages are installed. Although no known exploits in the wild have been reported yet, the presence of these packages on PyPI poses a significant risk to organizations relying on Python-based software development or deployment pipelines, as the supply chain compromise can lead to widespread infiltration and data breaches.

Detailed information about Backdoor implant discovered on PyPI posing as debugging utility. Get real-time updates, technical details, and mitigation strategies.

Title	Severity	Type	Source	Date

Threats Tagged 'supply chain attack'

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