In November 2024, supply chain attacks featured two key trends: attackers’ persistent use of “legitimate-first” package strategies and creative approaches like exploiting official documentation. Cryptocurrency remained the primary target through both credential theft and mining operations.
Let’s delve into some of the most striking events of November:
Dozens of Machines Infected: Year-Long NPM Supply Chain Attack Combines Crypto Mining and Data Theft
A malicious NPM package, masquerading as a legitimate XML-RPC implementation, operated for over a year — stealing data and mining cryptocurrency. Dozens of systems were affected. (Link to report).
xml-rpc attack flow
Malicious NPM Package Exploits React Native Documentation Example
An attacker published a malicious NPM package that mirrors an example from React Native’s official documentation, in an attempt to trick developers following the official guide. This highlights the need for careful package verification even when following official guides. (Link to report).
From React Native’s official documentationMalicious npm package mirroring example from React Native’s official documentation
Falling Stars
Two years after the discovery of StarJacking, an analysis of 21 package repositories reveals improved security measures against this threat — though the risk still persists in some repositories. (Link to report).
Example of PyPi ecosystem process — adding verification of the package metadata.
“aiocpa” Python Package Transforms From Legitimate Package to Crypto Thief
In November 2024, PyPI published an advisory about the aiocpa package, which was compromised when versions 0.1.13 and 0.1.14 introduced obfuscated malware designed to steal cryptocurrency credentials via Telegram. The attack was notable for its patience — the attacker maintained a legitimate package for months before adding malware, while keeping the GitHub repository clean. With thousands of downloads in its final month, aiocpa joins a growing trend where attackers establish legitimate packages before weaponizing them, in most cases to target cryptocurrency assets.
Our team will continue to hunt, squash attacks, and remove malicious packages in our effort to keep the open-source ecosystem safe.
I encourage you to stay up to date with the latest trends and tactics in software supply chain security by tuning into our future posts and learning how to defend against potential threats.
October 2024 heralded a new chapter in supply chain security challenges, characterized by innovative attack techniques and cryptocurrency-focused threats. A groundbreaking entry point exploitation technique affecting multiple package ecosystems was unveiled, while the NPM ecosystem witnessed the first-ever use of Ethereum smart contracts for malware C2 infrastructure. The month also saw multiple sophisticated attacks on cryptocurrency wallets through PyPI packages and a notable compromise of the popular lottie-player package, despite 2FA protections, highlighting the increasing complexity of supply chain security threats.
Let’s delve into some of the most striking events of October:
This New Supply Chain Attack Technique Can Trojanize All Your CLI Commands
A new supply chain attack technique exploits entry points in various programming ecosystems, allowing attackers to trojanize CLI commands. This stealthy method poses risks to developers and enterprises, bypassing traditional security checks. (Link to report).
With 2FA Enabled: NPM Package lottie-player Taken Over by Attackers
NPM package lottie-player compromised via leaked automation token, bypassing 2FA. Malicious versions injected code to trick users into connecting crypto wallets. Swift response: safe version released, compromised versions unpublished. (Link to report).
Crypto-Stealing Code Lurking in Python Package Dependencies
A sophisticated cyber attack on PyPI targeted cryptocurrency wallets through malicious packages. The attack used deceptive strategies, distributed malicious code across dependencies, and only activated when specific functions were called, making detection challenging. (Link to report).
Cryptocurrency Enthusiasts Targeted in Multi-Vector Supply Chain Attack
A malicious PyPI package “cryptoaitools” targeted cryptocurrency enthusiasts through a multi-vector supply chain attack. It used deceptive GUI, multi-stage infection, and comprehensive data exfiltration to steal crypto-related information from Windows and macOS users. (Link to report).
Supply Chain Attack Using Ethereum Smart Contracts to Distribute Multi-Platform Malware
A sophisticated NPM supply chain attack uses Ethereum smart contracts for C2 distribution. The cross-platform malware, targeting popular testing packages, affects Windows, Linux, and macOS through Typosquatting and preinstall scripts. (Link to report)
Our team will continue to hunt, squash attacks, and remove malicious packages in our effort to keep the open-source ecosystem safe.
I encourage you to stay up to date with the latest trends and tactics in software supply chain security by tuning into our future posts and learning how to defend against potential threats.
As part of our ongoing security efforts, we continuously monitor and detect malicious packages within various software ecosystems. Recently, we uncovered a unique supply chain attack through the NPM package “jest-fet-mock,” which implements a different approach using Ethereum smart contracts for command-and-control operations. The package masquerades as a popular testing utility while distributing malware across Windows, Linux, and macOS platforms. This discovery represents a notable difference in supply chain attack methodologies, combining blockchain technology with traditional attack vectors in a way not previously observed in npm. jest-fet-mock was the first package identified in a larger ongoing campaign targeting the npm ecosystem. Additional packages connected to this campaign were later reported by security firms Phylum and Socket.
Key Findings
First observed instance of malware utilizing Ethereum smart contracts for C2 server address distribution in the NPM ecosystem.
Typosquatting attack targeting developers by impersonating two legitimate, popular testing packages.
Cross-platform malware targeting Windows, Linux, and macOS development environments.
Uses NPM preinstall scripts to execute malicious code during package installation.
Performs info-stealing actions while establishing persistence mechanisms across infected systems.
The Art of Impersonation
The malicious package “jest-fet-mock”, published in mid-October, was designed to impersonate two legitimate and widely used JavaScript testing utilities.
The first, “fetch-mock-jest” (~200K weekly downloads), is a wrapper around fetch-mock that enables HTTP request mocking in Jest environments.
The second, “Jest-Fetch-Mock” (~1.3M weekly downloads), provides similar functionality through Jest’s native mocking capabilities.
Both legitimate packages are tools for testing HTTP requests in JavaScript applications. The attacker used a classic typosquatting technique by misspelling “fetch” as “fet” while maintaining the key terms “jest” and “mock”. Given that the legitimate packages are primarily used in development environments where developers typically have elevated system privileges, and are often integrated into CI/CD pipelines, we believe this attack specifically targets development infrastructure through the compromise of testing environments.
Attack Flow
Blockchain-Based Command & Control
Etherscan transaction details showing the smart contract’s getString method returning the C2 server address
The most distinctive aspect of this attack is how it leverages the Ethereum blockchain for its command-and-control infrastructure. When executed, the malware interacts with a smart contract at address “0xa1b40044EBc2794f207D45143Bd82a1B86156c6b”. Specifically, it calls the contract’s “getString” method, passing “0x52221c293a21D8CA7AFD01Ac6bFAC7175D590A84” as a parameter to retrieve its C2 server address.
By using the blockchain in this way, the attackers gain two key advantages: their infrastructure becomes virtually impossible to take down due to the blockchain’s immutable nature, and the decentralized architecture makes it extremely difficult to block these communications.
Understanding the Smart Contract Mechanism
Think of a smart contract on the Ethereum blockchain as a public bulletin board — anyone can read what’s posted, but only the owner has the ability to update it. The attackers in this case deployed such a contract, using it to store their C2 server address. Every time the malicious package is installed on a new system, it checks this bulletin board to find out where to download the actual malware. What makes this approach particularly effective is its flexibility. Instead of hardcoding server addresses in their malware, the attackers can simply update their smart contract whenever they need to point to a new server. This means that even if defenders successfully block one C2 server, the attackers can quickly switch to a new one by updating their contract, and all new infections will automatically connect to the new location.
Initial Execution
The attack chain begins during the npm package installation process through the preinstall script. This script determines the host operating system and constructs a platform-specific URL to download the appropriate payload. The malware then spawns a detached process, ensuring the malicious code continues running independently of the installation process.
Multi-Platform Malware
Our analysis revealed distinct malware variants designed for:
Windows (SHA-256: df67a118cacf68ffe5610e8acddbe38db9fb702b473c941f4ea0320943ef32ba),
Linux (SHA-256: 0801b24d2708b3f6195c8156d3661c027d678f5be064906db4fefe74e1a74b17),
and macOS (SHA-256: 3f4445eaf22cf236b5aeff5a5c24bf6dbc4c25dc926239b8732b351b09698653).
Notably, as of this writing, none of these files have been flagged as malicious by any security vendors on VirusTotal.
The malware variants demonstrated various capabilities including system reconnaissance, credential theft, and establishing persistence through platform-specific mechanisms — using AutoStart files in Linux and Launch Agent configuration (~/Library/LaunchAgents/com.user.startup.plist) in macOS.
Throughout their operation, all variants maintain consistent communication with the attacker’s C2 server, showcasing a coordinated cross-platform attack strategy aimed at compromising development environments.
Impact
By targeting development tools and testing utilities, attackers gain potential access to not only individual developer machines but also CI/CD pipelines and build systems. The use of blockchain technology for C2 infrastructure represents a different approach to supply chain attacks in the npm ecosystem, making the attack infrastructure more resilient to takedown attempts while complicating detection efforts.
The cross-platform nature of the malware, coupled with the fact that no security vendors have flagged these files as malicious on VirusTotal at the time of writing, makes this an actively dangerous threat to development environments.
Conclusion
The discovery of “jest-fet-mock” reveals how threat actors are finding different ways to compromise the software supply chain. This case serves as an important reminder for development teams to implement strict security controls around package management and carefully verify the authenticity of testing utilities, especially those requiring elevated privileges.
As part of the Checkmarx Supply Chain Security solution, our research team continuously monitors suspicious activities in the open-source software ecosystem. We track and flag “signals” that may indicate foul play, including suspicious entry points, and promptly alert our customers to help protect them from potential threats.
Cryptocurrency enthusiasts have been the target of another sophisticated and invasive malware campaign. This campaign was orchestrated through multiple attack vectors, including a malicious Python package named “cryptoaitools” on PyPI and deceptive GitHub repositories. This multi-stage malware, masquerading as a suite of cryptocurrency trading tools, aims to steal a wide range of sensitive data and drain victims’ crypto wallets.
Key Findings
A malicious package “cryptoaitools” was uploaded to PyPI, impersonating legitimate cryptocurrency trading tools, complete with a seemingly functional trading bot implementation.
The malware activated automatically upon installation, targeting both Windows and macOS operating systems.
The attacker also distributed the malware through GitHub repositories, expanding the attack surface.
A deceptive graphical user interface (GUI) was used to distract victims while the malware performed its malicious activities in the background.
The malware employed a multi-stage infection process, utilizing a fake website that appeared legitimate to host and deliver second-stage payloads.
The malware displayed extensive data theft capabilities focused on cryptocurrency-related information, including wallet data, browser data, and sensitive system files.
Attack Flow
Initial Infection Vector
The CryptoAITools malware campaign began with the upload of a malicious package named “cryptoaitools” to PyPI. This package contained code for a seemingly legitimate cryptocurrency trading bot, including functions for automated trading on DEXs, price monitoring, and liquidity management. This legitimate-looking code served to disguise the malware’s true nature.
The malware activates automatically upon installation through the package’s __init__.py file. This file imports and executes the run_base() function from base.py:
__init__.py file
The run_base() function determines the victim’s operating system and executes the appropriate malware variant:
The malware employs platform-specific helper functions to execute different versions for Windows and macOS systems. While the Windows version (basec_helper.py) is less obfuscated, the macOS variant (base_helper.py) is more heavily disguised. Despite these differences, both versions perform similar malicious activities, including data theft and cryptocurrency-related operations. These helper functions are responsible for downloading and executing additional malicious payloads, thus initiating subsequent stages of the attack.
Multi-Stage Infection Process
The CryptoAITools malware employs a sophisticated multi-stage infection process, leveraging a fake website to deliver its secondary payloads.
After the initial infection via the PyPI package, the malware’s second stage begins with the execution of base_helper.py (for macOS) or basec_helper.py (for Windows). These scripts are responsible for downloading additional malicious components from a deceptive website.
The malware uses a domain that appears legitimate: https://coinsw.app. This domain hosts a convincing appearance of a cryptocurrency trading bot service, complete with fake user reviews, subscriber counts, and detailed descriptions of AI-driven trading features. This elaborate disguise attempts to add credibility if a curious user investigates the domain.
The helper script decodes a base64-encoded URL and a list of filenames:
It then downloads these files from the fake website.
These downloaded files constitute the secondary payloads, expanding the malware’s capabilities. Notable among these is MHTBot.py, which is executed immediately after download (For MAC a different set of files are downloaded and the main.py file is then executed immediately after download)
This multi-stage approach allows the malware to:
Maintain a small initial footprint in the PyPI package
Evade detection during the initial installation
Flexibly update and expand its capabilities post-infection
Use a legitimate-looking website as a hosting platform for malicious payloads
Deceptive GUI
A unique aspect of this attack, compared to many malicious packages we have seen in the past, is that the CryptoAITools malware incorporates a graphical user interface (GUI) as a key component of its social engineering strategy. This GUI appears the moment the second-stage malware is activated and presents itself as an “AI Bot Starter” application. It is designed to distract users and collect sensitive information while the malware operates covertly. The interface’s role is straightforward: it begins by prompting users to create a password “to start using the bot securely.” Once a new password is added, a fake setup process is displayed, featuring a progress bar and loading animations. While users are engaged and focused on this seemingly legitimate interface and its fake setup process, the malware continues its malicious operations in the background, including data theft and system manipulation.
Data Heist
The CryptoAITools malware conducts an extensive data theft operation, targeting a wide range of sensitive information on the infected system. The primary goal is to gather any data that could aid the attacker in stealing cryptocurrency assets. The malware’s data collection capabilities are implemented across several modules, each focusing on specific types of data or system areas.
Types of Data Targeted
Cryptocurrency wallet data from various applications (Bitcoin, Ethereum, Exodus, Atomic, Electrum, etc.)
Browser data: saved passwords, cookies, and browsing history
Data from a wide range of browser extensions related to cryptocurrency
Sensitive system files, including SSH keys and configuration files
Files from user directories (Downloads, Documents, Desktop) containing keywords related to cryptocurrencies, passwords, and financial information
Telegram application data, including configuration files and message databases
System terminal history
Data from Apple Notes and Stickies applications on macOS systems
Data Exfiltration Method
The malware’s exfiltration process begins with the collected data stored in a hidden .temp directory in the user’s home folder. For each file, the exfiltration script changes the file extension to ‘.minecraft’. It then uploads the file to gofile.io using their API. Upon successful upload, gofile.io returns a download link, which is then sent to a Telegram bot of the attacker. After transmission, the local copy of the exfiltrated file is deleted. The process also includes error handling to prevent disruptions to the malware’s operation.
The Attacker
Our continued investigation into this campaign revealed the attacker was employing multiple infection vectors and social engineering tactics. The attack is not limited to the malicious Python package on PyPI, but extends to other platforms and methods:
PyPI Package: The initial discovery of the malicious “cryptoaitools” package on PyPI.
GitHub Repository: The attacker also distributes the malware through a GitHub repository named “Meme-Token-Hunter-Bot”. This repository contains similar malicious code, potentially infecting users who clone and run the code directly from GitHub.
Fake Website: The attacker operates a fake website at https://coinsw.app/, which mimics a legitimate cryptocurrency trading bot service.
Telegram Channel: The website’s “Buy” page leads to a Telegram chat named “Pancakeswap prediction bot”, where the attacker directly engages with potential victims.
In the Telegram chat, the attacker employs various tactics to lure potential victims. They offer “bot support” to establish credibility and trust. To entice users, they promote their GitHub repository as hosting their “most powerful bot,” appealing to those seeking advanced trading tools. The attacker then proposes an attractive offer: a free trial period followed by a monthly subscription model, making the proposition seem both risk-free and professional. To further personalize the experience and maintain ongoing engagement, they offer customized configuration options and continuous support, which creates a facade of a legitimate, customer-focused service.
This multi-platform approach allows the attacker to cast a wide net, potentially reaching victims who might be cautious about one platform but trust another.
Analysis of the GitHub repository interactions suggests that the scope of the attack may be larger than initially thought. Users who have starred or forked the malicious repository could potentially be victims, though further investigation would be needed to confirm this.
Impact
The CryptoAITools malware campaign has severe consequences for victims and the broader cryptocurrency community. Individuals face immediate financial losses through cryptocurrency theft, along with long-term risks of identity theft and privacy breaches due to extensive data exfiltration.
The true scope of the attack may be larger than initially thought, particularly given the GitHub repository interactions. Users who starred or forked the malicious “Meme-Token-Hunter-Bot” repository are potential victims, significantly expanding the attack’s reach.
On a larger scale, this attack erodes trust in cryptocurrency tools and platforms, potentially slowing adoption and innovation in the cryptocurrency space.
Conclusion
This cryptobot malware serves as a potent reminder that the stakes — and the risks — are high in the world of cryptocurrency. As digital assets continue to gain value and popularity, we can expect to see more sophisticated threats targeting this space.
As part of the Checkmarx Supply Chain Security solution, our research team continuously monitors suspicious activities in the open-source software ecosystem. We track and flag “signals” that may indicate foul play, including suspicious entry points, and promptly alert our customers to help protect them from potential threats.
On September 22nd, a new PyPI user orchestrated a wide-ranging attack by uploading multiple packages within a short timeframe. These packages, bearing names like “AtomicDecoderss,” “TrustDecoderss,” “WalletDecoderss,” and “ExodusDecodes,” masqueraded as legitimate tools for decoding and managing data from an array of popular cryptocurrency wallets.
The attack targeted users of Atomic, Trust Wallet, Metamask, Ronin, TronLink, Exodus, and other prominent wallets in the crypto ecosystem. Presenting themselves as utilities for extracting mnemonic phrases and decrypting wallet data, these packages appeared to offer valuable functionality for cryptocurrency users engaged in wallet recovery or management. However, behind the scenes, these packages would fetch malicious code from dependencies to covertly steal sensitive cryptocurrency wallet data, including private keys and mnemonic phrases, potentially granting the attackers full access to victims’ funds.
This strategic use of dependencies allowed the main packages to appear harmless while harboring malicious intent in their underlying components.
Key Findings
On September 22nd, multiple packages were published to the PyPI platform, targeting a wide range of crypto wallets including Atomic, Trust Wallet, Metamask, Ronin, TronLink, and Exodus.
The malicious packages distributed their code across dependencies, making detection more challenging. This approach separated the seemingly benign main package from its harmful components.
Unlike most malicious packages we’ve seen, these packages activate only when specific functions are called, not automatically upon installation.
The malware’s capabilities include theft of private keys, mnemonic phrases, and other sensitive wallet data.
The attack exploits the trust in open-source communities and the apparent utility of wallet management tools, potentially affecting a broad spectrum of cryptocurrency users.
A Web of Deception
The attacker behind this campaign implemented a multi-faceted strategy to disguise their intent and maximize the download count of their packages. This approach combined several deceptive techniques, each designed to exploit different aspects of user trust and package evaluation practices.
Deceptive Package Names and READMEs
Package names were carefully crafted to appeal to developers and users working with various wallet types. Names like “AtomicDecoderss,” “TrustDecoderss,” and “ExodusDecodes” mimicked legitimate tools.
Each package was accompanied by a professionally crafted README file, further enhancing its apparent legitimacy. These READMEs included detailed installation instructions, usage examples, and in one case, even “best practices” for virtual environments.
The attacker went a step further in their deception by including fake package statistics in the README files. At first glance, the packages displayed impressive stats, making them appear to be part of very popular and well-maintained projects. However, these were merely images creating an illusion of popularity and active development, further encouraging users to trust and download the package.
This level of detail and apparent popularity not only made the packages seem genuine but also significantly increased the likelihood of users implementing and running the malicious code.
Distributed Functionality Across Dependencies
Functionality was distributed across multiple dependencies within the packages themselves. Six of the malicious packages relied on a dependency called “cipherbcryptors,” which contained the core malicious code. Some packages further obfuscated their functionality by utilizing an additional dependency, “ccl_leveldbases.” This approach served dual purposes.
Firstly, it made the main packages appear more innocuous upon initial inspection, as they themselves contained little to no overtly malicious code.
Secondly, it significantly complicated the analysis process for security researchers and vigilant users. The full scope of each package’s capabilities wasn’t immediately apparent, requiring a deeper dive into the dependency chain to uncover the true nature of the code.
Obfuscation of Malicious Code
Within the “cipherbcryptors” package, where the heart of the malicious functionality resided, the code was heavily obfuscated. This obfuscation makes it challenging for automated security tools and human reviewers alike to quickly identify the package’s true intent.
Original obfuscated malicious function within the “cipherbcryptors” packageAfter deobfuscation — malicious function within the “cipherbcryptors” package
Furthermore, the attacker employed an additional layer of security by not hardcoding the address of their command and control server within any of the packages. Instead, they used external resources to retrieve this information dynamically.
This technique not only makes static analysis of the code more difficult but also provides the attackers with the flexibility to change their infrastructure without needing to update the packages themselves.
By combining these various deceptive techniques — from package naming and detailed documentation to false popularity metrics and code obfuscation — the attacker created a sophisticated web of deception. This multi-layered approach significantly increased the chances of the malicious packages being downloaded and used, all while making detection and analysis more challenging.
Attack Flow
The execution of this attack diverged from the typical pattern seen in malicious packages. Rather than triggering malicious actions immediately upon installation, these packages lay dormant until specific functions were called. This approach significantly reduced the chances of detection by security tools that scan packages at installation time.
When a user attempted to use one of the advertised functions, the malicious code would activate. The process typically began with the package attempting to access the user’s cryptocurrency wallet data. For different wallet types, this involved targeting specific file locations or data structures known to contain sensitive information.
Once the wallet data was accessed, the malware would attempt to extract critical information such as private keys, mnemonic phrases, and potentially other sensitive data like transaction histories or wallet balances. This stolen data would then be prepared for exfiltration.
The exfiltration process involved encoding the stolen data and sending it to a remote server controlled by the attacker.
Impact
The consequences for victims could be severe and far-reaching. The most immediate and obvious impact is the potential for financial losses. With access to private keys and mnemonic phrases, attackers can swiftly drain cryptocurrency wallets. The irreversible nature of blockchain transactions means that once funds are stolen, recovery is nearly impossible. Beyond immediate financial theft, compromised wallet data can lead to ongoing vulnerability, as attackers may monitor and exploit the wallet over time.
The packages’ ability to fetch external code adds another layer of risk. This feature allows attackers to dynamically update and expand their malicious capabilities without updating the package itself. As a result, the impact could extend far beyond the initial theft, potentially introducing new threats or targeting additional assets over time.
Conclusion
This sophisticated supply chain attack on the Python ecosystem serves as another stark reminder of the ever-evolving threats in the open-source world. The cryptocurrency space, in particular, continues to be a prime target, with attackers constantly devising new and innovative methods to compromise valuable digital assets. This incident underscores the critical need for ongoing vigilance of the entire open-source community to remain alert and proactive in identifying and mitigating such threats. The attack’s complexity — from its deceptive packaging to its dynamic malicious capabilities and use of malicious dependencies — highlights the importance of comprehensive security measures and continuous monitoring.
As part of the Checkmarx Supply Chain Security solution, our research team continuously monitors suspicious activities in the open-source software ecosystem. We track and flag “signals” that may indicate foul play and promptly alert our customers to help protect them.
Checkmarx One customers are protected from this attack.
For over a year, a persistent malware campaign has been targeting Roblox developers through malicious NPM packages. By mimicking the popular “noblox.js” library, attackers have published dozens of packages designed to steal sensitive data and compromise systems. This campaign exploits trust in the open-source ecosystem, particularly targeting the Roblox platform, an attractive target due to its massive user base of over 70 million daily active users. Despite multiple package takedowns, new malicious packages continue to appear on the NPM registry at the time of publication, requiring developers to remain vigilant against this ongoing threat.
Key Points
Dozens of malicious npm packages mimicking the popular “noblox.js” library have been identified in a campaign dating back to August 2023, with the most recent ones appearing in late August 2024.
The attackers of this campaign have employed techniques including brandjacking, combosquatting, and starjacking to create a convincing illusion of legitimacy for their malicious packages.
The malware’s capabilities include Discord token theft, system information harvesting, system persistence, and deployment of additional payloads such as Quasar RAT.
The malware employs a sophisticated persistence technique by manipulating the Windows registry, causing it to execute every time a user opens the Windows Settings app.
While the identified malicious packages have been removed from npm, the attacker’s GitHub repository containing malicious executables remains active, posing a potential threat for future attacks.
Malware in Disguise: The Social Engineering Aspect
The attackers have employed a multi-faceted approach to craft an illusion of authenticity around their malicious packages.
One deceptive tactic combines brandjacking and combosquatting — two methods that fall under the broader category of typosquatting. This strategy creates the illusion that their packages are either extensions of or closely related to the legitimate “noblox.js” library.
For example: noblox.js-async, noblox.js-thread, and noblox.js-api.
Since libraries commonly have multiple versions or extensions for specific use cases, mimicking this naming pattern increases the likelihood that developers will install the attackers’ packages, assuming they’re official extensions of “noblox.js.”
Another tactic used is starjacking, a popular method attackers employ to fake their package stats. In this instance, the malicious packages were linked to the GitHub repository URL of the genuine “noblox.js” package. This tactic falsely inflates the perceived popularity and trustworthiness of the malicious packages.
The attackers also used tactics to disguise the malware within the package itself. They meticulously mimicked the structure of the legitimate “noblox.js” but introduced their malicious code in the “postinstall.js” file. They heavily obfuscated this code, even including nonsensical Chinese characters to deter easy analysis.
These combined techniques create a convincing façade of legitimacy, significantly increasing the chances of the malicious packages being installed and executed on developers’ systems. As reported in previous analyses by Socket, Stacklok and Reversinglabs, these tactics have been consistently employed and refined throughout the year-long campaign.
Attack Flow
The malicious code exploits NPM’s postinstall hook, ensuring automatic execution when the package is installed. This hook, designed for legitimate setup processes, becomes a gateway for running the obfuscated malware without the user’s knowledge or consent.
At first glance, the obfuscated code appears daunting and impenetrable. However, by simply using an online automated JavaScript deobfuscation tool, we were able to gain significant insight into the malicious code’s operation. This initial deobfuscation revealed the general steps the malware takes to achieve its objectives. Yet, the resulting code still contained confusing elements and required additional cleaning to fully comprehend its functionality. This process of incremental deobfuscation and analysis allowed us to piece together the complete attack flow, with its most notable details listed below.
Discord Token Theft
The malware searches for Discord authentication tokens in multiple locations.
The stolen tokens are then validated to ensure only active ones are exfiltrated.
Antivirus Evasion
The malware aggressively undermines the system’s security measures. It first targets Malwarebytes, attempting to stop its service if running. This is followed by a more comprehensive attack on Windows Defender: the script identifies all disk drives and adds them to Windows Defender’s exclusion list. This action effectively blinds Windows Defender to any file on the system. By disabling third-party antivirus and manipulating built-in Windows security, the malware creates an environment where it can operate freely, significantly increasing its potential for damage and persistence.
Additional Payload Deployment
The malware expands its capabilities by downloading two additional executables from the attacker’s GitHub repository. These files, “cmd.exe” and “Client-built.exe”, are fetched using base64-encoded URLs and saved to the “C:\WindowsApi” directory with randomized names. The malware uses a combination of “nodeapi_” prefix and a unique hexadecimal string, likely to help the malicious files blend in with legitimate system files.
Persistence Mechanism
To ensure long-term access, the malware manipulates a Windows registry key to ensure it runs consistently on the infected system.
Specifically, it adds the path of the downloaded “Client-built.exe” to the strategic registry location:
By modifying this key, the malware hijacks legitimate Windows functionality. As a result, whenever a user attempts to open the Windows Settings app, the system inadvertently executes the malware instead.
Data Exfiltration
Throughout its execution, the malware collects various types of sensitive information from the infected system. This data is packaged and sent to the attacker’s command and control server using a Discord webhook.
QuasarRAT Deployment
The final stage involves deploying QuasarRAT, a remote access tool that gives the attacker extensive control over the infected system.
The Attacker
The second-stage malware originates from an active GitHub repository: https://github.com/aspdasdksa2/callback. As of this writing, the repository remains accessible and potentially in use for distributing malware through other packages. The repository, owned by user “aspdasdksa2”, contains multiple malicious executables. The repository’s continued existence and its content suggest ongoing malware development and distribution.
Notably, the attacker maintains a second repository named “noblox-spoof”, which appears to house the latest malicious npm package content, directly referencing the target of this campaign.
The most recent malicious packages impersonating the popular noblox.js library (four packages published by user “bicholassancheck14” — noblox.js-async, noblox.js-threads, noblox.js-thread, and noblox.js-api) have been taken down after we promptly reported them to npm’s security team. While this is a positive development, it’s important to note that the threat is not entirely neutralized.
We strongly advise the developer community to remain vigilant. The attacker’s continued infrastructure presence and persistence pose an ongoing threat. Developers should exercise caution, particularly when working with packages that resemble popular libraries like noblox.js.
Conclusion
This malware campaign, targeting Roblox developers through npm packages, has persisted for over a year. By mimicking the popular “noblox.js” library, attackers published dozens of malicious packages designed to steal sensitive data and compromise systems.
Central to the malware’s effectiveness is its approach to persistence, leveraging the Windows Settings app to ensure sustained access.
The discovery of these malicious NPM packages serves as a stark reminder of the persistent threats facing the developer community. By masquerading as legitimate, helpful libraries, attackers continue to find new ways to exploit trust within the open-source ecosystem.
This campaign underscores the critical importance of thoroughly vetting packages before incorporation into projects. Developers must remain vigilant, verifying the authenticity of packages, especially those resembling popular libraries, to protect themselves and their users from such sophisticated supply chain attacks.
As part of the Checkmarx Supply Chain Security solution, our research team continuously monitors suspicious activities in the open-source software ecosystem. We track and flag “signals” that may indicate foul play and promptly alert our customers to help protect them.
Checkmarx One customers are protected from this attack.
A malicious campaign involving several python packages, most notably the “spl-types” Python package began on June 25th with the upload of an innocuous package to PyPI. This initial version, devoid of malicious content, was intended to establish credibility and avoid immediate detection. It was a wolf in sheep’s clothing, waiting for the right moment to reveal its true nature. The attacker’s patience paid off on July 3rd when they unleashed multiple malicious versions of the package. The heart of the malware was in the init.py file, obfuscated to evade casual inspection. Upon installation, this code would execute automatically, setting in motion a chain of events designed to compromise and control the victim’s systems, while also exfiltrating their data and draining their crypto wallets.
Key Findings
Multiple malicious Python packages were uploaded to PyPI, targeting cryptocurrency users involved with Raydium and Solana.
The attacker exploited StackExchange as their primary vector to direct people to their malicious package. They posted a seemingly helpful answer on a popular thread that referenced their malicious package, leveraging trust in community-driven platforms.
The multi-stage malware exfiltrated extensive sensitive data and facilitated the draining of victims’ crypto wallets
Windows Virus and Threat Protection failed to detect the active malware on a victim’s system, providing a real-world validation of our previous research demonstrating that modern EDR systems are ineffective against threats from malicious packages.
A backdoor component in the malware granted the attacker persistent remote access to victims’ systems, enabling potential future exploits and long-term compromises.
A Multi-Stage Assault
The initial payload acted as a springboard, reaching out to external resources to download and execute additional malicious scripts. These scripts formed the core of the operation, and meticulously scanned the victim’s system for valuable data. The malware cast a wide net, targeting an array of sensitive information — browser data was readily available, as the malware harvested saved passwords, cookies, browsing history, and even stored credit card information. Cryptocurrency wallets, including popular options like Exodus, Electrum, and Monero, were also prime targets. The attack didn’t stop there; it also sought out data from messaging applications such as Telegram, Signal, and Session. In a particularly invasive move, the malware captured screenshots of the victim’s system, providing the attacker with a visual snapshot of the user’s activities. It also scoured the system for files containing specific keywords related to cryptocurrencies and other sensitive information, including GitHub recovery codes and BitLocker keys. The final part of this digital heist involved compressing the stolen data and exfiltrating it to the attacker’s command and control server via several Telegram bots.
Also included in the malicious scripts was a backdoor that granted the attacker remote control over the victim’s system, allowing for ongoing access and potential future exploits.
One of the attacker’s telegram bots receiving screenshots and data from victims machines.
Profiling the victims & motives behind the attack
As we continued to follow this attack, a clear pattern emerged around the victims. What united them was not their profession or location, but their involvement with Raydium and Solana, two prominent players in the cryptocurrency space. This commonality suggests that the attacker had a specific target in mind.
The focus on users of these platforms indicates a level of strategic thinking on the part of the attacker. By targeting this specific group, they positioned themselves to potentially intercept or manipulate high-value transactions, pointing to clear financial motives behind the attack.
Delivery strategy of the malware
The attacker was strategic when thinking about how to deliver the malicious package to unsuspecting victims. Their approach was twofold: create a seemingly legitimate package and then lending it credibility through manipulative online engagement.
Step 1: Crafting a Deceptive Package
The first step involved creating a package that would raise minimal suspicion.
In screenshots from compromised systems, we observed victims using, or installing, a package named “Raydium”.
It’s crucial to note that while Raydium is a legitimate blockchain-related platform (an Automated Market Maker (AMM) and liquidity provider built on the Solana blockchain for the Serum Decentralized Exchange (DEX)), it does not have an official Python library.
Exploiting this gap, the attacker, used a separate username to publish a Python package named “Raydium” on PyPI.
The malicious packages of this campaign were dependencies within other seemingly legitimate packages.
This package included the malicious “spl-types” as a dependency, effectively disguising the threat within a seemingly relevant and legitimate package.
Step 2: Building Credibility and Ensuring Adoption
To lend credibility to this package and ensure its widespread adoption, the attacker scoured StackExchange, a popular Q&A platform similar to Stack Overflow, for highly viewed threads related to Raydium and Solana development. Upon identifying a suitable and popular thread, the attacker contributed what seemed to be a high-quality, detailed answer. This response, while ostensibly helpful, included references to their malicious “Raydium” package. By choosing a thread with high visibility — garnering thousands of views — the attacker maximized their potential reach.
Developers seeking solutions to Raydium-related questions would likely view the answer as credible and follow the suggestions with minimal suspicion.
This tactic underscores the importance of verifying the authenticity of packages, especially those recommended in forums by unknown individuals.
Case Studies and the personal impact
The impact of this attack goes beyond theory. Behind each compromised system is a real person, and their stories reveal the true cost of such breaches. There were many cases, but in this blog, let’s look at a couple notable ones that highlight different aspects of this attack:
Case Study 1
In one of the malware-captured screenshots, we observed clear personal details of a victim. Cross-referencing this information with LinkedIn allowed us to identify the individual, who happened to also be employed at a respected IT company. This discovery prompted us to take the step of reaching out to warn them about the breach. During our subsequent communication, we learned the victim’s entire Solana crypto wallet had been drained shortly after unknowingly downloading the malicious package. This case vividly illustrates how such attacks can have immediate and severe financial consequences for individuals.
Case Study 2
Bottom left: Screenshot of the victim’s screen. Top right: Windows Defender scan declaring in Dutch that the system is clear of threats after the scan. Top left: victim’s private key.
Another victim’s experience highlighted a critical weakness in current cybersecurity practices. A screenshot from their system showed a private key clearly visible — a goldmine for any attacker since these keys bypass any password or multi factor authentication active on the account that the private key is for.
In addition to that, and what made this image particularly alarming, was the Windows Virus and Threat Protection screen displayed alongside it, declaring that a scan had just been completed and that the system was clear of threats.
The revelation that Windows Virus and Threat Protection failed to detect the threat during active data exfiltration is particularly concerning. It emphasizes a critical blind spot in traditional security measures when it comes to malicious activities initiated through package managers. This failure of detection occurred not just before or after the attack, but during the very moment the malware was active and stealing data.
This incident provides a real-world example of how Endpoint Detection and Response (EDR) systems can fall short in stopping malicious package activity or sending relevant alerts.
Moreover, even if the malicious package is later taken down from the repository and not publicly declared as malicious, EDR systems typically won’t flag the package as vulnerable if it remains installed
on a user’s system. This leaves users potentially exposed to ongoing threats from previously downloaded malicious packages.
PyPi for example, to this day, completely eliminates all traces of a package, leaving no placeholders behind. Although malicious usernames often remain, they appear without any associated malicious packages. Consequently, anyone who encounters these malicious usernames may be unaware of the user’s history of uploading malicious packages.
The “spl-types” malware incident is more than just another data breach. It serves as a stark reminder of the cost of cybersecurity failures and the ongoing challenges we face in securing the software supply chain.
This incident’s impact extends beyond individual users to enterprises. A single compromised developer can inadvertently introduce vulnerabilities into an entire company’s software ecosystem, potentially affecting the whole corporate network.
This attack serves as a wake-up call for both individuals and organizations to reassess their security strategies. Relying solely on traditional security measures is not sufficient. A more comprehensive approach is needed, one that includes rigorous vetting of third-party packages, continuous monitoring of development environments, and fostering a culture of security awareness among developers.
As part of the Checkmarx Supply Chain Security solution, our research team continuously monitors suspicious activities in the open-source software ecosystem. We track and flag “signals” that may indicate foul play and promptly alert our customers to help protect them.
The fight against such sophisticated threats is ongoing, and as we gather more insights into the attacker’s methods and infrastructure, we will continue to share our findings with the community. Together, we can work towards a safer digital future for all.
In December 2023, we reported on how North Korean threat actors, particularly Jade Sleet, have been compromising supply chains through the open-source ecosystem, with one of their key tactics being the exploitation of the public npm registry to distribute malicious packages. Despite the increased exposure and attention brought to this issue by our research and that of others in the field, it is evident that these attackers remain undeterred.
Throughout the first and even second quarter of 2024, we observed the continued publication of malicious packages on NPM, bearing striking similarities to those detailed in our previous blog post. Initially, we believed these packages to be a continuation of Jade Sleet’s campaign in late spring and early summer of 2023. However, new information came to light, making it apparent that a new threat actor was emerging on the scene.
Key Points
Moonstone Sleet, a newly identified North Korean threat actor, has entered the scene, targeting the open-source software supply chain with tactics similar to other well-known North Korean groups.
Among Moonstone Sleet’s key tactics is the distribution of malware through malicious NPM packages, which are published on the public NPM registry, exposing a wide range of developers to potential compromise.
The ongoing activities of Moonstone Sleet, Jade Sleet, and other North Korean state-sponsored actors underscore the constant threat to the open-source ecosystem.
Recent Developments:
In a recent publication, Microsoft shed light on a new rising North Korean threat actor named Moonstone Sleet, which employs various tactics, techniques, and procedures (TTPs) to target companies for financial gain and cyberespionage. With much of These TTPs utilized by Moonstone Sleet closely resemble those employed by other North Korean threat actors
A number of IOCs shared in Microsoft’s blog closely resemble those mentioned in our December blog post and recent publications by Phylum. showing that, in addition to delivering malicious npm packages through freelancing websites and platforms like LinkedIn, Moonstone Sleet has also been attempting to spread their malicious packages through the public npm registry.
This tactic allows them to potentially reach a wider audience and increases the likelihood of their malicious packages being installed by unsuspecting developers.
Differences in Code Style and Structure
The malicious npm packages discovered during the apring and early summer of 2023, affiliated with Jade Sleet, and those found in late 2023 to early 2024, containing IOCs linking them to the Moonstone Sleet group, exhibit distinct code style and structure differences. These differences offer interesting insights into the varying strategies used by different groups when targeting the open-source software supply chain.
Packages attributed to Jade Sleet
Jade Sleet’s packages, discovered throughout summer 2023, were designed to work in pairs, with each pair being published by a separate npm user account to distribute their malicious functionality. This approach was used in an attempt to make it more challenging to detect and trace the malicious activity back to a single source.
The first package in the pair was responsible for creating a directory on the victim’s machine, fetching updates from a remote server, and saving them in a file within the newly created directory. This package laid the groundwork for the second package to execute its malicious payload.
Code of the first package in the pair
The second package, upon execution, would read a token from the file created by the first package. It would then make a request to a specific URL, passing the token as a parameter. The response from this request, likely containing additional malicious code, would be written to another file on the victim’s machine. Finally, the second package would immediately execute this newly written file as a Node.js script, unleashing the full extent of the malicious functionality.
Code of second package in pair
Packages attributed to Moonstone Sleet
In contrast, the packages published throughout late 2023 and early 2024 adopted a more streamlined single-package approach which would execute its payload immediately upon installation.
The malicious payload was encoded within string constants and included OS-specific code, executing only if it detected that it was running on a Windows machine.
Packages published in the last quarter of 2023 and the first quarter of 2024 shared significant similarities, with only minor variations in file names, URLs, and decryption keys.
Despite these minor changes, the malicious payload’s overall structure and functionality remain largely the same, indicating that the attackers are relying on a proven technique while making small modifications to evade detection:
Malicious Payload Execution:
The malicious payload downloads a file from a remote server, decrypts it using a byte-wise XOR operation, renames the decrypted file, and executes it using rundll32. It then cleans up by deleting the temporary files and replacing the malicious package.json with a clean version.
Overall structure of malicious code
Changes in the Attack Flow in Second Quarter of 2024 In the second quarter of 2024, the packages increased in complexity, with the attackers adding obfuscation and having it target Linux systems as well. The following code would be executed if the OS was detected as Linux:
For a more detailed explanation of how the various packages operate, you can refer to our past publications.
The frequent publication of malicious packages on npm by North Korean threat actors underscores the persistent nature of their campaign. By continually adapting their tactics and techniques, they aim to evade detection and enhance their odds of breaching targeted systems.
With the revelation of this new North Korean group, coupled with the recent attacks by Russian and North Korean threat actors and the recent high-profile XZ attack, it has become increasingly apparent that the open-source ecosystem has become a prime target for powerful and sophisticated adversaries. And while the open-source community plays a crucial role in maintaining the security and integrity of the ecosystem, the primary responsibility for ensuring the safety of the software supply chain lies with the companies that consume these packages.
As the fight against malicious actors in the open-source ecosystem persists, collaboration and information sharing among the security community will be critical in identifying and thwarting these attacks. Through collective effort and proactive measures, we can work towards a safer and more secure open-source ecosystem for all.
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