Prevent crypto-mining malware with Darktrace. Learn how Monero-miner infections spread on networks. Stop cyber-criminals earning from illegal activities.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
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15
Apr 2018
Malware Trends
One of the top malware trends in recent months has been the stellar growth of crypto-mining malware. Of the various crypto-currencies, the most prominent malware used for illegal mining activities is Monero, a crypto-currency that can be profitably mined on commodity hardware such as laptops and workstations. Moreover, a related trend observed recently is that of laterally moving malware which, as its name suggests, moves between devices to execute its payloads in a variety of different ways. This malware, used in attacks such as WannaCry, NotPetya and BadRabbit, uses techniques such as encrypting hard drives with ransomware while also deploying Monero miners.
As Darktrace regularly detects crypto-mining attempts the moment they occur on a network, we can estimate the cash flow stream a cyber-criminal earned on a laterally moving Monero-miner infection that Darktrace identified.
Monero-miner Infection
Last month, a customer’s device – which we will call patient zero – became infected with a Monero-miner. After a short time, patient zero started looking for accessible SMB drives by scanning the internal network for devices on port 445. As the device had not conducted any network scanning activity in the past, Darktrace flagged the process as an unusual network scan and an anomalous SMB enumeration:
The network scan (device names are redacted)
Once patient zero identified accessible IPC$, ADMIN$ or C$ SMB drives, it transferred an executable to the drive. After the file transfer, the malware used PsExec to connect to the device and execute the malicious software. As patient zero had not made any SMB drive writes and had not used PsExec in this fashion before, alerts were raised immediately:
Lateral movement (device names are redacted)
Spread and containment of Monero-miner
The now-infected device started mining Monero and attempted to communicate over Tor2Web with Command & Control (C2) servers:
C2 traffic (device names are redacted)
Using Darktrace, the security team identified the infection within minutes and assessed the complete extent of the infection in less than an hour. Within three hours from initial detection, the security team had run a clean-up script on their network which stopped the spread.
Monero-miner Revenue estimates
We have estimated the hypothetical revenue for this particular attack. To make the mining less detectable, some of the current Monero-mining malware applies restrictions to both the number of threads that can be used and the maximum CPU usage capacity. As a result, we have estimated the figures below on a worst-case scenario basis.
We know that 300 machines were infected and that the Monero miners were running for around 4 hours.
Mining profitability is commonly measured in the amount of hashes calculated per second per CPU core or GPU. This number, known as hashes per second (H/S), can differ based on the hardware used. A common number on the lower end of the scale for H/S on a single CPU is 20 H/S for the CryptoNight algorithm used to mine Monero.
GPUs, being more efficient for the CryptoNight algorithm, can yield 2-3x the H/S rate of CPUs and beyond. Keeping with a worst-case scenario basis, we will assume all infected devices had only 2 CPU cores and no GPUs, meaning a single infected machine yielded 40 H/S. This leads us to the following calculation: 300 infected devices x 40 H/S = 12000 H/S.
A Monero-mining revenue calculation tool produced the following results: with a Monero price of $202.43 at the time of infection (disregarding electricity costs), the criminal would have earned roughly $15.85 in 24h. As the miners only ran for around 4 hours, the resulting revenue would have only been $2.64. So how is this profitable?
It’s a numbers game
Cryptocurrency-mining operations are designed to last for months, not hours. If this infection had gone undetected, the criminal would have earned $15.85 per day, or $475.62 per month. Furthermore, victims with larger networks are much less likely to notice the infection. As attacks spreading this kind of malware are often indiscriminate in nature, they will often hit thousands of organizations at the same time, giving them the capacity to generate much more than just half a dollar.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
def execute_rce_command(base_url, command, timeout=120): """ ACTUAL EXPLOIT METHOD - Next.js React Server Component RCE DO NOT MODIFY THIS FUNCTION Returns: (success, output) """ try: # Disable SSL warnings urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
AppleScript Abuse: Unpacking a macOS Phishing Campaign
Introduction
Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.
The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.
Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].
Technical analysis
The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.
Figure 1: The AppleScript header prompting execution of the script.
Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.
Figure 2: Curl request to receive the next stage.
This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.
The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].
Figure 3: Fake dialog prompt for system password.
The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.
Figure 4: Requirements gathered on trusted binary.
Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, ScriptEditor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]
To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.
After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:
Full disk access
Screen recording
Accessibility
Camera
Apple Events
Input monitoring
The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.
Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.
Figure 7: Snippet of decoded Base64 response.
A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:
/controller.php?req=instd
/controller.php?req=tell
/controller.php?req=skip
These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.
Figure 8: LaunchAgent patterns to be replaced with victim information.
The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.
Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.
Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met. However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.
Conclusion
This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.
Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections. These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.
Credit to Tara Gould (Malware Research Lead) Edited by Ryan Traill (Analyst Content Lead)
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