Authorities arrest 23-year-old accused of running the Kimwolf botnet


Authorities arrest 23-year-old accused of running the Kimwolf botnet

Pierluigi Paganini
May 22, 2026

Canadian authorities arrested a 23-year-old Ottawa man accused of running the Kimwolf DDoS botnet. The US is now seeking extradition.

US authorities have charged 23-year-old Jacob Butler (aka “Dort”), an Ottawa resident, for allegedly operating the recently disrupted Kimwolf botnet. Authorities arrested the suspect in Canada, he could face up to 10 years in prison if convicted in the US.

Butler was charged with aiding and abetting computer intrusion. According to the Justice Department, investigators linked him to the botnet using IP addresses, account records, financial transactions, and messaging app data.

In January, Synthient researchers reported that the Kimwolf botnet has compromised more than 2 million Android devices, spreading primarily via residential proxy networks.

“According to court documents, on April 10, 2026, U.S. authorities criminally charged Jacob Butler, aka “Dort,” 23, of Ottawa, Canada, with offenses related to the development and operation of the KimWolf botnet. KimWolf was a DDoS-for-hire service which infected over a million devices worldwide, including devices located in Alaska.” reads the press release by DoJ. “The complaint remained sealed pending Butler’s arrest.”

In March, the U.S. DoJ disrupted command-and-control infrastructure used by several IoT botnets, including AISURUKimwolf, JackSkid, and Mossad. The operation involved authorities from Canada and Germany, along with major tech companies, to target botnet operators and weaken their global cybercrime activities.

“The U.S. Justice Department participated in a court-authorized law enforcement operation today to disrupt Command and Control (C2) infrastructure used by the Aisuru, KimWolf, JackSkid and Mossad Internet of Things (IoT) botnets.” reads the press release published by DoJ.

“The operation was conducted simultaneously to law enforcement actions conducted in Canada and Germany, which targeted individuals who operated these botnets. The four botnets launched Distributed Denial of Service (DDoS) attacks targeting victims around the world. Some of these attacks measured approximately 30 Terabits per second, which were record-breaking attacks.”

U.S. authorities seized domains, servers, and infrastructure used in cybercrime, including DDoS attacks targeting Department of Defense systems. The disrupted botnets had infected over 3 million devices worldwide, mainly IoT like cameras and routers, often bypassing firewall protections. Operators used a “cybercrime-as-a-service” model, renting access to these hijacked devices to launch large-scale DDoS attacks globally.

Victims reported heavy losses from DDoS attacks, with criminals launching hundreds of thousands of attacks and sometimes demanding extortion payments. The Aisuru botnet was used to launch over 200,000 attack commands, JackSkid 90,000, KimWolf 25,000, and Mossad over 1,000. The joint international operation aims to disrupt these botnets, stop further infections, and prevent future attacks.

Kimwolf is a newly discovered Android botnet linked to the Aisuru botnet that has infected over 2 million devices and issued more than 1.7 billion DDoS attack commands.

The Kimwol Android botnet primarily targets TV boxes, compiled using the NDK and equipped with DDoS, proxy forwarding, reverse shell, and file management functions. It encrypts sensitive data with a simple Stack XOR, uses DNS over TLS to hide communication, and authenticates C2 commands with elliptic curve digital signatures. Recent versions even incorporate EtherHiding to resist takedowns via blockchain domains.

Kimwolf follows a naming pattern of “niggabox + v[number]”; versions v4 and v5 have been tracked. By taking over one C2 domain, researchers observed around 2.7 million IPs interacting over three days, indicating a likely infection scale exceeding 1.8 million devices. Its infrastructure spans multiple C2s, global time zones, and versions, making it hard to estimate the total number of infections.

The botnet borrows the code from the Aisuru family, however, operators redesigned it to evade detection. Its primary function is traffic proxying, though it can execute massive DDoS attacks.

“Law enforcement allegedly connected Butler to the administration of the KimWolf botnet through IP address, online account information, transaction records, and online messaging application records obtained through the issuance of legal process.” continues DoJ. “In addition to Butler’s arrest, the Central District of California unsealed seizure warrants which targeted online services supporting 45 DDoS-for-hire platforms. These seizures broadly disrupted the DDoS platforms, including at least one that collaborated with Butler’s KimWolf botnet. U.S. authorities also seized domain records associated with many of these services, redirecting them to an authorized “splash page,” which displays a warning to potential visitors that DDoS services are illegal.”

Sentencing will be decided by a federal judge.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, Kimwolf botnet)







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  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

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McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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