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November 23, 2022

How Darktrace Could Have Stopped a Surprise DDoS Incident

Learn how Darktrace could revolutionize DDoS defense, enabling companies to stop threats without 24/7 monitoring. Read more about how we thwart attacks!
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
Steven Sosa
Analyst Team Lead
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23
Nov 2022

When is the best time to be hit with a cyber-attack?

The answer that springs to most is ‘Never’,  however in today’s threat landscape, this is often wishful thinking. The next best answer is ‘When we’re ready for it’. Yet, this does not take into account the intention of those committing attacks. The reality is that the best time for a cyber-attack is when no one else is around to stop it.

When do cyber attacks happen?

Previous analysis from Mandiant reveals that over half of ransomware compromises occur at out of work hours, a trend Darktrace has also witnessed in the past two years [1]. This is deliberate, as the fewer people that are online, the harder it is to get ahold of security teams and the higher the likelihood there is of an attacker achieving their goals. Given this landscape, it is clear that autonomous response is more important than ever. In the absence of human resources, autonomous security can fill in the gap long enough for IT teams to begin remediation. 

This blog will detail an incident where autonomous response provided by Darktrace RESPOND would have entirely prevented an infection attempt, despite it occurring in the early hours of the morning. Because the customer had RESPOND in human confirmation mode (AI response must first be approved by a human), the attempt by XorDDoS was ultimately successful. Given that the attack occurred in the early hours of the morning, there was likely no one around to confirm Darktrace RESPOND actions and prevent the attack.

XorDDoS Primer

XorDDoS is a botnet, a type of malware that infects devices for the purpose of controlling them as a collective to carry out specific actions. In the case of XorDDoS, it infects devices in order to carry out denial of service attacks using said devices. This year, Microsoft has reported a substantial increase in activity from this malware strain, with an increased focus on Linux based operating systems [2]. XorDDoS most commonly finds its way onto systems via SSH brute-forcing, and once deployed, encrypts its traffic with an XOR cipher. XorDDoS has also been known to download additional payloads such as backdoors and cryptominers. Needless to say, this is not something you have on a corporate network. 

Initial Intrusion of XorDDoS

The incident begins with a device first coming online on 10th August. The device appeared to be internet facing and Darktrace saw hundreds of incoming SSH connections to the device from a variety of endpoints. Over the course of the next five days, the device received thousands of failed SSH connections from several IP addresses that, according to OSINT, may be associated with web scanners [3]. Successful SSH connections were seen from internal IP addresses as well as IP addresses associated with IT solutions relevant to Asia-Pacific (the customer’s geographic location). On midnight of 15th August, the first successful SSH connection occurred from an IP address that has been associated with web scanning. This connection lasted around an hour and a half, and the external IP uploaded around 3.3 MB of data to the client device. Given all of this, and what the industry knows about XorDDoS, it is likely that the client device had SSH exposed to the Internet which was then brute-forced for initial access. 

There were a few hours of dwell until the device downloaded a ZIP file from an Iraqi mirror site, mirror[.]earthlink[.]iq at around 6AM in the customer time zone. The endpoint had only been seen once before and was 100% rare for the network. Since there has been no information on OSINT around this particular endpoint or the ZIP files downloaded from the mirror site, the detection was based on the unusualness of the download.

Following this, Darktrace saw the device make a curl request to the external IP address 107.148.210[.]218. This was highlighted as the user agent associated with curl had not been seen on the device before, and the connection was made directly to an IP address without a hostname (suggesting that the connection was scripted). The URIs of these requests were ‘1.txt’ and ‘2.txt’. 

The ‘.txt’ extensions on the URIs were deceiving and it turned out that both were executable files masquerading as text files. OSINT on both of the hashes revealed that the files were likely associated with XorDDoS. Additionally, judging from packet captures of the connection, the true file extension appeared to be ‘.ELF’. As XorDDoS primarily affects Linux devices, this would make sense as the true extension of the payload. 

Figure 1: Packet capture of the curl request made by the breach device.

C2 Connections

Immediately after the ‘.ELF’ download, Darktrace saw the device attempting C2 connections. This included connections to DGA-like domains on unusual ports such as 1525 and 8993. Luckily, the client’s firewall seems to have blocked these connections, but that didn’t stop XorDDoS. XorDDoS continued to attempt connections to C2 domains, which triggered several Proactive Threat Notifications (PTNs) that were alerted by SOC. Following the PTNs, the client manually quarantined the device a few hours after the initial breach. This lapse in actioning was likely due to an early morning timing with the customer’s employees not being online yet. After the device was quarantined, Darktrace still saw XorDDoS attempting C2 connections. In all, hundreds of thousands of C2 connections were detected before the device was removed from the network sometime on 7th September.

Figure 2: AI Analyst was able to identify the anomalous activity and group it together in an easy to parse format.

An Alternate Timeline 

Although the device was ultimately removed, this attack would have been entirely prevented had RESPOND/Network not been in human confirmation mode. Autonomous response would have kicked in once the device downloaded the ‘.ZIP file’ from the Iraqi mirror site and blocked all outgoing connections from the breach device for an hour:

Figure 3: Screenshot of the first Antigena (RESPOND) breach that would have prevented all subsequent activity.

The model breach in Figure 3 would have prevented the download of the XorDDoS executables, and then prevented the subsequent C2 connections. This hour would have been crucial, as it would have given enough time for members of the customer’s security team to get back online should the compromised device have attempted anything else. With everyone attentive, it is unlikely that this activity would have lasted as long as it did. Had the attack been allowed to progress further, the infected device would have at the very least been an unwilling participant in a future DDoS attack. Additionally, the device could have a backdoor placed within it, and additional malware such as cryptojackers might have been deployed. 

Conclusions 

Unfortunately, we do not exist in the alternate timeline that autonomous response would have prevented this whole series of events.Luckily, although it was not in place, the PTN alerts provided by Darktrace’s SOC team still sped up the process of remediation in an event that was never intended to be discovered given the time it occurred. Unusual times of attack are not just limited to ransomware, so organizations need to have measures in place for the times that are most inconvenient to them, but most convenient to attackers. With Darktrace/RESPOND however, this is just one click away.

Thanks to Brianna Leddy for their contribution.

Appendices

Darktrace Model Detections

Below is a list of model breaches in order of trigger. The Proactive Threat Notification models are in bold and only the first Antigena [RESPOND] breach that would have prevented the initial compromise has been included. A manual quarantine breach has also been added to show when the customer began remediation.

  • Compliance / Incoming SSH, August 12th 23:39 GMT +8
  • Anomalous File / Zip or Gzip from Rare External Location, August 15th, 6:07 GMT +8 
  • Antigena / Network / External Threat / Antigena File then New Outbound Block, August 15th 6:36 GMT +8 [part of the RESPOND functionality]
  • Anomalous Connection / New User Agent to IP Without Hostname, August 15th 6:59 GMT +8
  • Anomalous File / Numeric Exe Download, August 15th 6:59 GMT +8
  • Anomalous File / Masqueraded File Transfer, August 15th 6:59 GMT +8
  • Anomalous File / EXE from Rare External Location, August 15th 6:59 GMT +8
  • Device / Internet Facing Device with High Priority Alert, August 15th 6:59 GMT +8
  • Compromise / Rare Domain Pointing to Internal IP, August 15th 6:59 GMT +8
  • Device / Initial Breach Chain Compromise, August 15th 6:59 GMT +8
  • Compromise / Large Number of Suspicious Failed Connections, August 15th 7:01 GMT +8
  • Compromise / High Volume of Connections with Beacon Score, August 15th 7:04 GMT +8
  • Compromise / Fast Beaconing to DGA, August 15th 7:04 GMT +8
  • Compromise / Suspicious File and C2, August 15th 7:04 GMT +8
  • Antigena / Network / Manual / Quarantine Device, August 15th 8:54 GMT +8 [part of the RESPOND functionality]

List of IOCs

MITRE ATT&CK Mapping

Reference List

[1] They Come in the Night: Ransomware Deployment Trends

[2] Rise in XorDdos: A deeper look at the stealthy DDoS malware targeting Linux devices

[3] Alien Vault: Domain Navicatadvvr & https://www.virustotal.com/gui/domain/navicatadvvr.com & https://maltiverse.com/hostname/navicatadvvr.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
Steven Sosa
Analyst Team Lead

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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