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October 25, 2017

Investigating the BadRabbit Cyber Threat

This blog post describes the currently-circulating ransomware called BadRabbit and how Darktrace’s machine learning technology detects it.
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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25
Oct 2017

This blog post describes the currently circulating ransomware called BadRabbit and how Darktrace’s machine learning technology detects it. BadRabbit is a self-propagating piece of malware that uses SMB to spread laterally. The campaign is reminiscent of the WannaCry and NotPetya attacks seen earlier this year. Some of the functionality in BadRabbit and the modus operandi of how it infects the targets is similar to the NotPetya attack.

The attack initially hit companies in Russia and Ukraine on October 24th, 2017. Since, the ransomware has spread to other countries across the world as well.

Infection process

The initial infection vector appears to be via drive-by downloads and social engineering using fake Adobe Flash player files. Various news and media websites predominantly but not exclusively in Russia and Ukraine served their visitors with pop-up alerts asking them to download Adobe Flash player software updates. It is unclear at this point if the websites were compromised, or if the advertisement networks were leveraged to display the fake Adobe Flash downloads.

This technique of presenting users with fake updates, commonly Adobe Flash, containing ransomware, adware or other forms of malware, has gained traction in the last six months. The same approach is often applied to trick users into inadvisable actions, such as downloading malware when browsing TV streaming websites, or torrent websites.

Once downloaded, a user has to execute the fake Adobe Flash player with administrative credentials manually. No exploits are used to automatically execute the malware. The malware creates a scheduled task for another file upon execution. The ransomware then encrypts files on the compromised devices using a hard-coded list of file extensions using a RSA 2048 key. The criminals demand a Bitcoin payment for decrypting the files. Users are pointed to a .onion website, which has to be accessed via Tor, to pay the ransom.

BadRabbit can brute-force its way over SMB to other devices on the network using a hard-coded list of common credentials. The malware appears to contain a stripped-down version of the Mimikatz tool which is used to gather credentials on Windows machines. This is likely used to further enhance its lateral movement capabilities using SMB.

Update (October 30, 2017): As the investigation of BadRabbit capabilities continued over the weekend, new details about how BadRabbit spreads have been uncovered. BadRabbit appears to be using the EternalRomance exploit that targets CVE-2017-0145, patched by Microsoft in March 2017, to propagate within the internal network over SMB. As Darktrace’s AI does not rely on identifying individual exploits to detect breaches, this latest discovery does not affect Darktrace’s capability to identify BadRabbit infections. All of the previously identified detection capabilities still hold true.

Darktrace instantly detects BadRabbit

Darktrace has strong detection capabilities for this campaign without the use of any signatures. In fact, we alerted a number of our customers within seconds of the initial fake Flash Player download on their respective networks, and well before the extent of the campaign was publicly known.

The initial fake Adobe Flash Player download from 1dnscontrol[.]com is immediately detected as a suspicious download:

If the early signs of BadRabbit go undetected, the infected devices start brute-forcing access to other devices on the network using SMB - causing thousands of SMB session login attempts per endeavored lateral movement over port 445. This highly anomalous behavior marks a sharp departure from customers’ normal ‘pattern of life’, making BadRabbit very easy to detect for Darktrace’s machine learning technology. Within seconds, Darktrace alerted the affected organizations about this attack flagging it as ‘SMB Session Brute Force’. The below shows an ongoing lateral movement attempt from an infected device to another client device using SMB session brute-force.

Infected devices make connection attempts to one or two seemingly randomly generated IP addresses on the internet over port 445 and also port 139. Examples of these failed connection attempts are displayed below. Darktrace instantly recognized this as unusual behavior for the infected device:

Compromised devices will attempt to move laterally on the network in a search for other devices to infect. Darktrace’s AI algorithms can swiftly recognize this anomalous behavior, alerting the affected organization in real time about these ‘Unusual Internal Connections’, as well as potential ‘Network Scans’.

The below model breaches seen in Darktrace are expected in a BadRabbit infection. Please be aware that not all models listed below are expected to breach in every infection - this depends on the actual behavior observed by Darktrace.

Anomalous File / EXE from Rare External Destination
Device / SMB Session Brute Force
Unusual Activity / Unusual Internal Connections
Device / Network Scan
Unusual Activity / Sustained Unusual Activity
Anomalous Connection / Suspicious Read / Write Ratio
Compliance / Tor Usage

The Darktrace ‘Omnisearch’ and ‘Advanced Search’ features can be used to identify any connections made to the known network Indicators of Compromise:

1dnscontrol[.]com(hosting the fake Adobe Flash player file)185.149.120[.]3(static IP observed, victims HTTP POSTing to the IP)

Conclusion

BadRabbit is a machine-speed ransomware attack that exhibits some of the functionality and infection mechanics of the WannaCry and NotPetya breaches observed earlier this year. The BadRabbit malware masks itself as an ‘Adobe Flash’ software update, tempting unsuspecting users to initiate a download. After the initial impact, the attack can spread from machine to machine without human intervention.

Darktrace’s AI algorithms are quick to detect the highly anomalous patterns of behavior that BadRabbit triggers on a network, alerting the security team in real time. We have seen BadRabbit bypass traditional security controls around the globe, demonstrating once again the futility of attempting to identify and stop threats with rules and signatures. As Darktrace’s machine learning technology doesn’t rely on any assumptions of what ‘bad’ looks like and detects unfolding attacks not by what they are but by what they do, it is very powerful at catching and stopping ransomware attacks like BadRabbit in real time.

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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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
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