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August 19, 2020

Evil Corp's WastedLocker Ransomware Attacks Observation

Darktrace detects Evil Corp intrusions with WastedLocker ransomware. Learn how AI spotted malicious activity, from initial intrusion to data exfiltration.
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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19
Aug 2020

Darktrace has recently observed several targeted intrusions associated with Evil Corp, an advanced cyber-criminal group recently in the headlines after a surge in WastedLocker ransomware cases. The group is believed to have targeted hundreds of organizations in over 40 countries, demanding ransoms of $500,000 to $1m to unlock computer files it seizes. US authorities are now offering a $5m reward for information leading to the arrest of the group’s leaders — understood to be the largest sum of money ever offered for a cyber-criminal.

Thanks to its self-learning nature, Darktrace's AI detected these intrusions without the use of any threat intelligence or static Indicators of Compromise (IoCs). This blog describes the techniques, tools and procedures used in multiple intrusions by Evil Corp – also known as TA505 or SectorJ04.

Key takeaways

  • The threat actor was reusing TTPs as well as infrastructure across multiple intrusions
  • Some infrastructure was only observed in individual intrusions
  • While most WastedLocker reports focus on the ransomware, Darktrace has observed Evil Corp conducting data exfiltration
  • The attacker used various ‘Living off the Land’ techniques for lateral movement
  • Data exfiltration and ransomware activity took place on weekends, likely to reduce response capabilities of IT teams
  • Although clearly an advanced actor, Evil Corp can be detected and stopped before encryption ensues

Evil Corp ransomware attack

Figure 1: The standard attack lifecycle observed in Evil Corp campaigns

Initial intrusion

While Evil Corp is technically sophisticated enough to choose from an array of initial intrusion methods, fake browser updates were the weapon of choice in the observed campaign. These were delivered from legitimate websites and used social engineering to convince users to download these malicious ‘updates’. Evil Corp has actually built a framework around this capability, referred to as SocGholish.

Establishing foothold / Command & Control Traffic

Darktrace detected different C2 domains being contacted after the initial infection. These domains overlap across various victims, showing that the attacker is reusing infrastructure within the same campaign. The C2 communication – comprised of thousands of connections over several days – took place over encrypted channels with valid SSL certificates. No single infected device ever beaconed to more than one C2 domain at a time.

Two example C2 domains are listed below with more details:

techgreeninc[.]com

SSL beacon details:

  • Median beacon period: 3 seconds
  • Range of periods: 1 seconds - 2.58 minutes
  • Data volume sent per connection on average: 921 Bytes

investimentosefinancas[.]com

SSL beacon details:

  • Median beacon period: 1.7 minutes
  • Range of periods: 1 seconds - 6.68 minutes
  • Data volume sent per connection on average: 935 Bytes

Certificate information:

  • Subject: CN=investimentosefinancas.com
  • Issuer: CN=Thawte RSA CA 2018,OU=www.digicert.com,O=DigiCert Inc,C=US
  • Validation status: OK

Note in particular the median beacon period, which indicates that some C2 channels were much more hands-on, whilst others possibly acted as backup channels in case the main C2 was burned or detected. It’s also interesting to see the low amount of data being transferred to the hands-on C2 domains. The actual data exfiltration took place to yet another C2 destination, intentionally separated from the hands-on intrusion C2s. All observed C2 websites were recently registered with Russian providers and are not responsive (see below).

Figure 2: The unresponsive C2 domain

Registrar: reg.ru

Created: 2020-06-29 (6 weeks ago) | Updated: 2020-07-07 (5 weeks ago)

Figure 3: Some key information relating to the C2 domain

Darktrace’s Cyber AI Platform detected this Command & Control activity via various behavioral indicators, including unusual beaconing and unusual usage of TLS (JA3).

Internal reconnaissance

In some cases, Darktrace witnessed several days of inactivity between establishing C2 and internal reconnaissance. The attackers used Advanced Port Scanner, a common IT tool, in a clear attempt to blend in with regular network activity. Several hundred IPs and dozens of popular ports were scanned at once, with tens of thousands of connections made in a short period of time.

Some key ports scanned were: 21, 22, 23, 80, 135, 139, 389, 443, 445, 1433, 3128, 3306, 3389, 4444, 4899, 5985, 5986, 8080. Darktrace detected this anomalous behavior easily as the infected devices don’t usually scan the network.

Lateral movement

Different methods of lateral movement were observed across intrusions, but also within the same intrusion, with WMI used to move between devices. Darktrace detected this by identifying when WMI usage was unusual or new for a device. An example of the lateral movement is shown below, with Darktrace detecting this as ‘New Activity’.

Figure 4: The model breach event log

PsExec was used where it already existed in the environment and Darktrace also witnessed SMB drive writes to hidden shares to copy malware, e.g.

C$ file=Programdata\[REDACTED]4rgsfdbf[REDACTED]

A malicious Powershell file was downloaded – partly shown in the screenshot below.

Figure 5: The malicious Powershell file

Accomplish mission – Data exfiltration or ransomware deployment

Evil Corp is currently best known for its WastedLocker ransomware. Whilst some of its recent intrusions have seen ransomware deployments, others have been classic cases of data exfiltration. Darktrace has not yet observed a double-threat – a case of exfiltration followed by ransomware.

The data exfiltration took place over HTTP to generic .php endpoints under the attacker’s control.

How Cyber AI Analyst reported on WastedLocker

When the first signs of anomalous activity were picked up by Darktrace’s Enterprise Immune System, Cyber AI Analyst automatically launched a full investigation and quickly provided a full overview of the overall incident. The AI Analyst continued to add more details to the ongoing incident as it evolved. There were a total of six AI Analyst incidents for the week spanning an example Evil Corp intrusion – and two of them directly covered the Evil Corp attack. In stitching together disparate security events and presenting a single narrative, Cyber AI Analyst did all the heavy lifting for human security staff, who could look at just a handful of fully-investigated incidents, instead of having to triage countless individual model breaches.

Figure 6: Cyber AI Analyst’s overview of the incident

Note how AI Analyst covers five phases of the attack lifecycle in a single incident report:

  1. Unusual Repeated Connections – Initial C2
  2. Possible HTTP Command & Control Traffic – Further C2
  3. Possible SSL Command & Control Traffic – Further C2
  4. Scanning of Multiple Devices – Internal reconnaissance with Advanced IP Scanner
  5. SMB Writes of Suspicious Files – Lateral Movement

Evil Corp rising

Every indicator suggests that this was not a case of indiscriminate ransomware, but rather highly sophisticated and targeted attacks by an advanced threat actor. With the ultimate goal of ransoming operations, the attacker moved towards the crown jewels of the organization: file servers and databases.

The organizations involved in the above analysis did not have Darktrace Antigena – Darktrace’s Autonomous Response technology – in active mode, and the threat was therefore allowed to escalate beyond its initial stages. With Antigena in full operation, the activity would have been contained at its early stages with a precise and surgical response which would have stopped the malicious behavior whilst allowing the business to operate as normal.

Despite the targeted and advanced nature of the threat, security teams are perfectly capable of detecting, investigating, and stopping the threat with Cyber AI. Darktrace was able to not only detect WastedLocker ransomware based on a series of anomalies in network traffic, but also stitch together those anomalies and investigate the incident in real time, presenting an actionable summary of the different attack stages without flooding the security team with meaningless alerts.

Learn more about Autonomous Response

Network IoCs:

IoCCommenttechgreeninc[.]comC2 domaininvestimentosefinancas[.]comC2 domain

Selected associated Darktrace model breaches:

  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Suspicious Beaconing Behaviour
  • Device / New or Unusual Remote Command Execution
  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / New User Agent
  • Unusual Activity / Unusual Internal Connections
  • Device / Suspicious Network Scan Activity
  • Device / Network Scan
  • Device / Network Scan - Low Anomaly Score
  • Device / ICMP Address Scan
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compromise / SSL Beaconing to Rare Destination
  • Anomalous Connection / SMB Enumeration
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Unusual SMB Script Write

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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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 additional 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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