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October 30, 2023

Exploring AI Threats: Package Hallucination Attacks

Learn how malicious actors exploit errors in generative AI tools to launch packet attacks. Read how Darktrace products detect and prevent these threats!
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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Senior Cyber Analyst & Team Lead
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30
Oct 2023

AI tools open doors for threat actors

On November 30, 2022, the free conversational language generation model ChatGPT was launched by OpenAI, an artificial intelligence (AI) research and development company. The launch of ChatGPT was the culmination of development ongoing since 2018 and represented the latest innovation in the ongoing generative AI boom and made the use of generative AI tools accessible to the general population for the first time.

ChatGPT is estimated to currently have at least 100 million users, and in August 2023 the site reached 1.43 billion visits [1]. Darktrace data indicated that, as of March 2023, 74% of active customer environments have employees using generative AI tools in the workplace [2].

However, with new tools come new opportunities for threat actors to exploit and use them maliciously, expanding their arsenal.

Much consideration has been given to mitigating the impacts of the increased linguistic complexity in social engineering and phishing attacks resulting from generative AI tool use, with Darktrace observing a 135% increase in ‘novel social engineering attacks’ across thousands of active Darktrace/Email™ customers from January to February 2023, corresponding with the widespread adoption of ChatGPT and its peers [3].

Less overall consideration, however, has been given to impacts stemming from errors intrinsic to generative AI tools. One of these errors is AI hallucinations.

What is an AI hallucination?

AI “hallucination” is a term which refers to the predictive elements of generative AI and LLMs’ AI model gives an unexpected or factually incorrect response which does not align with its machine learning training data [4]. This differs from regular and intended behavior for an AI model, which should provide a response based on the data it was trained upon.  

Why are AI hallucinations a problem?

Despite the term indicating it might be a rare phenomenon, hallucinations are far more likely than accurate or factual results as the AI models used in LLMs are merely predictive and focus on the most probable text or outcome, rather than factual accuracy.

Given the widespread use of generative AI tools in the workplace employees are becoming significantly more likely to encounter an AI hallucination. Furthermore, if these fabricated hallucination responses are taken at face value, they could cause significant issues for an organization.

Use of generative AI in software development

Software developers may use generative AI for recommendations on how to optimize their scripts or code, or to find packages to import into their code for various uses. Software developers may ask LLMs for recommendations on specific pieces of code or how to solve a specific problem, which will likely lead to a third-party package. It is possible that packages recommended by generative AI tools could represent AI hallucinations and the packages may not have been published, or, more accurately, the packages may not have been published prior to the date at which the training data for the model halts. If these hallucinations result in common suggestions of a non-existent package, and the developer copies the code snippet wholesale, this may leave the exchanges vulnerable to attack.

Research conducted by Vulcan revealed the prevalence of AI hallucinations when ChatGPT is asked questions related to coding. After sourcing a sample of commonly asked coding questions from Stack Overflow, a question-and-answer website for programmers, researchers queried ChatGPT (in the context of Node.js and Python) and reviewed its responses. In 20% of the responses provided by ChatGPT pertaining to Node.js at least one un-published package was included, whilst the figure sat at around 35% for Python [4].

Hallucinations can be unpredictable, but would-be attackers are able to find packages to create by asking generative AI tools generic questions and checking whether the suggested packages exist already. As such, attacks using this vector are unlikely to target specific organizations, instead posing more of a widespread threat to users of generative AI tools.

Malicious packages as attack vectors

Although AI hallucinations can be unpredictable, and responses given by generative AI tools may not always be consistent, malicious actors are able to discover AI hallucinations by adopting the approach used by Vulcan. This allows hallucinated packages to be used as attack vectors. Once a malicious actor has discovered a hallucination of an un-published package, they are able to create a package with the same name and include a malicious payload, before publishing it. This is known as a malicious package.

Malicious packages could also be recommended by generative AI tools in the form of pre-existing packages. A user may be recommended a package that had previously been confirmed to contain malicious content, or a package that is no longer maintained and, therefore, is more vulnerable to hijack by malicious actors.

In such scenarios it is not necessary to manipulate the training data (data poisoning) to achieve the desired outcome for the malicious actor, thus a complex and time-consuming attack phase can easily be bypassed.

An unsuspecting software developer may incorporate a malicious package into their code, rendering it harmful. Deployment of this code could then result in compromise and escalation into a full-blown cyber-attack.

Figure 1: Flow diagram depicting the initial stages of an AI Package Hallucination Attack.

For providers of Software-as-a-Service (SaaS) products, this attack vector may represent an even greater risk. Such organizations may have a higher proportion of employed software developers than other organizations of comparable size. A threat actor, therefore, could utilize this attack vector as part of a supply chain attack, whereby a malicious payload becomes incorporated into trusted software and is then distributed to multiple customers. This type of attack could have severe consequences including data loss, the downtime of critical systems, and reputational damage.

How could Darktrace detect an AI Package Hallucination Attack?

In June 2023, Darktrace introduced a range of DETECT™ and RESPOND™ models designed to identify the use of generative AI tools within customer environments, and to autonomously perform inhibitive actions in response to such detections. These models will trigger based on connections to endpoints associated with generative AI tools, as such, Darktrace’s detection of an AI Package Hallucination Attack would likely begin with the breaching of one of the following DETECT models:

  • Compliance / Anomalous Upload to Generative AI
  • Compliance / Beaconing to Rare Generative AI and Generative AI
  • Compliance / Generative AI

Should generative AI tool use not be permitted by an organization, the Darktrace RESPOND model ‘Antigena / Network / Compliance / Antigena Generative AI Block’ can be activated to autonomously block connections to endpoints associated with generative AI, thus preventing an AI Package Hallucination attack before it can take hold.

Once a malicious package has been recommended, it may be downloaded from GitHub, a platform and cloud-based service used to store and manage code. Darktrace DETECT is able to identify when a device has performed a download from an open-source repository such as GitHub using the following models:

  • Device / Anomalous GitHub Download
  • Device / Anomalous Script Download Followed By Additional Packages

Whatever goal the malicious package has been designed to fulfil will determine the next stages of the attack. Due to their highly flexible nature, AI package hallucinations could be used as an attack vector to deliver a large variety of different malware types.

As GitHub is a commonly used service by software developers and IT professionals alike, traditional security tools may not alert customer security teams to such GitHub downloads, meaning malicious downloads may go undetected. Darktrace’s anomaly-based approach to threat detection, however, enables it to recognize subtle deviations in a device’s pre-established pattern of life which may be indicative of an emerging attack.

Subsequent anomalous activity representing the possible progression of the kill chain as part of an AI Package Hallucination Attack could then trigger an Enhanced Monitoring model. Enhanced Monitoring models are high-fidelity indicators of potential malicious activity that are investigated by the Darktrace analyst team as part of the Proactive Threat Notification (PTN) service offered by the Darktrace Security Operation Center (SOC).

Conclusion

Employees are often considered the first line of defense in cyber security; this is particularly true in the face of an AI Package Hallucination Attack.

As the use of generative AI becomes more accessible and an increasingly prevalent tool in an attacker’s toolbox, organizations will benefit from implementing company-wide policies to define expectations surrounding the use of such tools. It is simple, yet critical, for example, for employees to fact check responses provided to them by generative AI tools. All packages recommended by generative AI should also be checked by reviewing non-generated data from either external third-party or internal sources. It is also good practice to adopt caution when downloading packages with very few downloads as it could indicate the package is untrustworthy or malicious.

As of September 2023, ChatGPT Plus and Enterprise users were able to use the tool to browse the internet, expanding the data ChatGPT can access beyond the previous training data cut-off of September 2021 [5]. This feature will be expanded to all users soon [6]. ChatGPT providing up-to-date responses could prompt the evolution of this attack vector, allowing attackers to publish malicious packages which could subsequently be recommended by ChatGPT.

It is inevitable that a greater embrace of AI tools in the workplace will be seen in the coming years as the AI technology advances and existing tools become less novel and more familiar. By fighting fire with fire, using AI technology to identify AI usage, Darktrace is uniquely placed to detect and take preventative action against malicious actors capitalizing on the AI boom.

Credit to Charlotte Thompson, Cyber Analyst, Tiana Kelly, Analyst Team Lead, London, Cyber Analyst

References

[1] https://seo.ai/blog/chatgpt-user-statistics-facts

[2] https://darktrace.com/news/darktrace-addresses-generative-ai-concerns

[3] https://darktrace.com/news/darktrace-email-defends-organizations-against-evolving-cyber-threat-landscape

[4] https://vulcan.io/blog/ai-hallucinations-package-risk?nab=1&utm_referrer=https%3A%2F%2Fwww.google.com%2F

[5] https://twitter.com/OpenAI/status/1707077710047216095

[6] https://www.reuters.com/technology/openai-says-chatgpt-can-now-browse-internet-2023-09-27/

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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Senior Cyber Analyst & Team Lead

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July 31, 2026

Building Operational Resilience Across Mission-Critical Marine Services

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Mission-Critical Marine Services

This marine organization supports offshore energy production, export infrastructure, and regional logistics, delivering critical services through its diverse fleet and a regional shorebase footprint. To power rapid mobilization and 24/7 operational readiness, the organization has embraced cloud adoption and digital transformation, reshaping how crews, contractors, and shore-based teams access services.

While technology modernization has enhanced operations, it has also introduced new security complexities.

  • From perimeter to identity: As access becomes more distributed, identity has become the primary security control, elevating the risk of credential compromise and privilege misuse.
  • From confidentiality to availability and resilience: As cloud platforms increasingly underpin fleet and operational systems, cyber incidents can disrupt services and safety.
  • From isolated tools to unified visibility:  Because Information Technology (IT) and Operational Technology (OT) often intersect, and legacy systems coexist with modern cloud platforms, fragmented monitoring makes it harder to understand risk and respond decisively across domains.
“Our cybersecurity priorities expanded along with our business goals, placing availability and resilience at the forefront. The impact of a potential threat became an operational risk, which elevated cybersecurity from an IT issue to an operational safety and resilience enabler.” - Information and Communications Technology (ICT) Manager.

A Unified, AI-Driven Platform for IT and OT

To strengthen visibility, detection, and response across its highly distributed environment, the customer adopted the Darktrace ActiveAI Security Platform™ in 2022.

Darktrace’s contextual detection capability was a key driver. Unlike traditional tools that rely on known threat signatures, Darktrace’s Self-Learning AI learns “normal” behavior to identify emerging threats and correlate visibility across the customer's siloed on-premises and cloud domains.

Today, the customer relies on:

  • Darktrace / EMAIL™ to reduce phishing risk and minimize disruption from legacy mail controls and false positives
  • Darktrace / IDENTITY™ to support identity-centric security as cloud access expands across vessels and shore-based operations
  • Darktrace / NETWORK™ to strengthen oversight across the broader environment, including operational contexts where IT and OT intersect
  • Darktrace / CLOUD™ (Azure), added in 2024, to extend detection and response into Azure and support cloud transformation without treating cloud as a separate security silo
  • Darktrace / Incident Readiness & Recovery to strengthen incident readiness and recovery planning
  • Darktrace Managed Detection and Response Services to provide 24/7/365 monitoring and support

This combination supports a single operating model for the customer: security that can adapt as the environment changes while remaining practical for a lean team responsible for safeguarding both business operations and safety-critical services.

Extending cloud protection without complexity

As the customer accelerated cloud adoption, it expanded coverage in 2024 with Darktrace / CLOUD for Azure to bring cloud workloads under the same AI-driven visibility and response model – without adding operational burden. “This matters in hybrid environments because attacks rarely stay in one place,” explains the ICT Manager. “A compromised identity can trigger activity in the cloud, which can open pathways back into on-premises systems.”

In parallel, Darktrace / CLOUD’s posture management capabilities support governance and audit readiness by surfacing misconfigurations and exposure risks earlier, before they become incidents.

A Stronger, Faster, More Resilient Business

Since adopting Darktrace, the customer has strengthened cyber resilience while reducing operational burden on its small ICT team.

Darktrace continuously analyzes millions of individual events that can contribute to a wider incident. Within a single month, the solution autonomously investigated 88% of all potential threats, taking appropriate action within just 39.4 seconds on average.

Autonomous capabilities ensure threats are stopped and contained until the ICT team can investigate. In one standout instance, Darktrace autonomously blocked malicious links during a mass phishing/spam event before other controls flagged the threat. the ICT Manager later confirmed Microsoft reported the link as malicious, but Darktrace had already acted to prevent delivery and reduce exposure.

“Whether something happens during off hours, while we’re on vacation, or when our attention is focused elsewhere, we’re confident Darktrace will take control and stop a threat before it spreads,” says the ICT Manager.

Darktrace’s Self-Learning AI combines multiple AI methods and advanced techniques to improve threat detection, investigation, and response dramatically reducing alert overload and manual triage. Within a single month, the solution saved the customer's IT group 411 equivalent human investigation hours.

“For a lean team supporting a 24/7 operational footprint, this autonomous action eliminates the constant firefighting and stress, giving us the space to focus on higher-value priorities.”- Information and Communications Technology (ICT) Manager.

Protecting communications without disruption

the customer experienced friction from legacy email and network controls prior to Darktrace, which generated high false positive rates, disrupted legitimate communications, created operational drag, and added workload for ICT. With Darktrace / EMAIL learning normal email behavior and applying context-aware actions, the team reduced unnecessary interruptions while maintaining protection.

“That shift matters in marine services, where business communications directly support coordination across vessels, shore bases, clients, ports, and regulators,” says the ICT Manager. “Darktrace doesn’t just block more threats, it autonomously makes decisions that preserve operational continuity and enable my team to focus on credible threats instead of chasing volume.”

Delivering clarity and confidence

Darktrace has reduced manual triage by correlating activity across email, identity, network, and cloud, providing the context needed to prioritize what matters without requiring the ICT team to stitch together evidence across multiple tools.

“With unified visibility we can identify patterns across domains, make informed decisions about where risk actually exists, and align security actions with operational impact rather than theoretical threats,” explains the ICT Manager. “I can now prioritize effort and investment across our ICT landscape with far greater confidence.”

Regular Executive Threat Reports reinforce operational confidence by giving leadership clear visibility into threats Darktrace has handled autonomously, supporting decisive action when needed and confidence to avoid unnecessary disruption when it isn’t.

Scaling Securely in a Hybrid World

As the customer advances its cloud transformation, the ICT Manager sees the Darktrace partnership evolving into a foundational layer of resilience and assurance, supporting scale, governance, and operational confidence in an increasingly cloud-centric environment.

Key priorities include:

  • Shifting from hybrid visibility to cloud-first resilience, using continuous monitoring and posture insights to reduce exposure earlier
  • Strengthening governance and audit readiness, especially as critical workloads and sensitive data expand in Azure and expectations rise under regulatory and client assurance requirements
  • Increasing reliance on autonomous response and AI investigation as the number of identities, workloads, and access paths grows faster than headcount
  • Deepening cross-domain correlation so cloud signals further enrich decision-making, supporting faster containment and more confident prioritization

“As we accelerate our cloud strategy, Darktrace will play an even more strategic role,” says the ICT Manager, “providing the guidance, technology, and expertise that allow us to grow with confidence and innovate securely.”

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July 28, 2026

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

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Why ransomware has changed

Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.

As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.

As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.

Inside a modern ransomware intrusion

In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.

The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.

The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.

Darktrace’s detection of a ransomware event in a customer’s environment

The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.

The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.

While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.

Figure 1: Timeline of the attack progression.

Day 1: Reconnaissance and privilege escalation

The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.

Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.

Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.

Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.

Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.

Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.

This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.

Day 3: Lateral Movement, Command & Control, and Exfiltration

Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.

The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.  

Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.

On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].

Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.

Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment.  More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.

Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration

Day 4: Encryption

The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.

Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.

Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.

Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.

Defender action recommendations

What Could Have Stopped the Attack Earlier?

Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:

Detect and investigate indicators of reconnaissance and lateral movement

  • Unusual scanning
  • Active Directory replication anomalies consistent with DCSync activity
  • Anomalous use of native tools and processes indicative of LOTL attacks
  • Unusual use of common reconnaissance tools such as Nmap and NetScan

Contain compromised credentials and affected devices

  • Disable and reset compromised VPN credentials
  • Isolate devices performing anomalous scanning and lateral movement activity

Block suspicious external communications and data exfiltration

  • Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
  • Prevent data exfiltration to unauthorized cloud storage services such as Wasabi

Conclusion

The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.

In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.

As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.

Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://industrialcyber.co/ransomware/check-point-reports-ransomware-attacks-jump-48-year-over-year-despite-decline-in-overall-cyberattack-activity/

[2] https://www.europol.europa.eu/media-press/newsroom/news/law-enforcement-disrupt-worlds-biggest-ransomware-operation

[3] https://fieldeffect.com/blog/field-effect-mitigates-not-so-simplehelp-exploits-enabling-deployment-of-backdoors

[4] https://www.darktrace.com/blog/from-vps-to-phishing-how-darktrace-uncovered-saas-hijacks-through-virtual-infrastructure-abuse

Indicators of Compromise (IoCs)

IP Addresses

  • 137[.]220[.]59[.]55 – Potential C2 infrastructure (Vultr AS20473)
  • 38[.]27[.]106[.]123 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]128 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]117 – Wasabi cloud storage endpoint associated with potential data exfiltration

Domains

  • safedata[.]s3[.]wasabisys[.]com – Potential data exfiltration endpoint
  • *.wasabisys[.]com – Associated Wasabi cloud storage infrastructure

JA3 Fingerprint

  • d6828e30ab66774a91a96ae93be4ae4c – Associated with the Sliver adversary emulation framework

Files

  • Delete[.]me – File observed during reconnaissance activity, commonly associated with NetScan

Darktrace Model Coverage

·       Anomalous Connection / Active Remote Desktop Tunnel

·       Anomalous Connection / Anomalous Remote Registry Service Control

·       Anomalous Connection / Multiple Failed Windows UDP

·       Anomalous Connection / New or Uncommon Service Control

·       Anomalous Connection / New or Uncommon Service Enumeration

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Suspicious Activity On High Risk Device

·       Anomalous Connection / Suspicious Read Write Ratio

·       Anomalous Connection / Sustained MIME Type Conversion

·       Anomalous Connection / Uncommon 1 GiB Outbound

·       Anomalous Connection / Unusual Admin RDP Session

·       Anomalous Connection / Unusual Admin SMB Session

·       Anomalous Connection / Unusual SMB Version 1 Connectivity

·       Anomalous File / EXE from Rare External Location

·       Anomalous File / Internal / Additional Extension Appended to SMB File

·       Anomalous Server Activity / Outgoing from Server

·       Compromise / Beaconing Activity To External Rare

·       Compromise / Ransomware / Possible Ransom Note Read

·       Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·       Compromise / Ransomware / Suspicious SMB Activity

·       Device / Anonymous NTLM Logins

·       Device / Attack and Recon Tools

·       Device / Initial Attack Chain Activity

·       Device / Large Number of Model Alerts

·       Device / Long Agent Connection to New Endpoint

·       Device / Multiple Lateral Movement Model Alerts

·       Device / Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / New or Unusual Remote Command Execution

·       Device / New User Agent

·       Device / Possible RPC Lateral Movement

·       Device / Possible SMB/NTLM Brute Force

·       Device / RDP Scan

·       Device / SMB Lateral Movement

·       Device / SMB Session Brute Force (Non-Admin)

·       Device / SMB Version 1 Access Failures

·       Device / Suspicious File Delete Activity

·       Device / Suspicious Network Scan Activity

·       Device / Unusual SMB Error Detected

·       Device / Unusual SMB To Critical Resource

·       Device / Unusual Winreg Operation

·       Unusual Activity / Multiple Failed Internal Connections

·       Unusual Activity / Possible RPC Recon Activity

·       Unusual Activity / Sustained Anomalous SMB Activity

·       Unusual Activity / Unusual External Data to New Endpoint

·       Unusual Activity / Unusual External Data Transfer

·       Unusual Activity / Unusual File Storage Data Transfer

·       Unusual Activity / Unusual Large Internal Transfer

·       User / New Admin Credentials on Client

·       User / NTLM Login from Unauthenticated Device

Autonomous Response Model Alerts

·       Antigena / Network / External Threat / Antigena Ransomware Block

·       Antigena / Network / External Threat / Antigena Suspicious File Block

·       Antigena / Network / Insider Threat / Antigena Active Threat SMB Write Block

·       Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

·       Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

·       Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

·       Antigena / Network / Manual / Quarantine Device

·       Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·       Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block 100

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK MAPPING

Command and control - Protocol Tunneling - T1572

Command and control – Web Protocols – T1071.001

Credential Access- Exploitation for Credential Access- T1212

Credential Access- Password Guessing- T1110

Discovery- File and Directory Discovery- T1083

Discovery- Network Service Discovery- T1046

Discovery- Network Share Discovery- T1135

Discovery- Remote System Discovery- T1018

Exfiltration- Exfiltration Over C2 Channel- T1041

Exfiltration- Exfiltration to Cloud Storage- T1567

Impact- Data Encrypted for Impact- T1486

Impact- Data Encrypted for Impact- T1486

Impact- Service Stop - T1489

Initial Access- Public-Facing Application- T1190

Lateral Movement- Exploitation of Remote Services- T1210

Lateral Movement- Remote Desktop Protocol- T1021

Lateral Movement- SMB/Windows Admin Shares- T1021

Lateral Movement- Taint Shared Content- T1080

Persistence- Modify Registry- T1112

Privilege Escalation- Exploitation for Privilege Escalation- T0890

Privilege Escalation- Valid Accounts- T1078

Reconnaissance- Scanning IP Blocks- T1595

Reconnaissance- Vulnerability Scanning- T1595

Resource Development- Malware- T1588

Stealth- File Deletion- T1070

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About the author
Alexandra Evzona
Cyber Analyst
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