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September 4, 2022

Steps of a BumbleBee Intrusion to a Cobalt Strike

Discover the steps of a Bumblebee intrusion, from initial detection to Cobalt Strike deployment. Learn how Darktrace defends against evolving threats with AI.
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
Sam Lister
Specialist Security Researcher
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04
Sep 2022

Introduction

Throughout April 2022, Darktrace observed several cases in which threat actors used the loader known as ‘BumbleBee’ to install Cobalt Strike Beacon onto victim systems. The threat actors then leveraged Cobalt Strike Beacon to conduct network reconnaissance, obtain account password data, and write malicious payloads across the network. In this article, we will provide details of the actions threat actors took during their intrusions, as well as details of the network-based behaviours which served as evidence of the actors’ activities.  

BumbleBee 

In March 2022, Google’s Threat Analysis Group (TAG) provided details of the activities of an Initial Access Broker (IAB) group dubbed ‘Exotic Lily’ [1]. Before March 2022, Google’s TAG observed Exotic Lily leveraging sophisticated impersonation techniques to trick employees of targeted organisations into downloading ISO disc image files from legitimate file storage services such as WeTransfer. These ISO files contained a Windows shortcut LNK file and a BazarLoader Dynamic Link Library (i.e, DLL). BazarLoader is a member of the Bazar family — a family of malware (including both BazarLoader and BazarBackdoor) with strong ties to the Trickbot malware, the Anchor malware family, and Conti ransomware. BazarLoader, which is typically distributed via email campaigns or via fraudulent call campaigns, has been known to drop Cobalt Strike as a precursor to Conti ransomware deployment [2]. 

In March 2022, Google’s TAG observed Exotic Lily leveraging file storage services to distribute an ISO file containing a DLL which, when executed, caused the victim machine to make HTTP requests with the user-agent string ‘bumblebee’. Google’s TAG consequently called this DLL payload ‘BumbleBee’. Since Google’s discovery of BumbleBee back in March, several threat research teams have reported BumbleBee samples dropping Cobalt Strike [1]/[3]/[4]/[5]. It has also been reported by Proofpoint [3] that other threat actors such as TA578 and TA579 transitioned to BumbleBee in March 2022.  

Interestingly, BazarLoader’s replacement with BumbleBee seems to coincide with the leaking of the Conti ransomware gang’s Jabber chat logs at the end of February 2022. On February 25th, 2022, the Conti gang published a blog post announcing their full support for the Russian state’s invasion of Ukraine [6]. 

Figure 1: The Conti gang's public declaration of their support for Russia's invasion of Ukraine

Within days of sharing their support for Russia, logs from a server hosting the group’s Jabber communications began to be leaked on Twitter by @ContiLeaks [7]. The leaked logs included records of conversations among nearly 500 threat actors between Jan 2020 and March 2022 [8]. The Jabber logs were supposedly stolen and leaked by a Ukrainian security researcher [3]/[6].

Affiliates of the Conti ransomware group were known to use BazarLoader to deliver Conti ransomware [9]. BumbleBee has now also been linked to the Conti ransomware group by several threat research teams [1]/[10]/[11]. The fact that threat actors’ transition from BazarLoader to BumbleBee coincides with the leak of Conti’s Jabber chat logs may indicate that the transition occurred as a result of the leaks [3]. Since the transition, BumbleBee has become a significant tool in the cyber-crime ecosystem, with links to several ransomware operations such as Conti, Quantum, and Mountlocker [11]. The rising use of BumbleBee by threat actors, and particularly ransomware actors, makes the early detection of BumbleBee key to identifying the preparatory stages of ransomware attacks.  

Intrusion Kill Chain 

In April 2022, Darktrace observed the following pattern of threat actor activity within the networks of several Darktrace clients: 

1.     Threat actor socially engineers user via email into running a BumbleBee payload on their device

2.     BumbleBee establishes HTTPS communication with a BumbleBee C2 server

3.     Threat actor instructs BumbleBee to download and execute Cobalt Strike Beacon

4.     Cobalt Strike Beacon establishes HTTPS communication with a Cobalt Strike C2 server

5.     Threat actor instructs Cobalt Strike Beacon to scan for open ports and to enumerate network shares

6.     Threat actor instructs Cobalt Strike Beacon to use the DCSync technique to obtain password account data from an internal domain controller

7.     Threat actor instructs Cobalt Strike Beacon to distribute malicious payloads to other internal systems 

With limited visibility over affected clients’ email environments, Darktrace was unable to determine how the threat actors interacted with users to initiate the BumbleBee infection. However, based on open-source reporting on BumbleBee [3]/[4]/[10]/[11]/[12]/[13]/[14]/[15]/[16]/[17], it is likely that the actors tricked target users into running BumbleBee by sending them emails containing either a malicious zipped ISO file or a link to a file storage service hosting the malicious zipped ISO file. These ISO files typically contain a LNK file and a BumbleBee DLL payload. The properties of these LNK files are set in such a way that opening them causes the corresponding DLL payload to run. 

In several cases observed by Darktrace, devices contacted a file storage service such as Microsoft OneDrive or Google Cloud Storage immediately before they displayed signs of BumbleBee infection. In these cases, it is likely that BumbleBee was executed on the users’ devices as a result of the users interacting with an ISO file which they were tricked into downloading from a file storage service. 

Figure 2: The above figure, taken from the event log for an infected device, shows that the device contacted a OneDrive endpoint immediately before making HTTPS connections to the BumbleBee C2 server, 45.140.146[.]244
Figure 3: The above figure, taken from the event log for an infected device, shows that the device contacted a Google Cloud Storage endpoint and then the malicious endpoint ‘marebust[.]com’ before making HTTPS connections to the  BumbleBee C2 servers, 108.62.118[.]61 and 23.227.198[.]217

After users ran a BumbleBee payload, their devices immediately initiated communications with BumbleBee C2 servers. The BumbleBee samples used HTTPS for their C2 communication, and all presented a common JA3 client fingerprint, ‘0c9457ab6f0d6a14fc8a3d1d149547fb’. All analysed samples excluded domain names in their ‘client hello’ messages to the C2 servers, which is unusual for legitimate HTTPS communication. External SSL connections which do not specify a destination domain name and whose JA3 client fingerprint is ‘0c9457ab6f0d6a14fc8a3d1d149547fb’ are potential indicators of BumbleBee infection. 

Figure 4:The above figure, taken from Darktrace's Advanced Search interface, depicts an infected device's spike in HTTPS connections with the JA3 client fingerprint ‘0c9457ab6f0d6a14fc8a3d1d149547fb’

Once the threat actors had established HTTPS communication with the BumbleBee-infected systems, they instructed BumbleBee to download and execute Cobalt Strike Beacon. This behaviour resulted in the infected systems making HTTPS connections to Cobalt Strike C2 servers. The Cobalt Strike Beacon samples all had the same JA3 client fingerprint ‘a0e9f5d64349fb13191bc781f81f42e1’ — a fingerprint associated with previously seen Cobalt Strike samples [18]. The domain names ‘fuvataren[.]com’ and ‘cuhirito[.]com’ were observed in the samples’ HTTPS communications. 

Figure 5:The above figure, taken from Darktrace's Advanced Search interface, depicts the Cobalt Strike C2 communications which immediately followed a device's BumbleBee C2 activity

Cobalt Strike Beacon payloads call home to C2 servers for instructions. In the cases observed, threat actors first instructed the Beacon payloads to perform reconnaissance tasks, such as SMB port scanning and SMB enumeration. It is likely that the threat actors performed these steps to inform the next stages of their operations.  The SMB enumeration activity was evidenced by the infected devices making NetrShareEnum and NetrShareGetInfo requests to the srvsvc RPC interface on internal systems.

Figure 6: The above figure, taken from Darktrace’s Advanced Search interface, depicts a spike in srvsvc requests coinciding with the infected device's Cobalt Strike C2 activity

After providing Cobalt Strike Beacon with reconnaissance tasks, the threat actors set out to obtain account password data in preparation for the lateral movement phase of their operation. To obtain account password data, the actors instructed Cobalt Strike Beacon to use the DCSync technique to replicate account password data from an internal domain controller. This activity was evidenced by the infected devices making DRSGetNCChanges requests to the drsuapi RPC interface on internal domain controllers. 

Figure 7: The above figure, taken from Darktrace’s Advanced Search interface, depicts a spike in DRSGetNCChanges requests coinciding with the infected device’s Cobalt Strike C2 activity

After leveraging the DCSync technique, the threat actors sought to broaden their presence within the targeted networks.  To achieve this, they instructed Cobalt Strike Beacon to get several specially selected internal systems to run a suspiciously named DLL (‘f.dll’). Cobalt Strike first established SMB sessions with target systems using compromised account credentials. During these sessions, Cobalt Strike uploaded the malicious DLL to a hidden network share. To execute the DLL, Cobalt Strike abused the Windows Service Control Manager (SCM) to remotely control and manipulate running services on the targeted internal hosts. Cobalt Strike first opened a binding handle to the svcctl interface on the targeted destination systems. It then went on to make an OpenSCManagerW request, a CreateServiceA request, and a StartServiceA request to the svcctl interface on the targeted hosts: 

·      Bind request – opens a binding handle to the relevant RPC interface (in this case, the svcctl interface) on the destination device

·      OpenSCManagerW request – establishes a connection to the Service Control Manager (SCM) on the destination device and opens a specified SCM database

·      CreateServiceA request – creates a service object and adds it to the specified SCM database 

·      StartServiceA request – starts a specified service

Figure 8: The above figure, taken from Darktrace’s Advanced Search interface, outlines an infected system’s lateral movement activities. After writing a file named ‘f.dll’ to the C$ share on an internal server, the infected device made several RPC requests to the svcctl interface on the targeted server

It is likely that the DLL file which the threat actors distributed was a Cobalt Strike payload. In one case, however, the threat actor was also seen distributing and executing a payload named ‘procdump64.exe’. This may suggest that the threat actor was seeking to use ProcDump to obtain authentication material stored in the process memory of the Local Security Authority Subsystem Service (LSASS). Given that ProcDump is a legitimate Windows Sysinternals tool primarily used for diagnostics and troubleshooting, it is likely that threat actors leveraged it in order to evade detection. 

In all the cases which Darktrace observed, threat actors’ attempts to conduct follow-up activities after moving laterally were thwarted with the help of Darktrace’s SOC team. It is likely that the threat actors responsible for the reported activities were seeking to deploy ransomware within the targeted networks. The steps which the threat actors took to make progress towards achieving this objective resulted in highly unusual patterns of network traffic. Darktrace’s detection of these unusual network activities allowed security teams to prevent these threat actors from achieving their disruptive objectives. 

Darktrace Coverage

Once threat actors succeeded in tricking users into running BumbleBee on their devices, Darktrace’s Self-Learning AI immediately detected the command-and-control (C2) activity generated by the loader. BumbleBee’s C2 activity caused the following Darktrace models to breach:

·      Anomalous Connection / Anomalous SSL without SNI to New External

·      Anomalous Connection / Suspicious Self-Signed SSL

·      Anomalous Connection / Rare External SSL Self-Signed

·      Compromise / Suspicious TLS Beaconing To Rare External

·      Compromise / Beacon to Young Endpoint

·      Compromise / Beaconing Activity To External Rare

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Suspicious TLS Beaconing To Rare External

·      Compromise / SSL Beaconing to Rare Destination

·      Compromise / Large Number of Suspicious Successful Connections

·      Device / Multiple C2 Model Breaches 

BumbleBee’s delivery of Cobalt Strike Beacon onto target systems resulted in those systems communicating with Cobalt Strike C2 servers. Cobalt Strike Beacon’s C2 communications resulted in breaches of the following models: 

·      Compromise / Beaconing Activity To External Rare

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Large Number of Suspicious Successful Connections

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / SSL or HTTP Beacon

·      Compromise / Slow Beaconing Activity To External Rare

·      Compromise / SSL Beaconing to Rare Destination 

The threat actors’ subsequent port scanning and SMB enumeration activities caused the following models to breach:

·      Device / Network Scan

·      Anomalous Connection / SMB Enumeration

·      Device / Possible SMB/NTLM Reconnaissance

·      Device / Suspicious Network Scan Activity  

The threat actors’ attempts to obtain account password data from domain controllers using the DCSync technique resulted in breaches of the following models: 

·      Compromise / Unusual SMB Session and DRS

·      Anomalous Connection / Anomalous DRSGetNCChanges Operation

Finally, the threat actors’ attempts to internally distribute and execute payloads resulted in breaches of the following models:

·      Compliance / SMB Drive Write

·      Device / Lateral Movement and C2 Activity

·      Device / SMB Lateral Movement

·      Device / Multiple Lateral Movement Model Breaches

·      Anomalous File / Internal / Unusual SMB Script Write

·      Anomalous File / Internal / Unusual Internal EXE File Transfer

·      Anomalous Connection / High Volume of New or Uncommon Service Control

If Darktrace/Network had been configured in the targeted environments, then it would have blocked BumbleBee’s C2 communications, which would have likely prevented the threat actors from delivering Cobalt Strike Beacon into the target networks. 

Figure 9: Attack timeline

Conclusion

Threat actors use loaders to smuggle more harmful payloads into target networks. Prior to March 2022, it was common to see threat actors using the BazarLoader loader to transfer their payloads into target environments. However, since the public disclosure of the Conti gang’s Jabber chat logs at the end of February, the cybersecurity world has witnessed a shift in tradecraft. Threat actors have seemingly transitioned from using BazarLoader to using a novel loader known as ‘BumbleBee’. Since BumbleBee first made an appearance in March 2022, a growing number of threat actors, in particular ransomware actors, have been observed using it.

It is likely that this trend will continue, which makes the detection of BumbleBee activity vital for the prevention of ransomware deployment within organisations’ networks. During April, Darktrace’s SOC team observed a particular pattern of threat actor activity involving the BumbleBee loader. After tricking users into running BumbleBee on their devices, threat actors were seen instructing BumbleBee to drop Cobalt Strike Beacon. Threat actors then leveraged Cobalt Strike Beacon to conduct network reconnaissance, obtain account password data from internal domain controllers, and distribute malicious payloads internally.  Darktrace’s detection of these activities prevented the threat actors from achieving their likely harmful objectives.  

Thanks to Ross Ellis for his contributions to this blog.

Appendices 

References 

[1] https://blog.google/threat-analysis-group/exposing-initial-access-broker-ties-conti/ 

[2] https://securityintelligence.com/posts/trickbot-gang-doubles-down-enterprise-infection/ 

[3] https://www.proofpoint.com/us/blog/threat-insight/bumblebee-is-still-transforming

[4] https://www.cynet.com/orion-threat-alert-flight-of-the-bumblebee/ 

[5] https://research.nccgroup.com/2022/04/29/adventures-in-the-land-of-bumblebee-a-new-malicious-loader/ 

[6] https://www.bleepingcomputer.com/news/security/conti-ransomwares-internal-chats-leaked-after-siding-with-russia/ 

[7] https://therecord.media/conti-leaks-the-panama-papers-of-ransomware/ 

[8] https://www.secureworks.com/blog/gold-ulrick-leaks-reveal-organizational-structure-and-relationships 

[9] https://www.prodaft.com/m/reports/Conti_TLPWHITE_v1.6_WVcSEtc.pdf 

[10] https://www.kroll.com/en/insights/publications/cyber/bumblebee-loader-linked-conti-used-in-quantum-locker-attacks 

[11] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/bumblebee-loader-cybercrime 

[12] https://isc.sans.edu/diary/TA578+using+thread-hijacked+emails+to+push+ISO+files+for+Bumblebee+malware/28636 

[13] https://isc.sans.edu/diary/rss/28664 

[14] https://www.logpoint.com/wp-content/uploads/2022/05/buzz-of-the-bumblebee-a-new-malicious-loader-threat-report-no-3.pdf 

[15] https://ghoulsec.medium.com/mal-series-23-malware-loader-bumblebee-6ab3cf69d601 

[16]  https://blog.cyble.com/2022/06/07/bumblebee-loader-on-the-rise/  

[17]  https://asec.ahnlab.com/en/35460/ 

[18] https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/

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
Sam Lister
Specialist Security Researcher

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May 1, 2026

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

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What are email-delivered prompt injection attacks?

As organizations rapidly adopt AI assistants to improve productivity, a new class of cyber risk is emerging alongside them: email-delivered AI prompt injection. Unlike traditional attacks that target software vulnerabilities or rely on social engineering, this is the act of embedding malicious or manipulative instructions into content that an AI system will process as part of its normal workflow. Because modern AI tools are designed to ingest and reason over large volumes of data, including emails, documents, and chat histories, they can unintentionally treat hidden attacker-controlled text as legitimate input.  

At Darktrace, our analysis has shown an increase of 90% in the number of customer deployments showing signals associated with potential prompt injection attempts since we began monitoring for this type of activity in late 2025. While it is not always possible to definitively attribute each instance, internal scoring systems designed to identify characteristics consistent with prompt injection have recorded a growing number of high-confidence matches. The upward trend suggests that attackers are actively experimenting with these techniques.

Recent examples of prompt injection attacks

Two early examples of this evolving threat are HashJack and ShadowLeak, which illustrate prompt injection in practice.

HashJack is a novel prompt injection technique discovered in November 2025 that exploits AI-powered web browsers and agentic AI browser assistants. By hiding malicious instructions within the URL fragment (after the # symbol) of a legitimate, trusted website, attackers can trick AI web assistants into performing malicious actions – potentially inserting phishing links, fake contact details, or misleading guidance directly into what appears to be a trusted AI-generated output.

ShadowLeak is a prompt injection method to exfiltrate PII identified in September 2025. This was a flaw in ChatGPT (now patched by OpenAI) which worked via an agent connected to email. If attackers sent the target an email containing a hidden prompt, the agent was tricked into leaking sensitive information to the attacker with no user action or visible UI.

What’s the risk of email-delivered prompt injection attacks?

Enterprise AI assistants often have complete visibility across emails, documents, and internal platforms. This means an attacker does not need to compromise credentials or move laterally through an environment. If successful, they can influence the AI to retrieve relevant information seamlessly, without the labor of compromise and privilege escalation.

The first risk is data exfiltration. In a prompt injection scenario, malicious instructions may be embedded within an ordinary email. As in the ShadowLeak attack, when AI processes that content as part of a legitimate task, it may interpret the hidden text as an instruction. This could result in the AI disclosing sensitive data, summarizing confidential communications, or exposing internal context that would otherwise require significant effort to obtain.

The second risk is agentic workflow poisoning. As AI systems take on more active roles, prompt injection can influence how they behave over time. An attacker could embed instructions that persist across interactions, such as causing the AI to include malicious links in responses or redirect users to untrusted resources. In this way, the attacker inserts themselves into the workflow, effectively acting as a man-in-the-middle within the AI system.

Why can’t other solutions catch email-delivered prompt injection attacks?

AI prompt injection challenges many of the assumptions that traditional email security is built on. It does not fit the usual patterns of phishing, where the goal is to trick a user into clicking a link or opening an attachment.  

Most security solutions are designed to detect signals associated with user engagement: suspicious links, unusual attachments, or social engineering cues. Prompt injection avoids these indicators entirely, meaning there are fewer obvious red flags.

In this case, the intention is actually the opposite of user solicitation. The objective is simply for the email to be delivered and remain in the inbox, appearing benign and unremarkable. The malicious element is not something the recipient is expected to engage with, or even notice.

Detection is further complicated by the nature of the prompts themselves. Unlike known malware signatures or consistent phishing patterns, injected prompts can vary widely in structure and wording. This makes simple pattern-matching approaches, such as regex, unreliable. A broad rule set risks generating large numbers of false positives, while a narrow one is unlikely to capture the diversity of possible injections.

How does Darktrace catch these types of attacks?

The Darktrace approach to email security more generally is to look beyond individual indicators and assess context, which also applies here.  

For example, our prompt density score identifies clusters of prompt-like language within an email rather than just single occurrences. Instead of treating the presence of a phrase as a blocking signal, the focus is on whether there is an unusual concentration of these patterns in a way that suggests injection. Additional weighting can be applied where there are signs of obfuscation. For example, text that is hidden from the user – such as white font or font size zero – but still readable by AI systems can indicate an attempt to conceal malicious prompts.

This is combined with broader behavioral signals. The same communication context used to detect other threats remains relevant, such as whether the content is unusual for the recipient or deviates from normal patterns.

Ask your email provider about email-delivered AI prompt injection

Prompt injection targets not just employees, but the AI systems they rely on, so security approaches need to account for both.

Though there are clear indications of emerging activity, it remains to be seen how popular prompt injection will be with attackers going forward. Still, considering the potential impact of this attack type, it’s worth checking if this risk has been considered by your email security provider.

Questions to ask your email security provider

  • What safeguards are in place to prevent emails from influencing AI‑driven workflows over time?
  • How do you assess email content that’s benign for a human reader, but may carry hidden instructions intended for AI systems?
  • If an email contains no links, no attachments, and no social engineering cues, what signals would your platform use to identify malicious intent?

Visit the Darktrace / EMAIL product hub to discover how we detect and respond to advanced communication threats.  

Learn more about securing AI in your enterprise.

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About the author
Kiri Addison
Senior Director of Product

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April 30, 2026

Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Anthropic’s Mythos and what it means for security teams

Recent attention on systems such as Anthropic Mythos highlights a notable problem for defenders. Namely that disclosure’s role in coordinating defensive action is eroding.

As AI systems gain stronger reasoning and coding capability, their usefulness in analyzing complex software environments and identifying weaknesses naturally increases. What has changed is not attacker motivation, but the conditions under which defenders learn about and organize around risk. Vulnerability discovery and exploitation increasingly unfold in ways that turn disclosure into a retrospective signal rather than a reliable starting point for defense.

Faster discovery was inevitable and is already visible

The acceleration of vulnerability discovery was already observable across the ecosystem. Publicly disclosed vulnerabilities (CVEs) have grown at double-digit rates for the past two years, including a 32% increase in 2024 according to NIST, driven in part by AI even prior to Anthropic’s Mythos model. Most notably XBOW topped the HackerOne US bug bounty leaderboard, marking the first time an autonomous penetration tester had done so.  

The technical frontier for AI capabilities has been described elsewhere as jagged, and the implication is that Mythos is exceptional but not unique in this capability. While Mythos appears to make significant progress in complex vulnerability analysis, many other models are already able to find and exploit weaknesses to varying degrees.  

What matters here is not which model performs best, but the fact that vulnerability discovery is no longer a scarce or tightly bounded capability.

The consequence of this shift is not simply earlier discovery. It is a change in the defender-attacker race condition. Disclosure once acted as a rough synchronization point. While attackers sometimes had earlier knowledge, disclosure generally marked the moment when risk became visible and defensive action could be broadly coordinated. Increasingly, that coordination will no longer exist. Exploitation may be underway well before a CVE is published, if it is published at all.

Why patch velocity alone is not the answer

The instinctive response to this shift is to focus on patching faster, but treating patch velocity as the primary solution misunderstands the problem. Most organizations are already constrained in how quickly they can remediate vulnerabilities. Asset sprawl, operational risk, testing requirements, uptime commitments, and unclear ownership all limit response speed, even when vulnerabilities are well understood.

If discovery and exploitation now routinely precede disclosure, then patching cannot be the first line of defense. It becomes one necessary control applied within a timeline that has already shifted. This does not imply that organizations should patch less. It means that patching cannot serve as the organizing principle for defense.

Defense needs a more stable anchor

If disclosure no longer defines when defense begins, then defense needs a reference point that does not depend on knowing the vulnerability in advance.  

Every digital environment has a behavioral character. Systems authenticate, communicate, execute processes, and access resources in relatively consistent ways over time. These patterns are not static rules or signatures. They are learned behaviors that reflect how an organization operates.

When exploitation occurs, even via previously unknown vulnerabilities, those behavioral patterns change.

Attackers may use novel techniques, but they still need to gain access, create processes, move laterally, and will ultimately interact with systems in ways that diverge from what is expected. That deviation is observable regardless of whether the underlying weakness has been formally named.

In an environment where disclosure can no longer be relied on for timing or coordination, behavioral understanding is no longer an optional enhancement; it becomes the only consistently available defensive signal.

Detecting risk before disclosure

Darktrace’s threat research has consistently shown that malicious activity often becomes visible before public disclosure.

In multiple cases, including exploitation of Ivanti, SAP NetWeaver, and Trimble Cityworks, Darktrace detected anomalous behavior days or weeks ahead of CVE publication. These detections did not rely on signatures, threat intelligence feeds, or awareness of the vulnerability itself. They emerged because systems began behaving in ways that did not align with their established patterns.

This reflects a defensive approach grounded in ‘Ethos’, in contrast to the unbounded exploration represented by ‘Mythos’. Here, Mythos describes continuous vulnerability discovery at speed and scale. Ethos reflects an understanding of what is normal and expected within a specific environment, grounded in observed behavior.

Revisiting assume breach

These conditions reinforce a principle long embedded in Zero Trust thinking: assume breach.

If exploitation can occur before disclosure, patching vulnerabilities can no longer act as the organizing principle for defense. Instead, effective defense must focus on monitoring for misuse and constraining attacker activity once access is achieved. Behavioral monitoring allows organizations to identify early‑stage compromise and respond while uncertainty remains, rather than waiting for formal verification.

AI plays a critical role here, not by predicting every exploit, but by continuously learning what normal looks like within a specific environment and identifying meaningful deviation at machine speed. Identifying that deviation enables defenders to respond by constraining activity back towards normal patterns of behavior.

Not an arms race, but an asymmetry

AI is often framed as fueling an arms race between attackers and defenders. In practice, the more important dynamic is asymmetry.

Attackers operate broadly, scanning many environments for opportunities. Defenders operate deeply within their own systems, and it’s this business context which is so significant. Behavioral understanding gives defenders a durable advantage. Attackers may automate discovery, but they cannot easily reproduce what belonging looks like inside a particular organization.

A changed defensive model

AI‑accelerated vulnerability discovery does not mean defenders have lost. It does mean that disclosure‑driven, patch‑centric models no longer provide a sufficient foundation for resilience.

As vulnerability volumes grow and exploitation timelines compress, effective defense increasingly depends on continuous behavioral understanding, detection that does not rely on prior disclosure, and rapid containment to limit impact. In this model, CVEs confirm risk rather than define when defense begins.

The industry has already seen this approach work in practice. As AI continues to reshape both offense and defense, behavioral detection will move from being complementary to being essential.

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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