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
Keith Siepel
IT Manager, Hydrotech, Inc.
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04
Feb 2019
The following guest-authored blog post examines an advanced cyber-threat discovered by Darktrace on a customer’s network.
Previously I have talked about how Darktrace is a force multiplier for Hydrotech. As an example of this, I am sharing the anatomy of a zero-day trojan that was caught by our Darktrace system on the afternoon of Thursday, January 17. The following process was completed, in its entirety, within 20 minutes.
Remediation started within five minutes of the initial identification of the VMWare recompose process. Although the following notifications appeared at 1:38 p.m., I was working on another unrelated issue and didn’t find this information until 2:15 p.m., at which point I started my investigation and remediation efforts.
Darktrace Email Notifications @ 1:38PM EST 1/17/2018: 2019-01-17 18:37:57 UTC o365n-88.ad.hydrotech[.]com breached "Antigena / Network / External Threat / Antigena Malware File Pattern of Life Block"
FileTransfer::Exe file transfer started with filetype (application/x-dosexec)
FileTransfer::Exe file transfer started with filetype (application/x-dosexec)
2019-01-17 18:38:05 UTC o365n-88.ad.hydrotech[.]com breached "Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block"
Anomalous File / Multiple EXE from Rare External Locations
2019-01-17 18:38:14 UTC o365n-88.ad.hydrotech[.]com breached "Antigena / Network / External Threat / Antigena File then New Outbound Block"
Anomalous File / EXE from Rare External Location
Review of Darktrace breach logs
The first breach log showed a file downloaded by the name “MediaTable.bin.”
This was followed shortly after by a second file downloaded by the name “OfficeActivate.bin.”
At this point I contacted the end user and told them that I was going to perform an emergency recompose within VMWare — restoring their VM to a previously known good version of the operating system — to block a suspicious software that they had downloaded 30 minutes prior. This action effectively removes any applications that have been installed on the virtual desktop computer.
After starting the recompose efforts, I then proceeded to run the URLs that I had gathered through virustotal.com to see what had been downloaded:
For the file MediaTable.bin, virustotal.com informed me that four engines detected the URL as containing malicious content.
For the file OfficeActivate.bin, virustotal.com informed me that three engines detected the URL as containing malicious content.
Review of our Intrusion Detection System on the firewall showed the following initial approval, followed by a second alert — several hours later — that changed the approval to a diagnostic of malicious, after the files had already been downloaded.
1/17/2019 13:38 File Scanned 69.163.33[.]84 Allowed OfficeActivate.bin downloaded from [http://69.163.33[.]84:8080/ELjOX2c8/OfficeActivate.bin] 1/17/2019 13:37 File Scanned 91.205.215[.]13 Allowed MediaTable.bin downloaded from [http://91.205.215[.]13:8080/O11L9Qub/MediaTable.bin] 1/17/2019 19:34 File Disposition Changed Malicious Disposition was Unknown and has been seen 1 time: OfficeActivate.bin 1/17/2019 19:34 File Disposition Changed Malicious Disposition was Unknown and has been seen 1 time: MediaTable.bin
I then input the IP addresses previously identified into the Darktrace interface to determine if any other devices had accessed them. Fortunately, I found that they had not.
Images of the event logs for those IP addresses from within Darktrace are as follows:
Event log for 69.163.33[.]84.
Event log for 91.205.215[.]13.
Further research showed that this attack was, in fact, a zero-day trojan that was first detected in the wild on January 17, 2019 — the same day as our breach. My review of the forensics for this breach, along with my review of the activity of the user utilizing the victimized virtual machine, revealed that the attack originated from this user clicking on a phishing link from their email.
I feel fairly lucky that I have Darktrace, because without it I am not sure if or when this trojan would have been identified on our network.
If there is anyone out there who has questions about Darktrace, please message me privately, as I have just become Darktrace’s biggest evangelist!
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.
Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems
Three shifts have reshaped what it means to defend an enterprise securely.
First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.
Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.
Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.
If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.
This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.
A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.
The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.
In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.
This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.
Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.
Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.
CVE
CVE public disclosure date
Darktrace detection date
Days between detection of exploitation and CVE public disclosure
CVE-2025-0994Trimble Cityworks
2025-02-06
2025-01-19
18 days
CVE-2025-24183Apache
2025-03-10
2025-02-18
20 days
CVE-2025-10035Fortra GoAnywhere
2025-09-18
2025-09-11
7 days
CVE-2026-0257PAN-OS
2026-05-13
—
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Identity is the real control plane
The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.
Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.
This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.
In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however, they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.
Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.
AI accelerates the threat
The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.
The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.
The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.
Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.
1. AI as an Attack Multiplier
In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].
Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.
What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.
Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.
AI as a trusted but dangerous actor
This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.
The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.
In these scenarios, the security challenge shifts from validating access to validating behavior.
This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.
Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.
Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.
The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.
For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.
Conclusion
Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.
In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.
Credit to: Daniel Levy, Threat Hunting Data Scientist
2026年6月12日、DarktraceはLiteLLM-Proxyという名前のAmazon Web Service (AWS) EC2インスタンスから暗号通貨マイニング発生中とみられるアクティビティを観測しました。このインスタンスはLiteLLMアクティビティをサポートしており、Amazon Bedrockリソースへのアクセス権を有するインスタンスプロファイルと関連付けられていました。
