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June 27, 2021

Post-Mortem Analysis of a SQL Server Exploit

Learn about the post-mortem analysis of a SQL Server exploit. Discover key insights and strategies to enhance your cybersecurity defenses.
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
Max Heinemeyer
Global Field CISO
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27
Jun 2021

While SaaS and IoT devices are increasingly popular vectors of intrusion, server-side attacks remain a serious threat to organizations worldwide. With sophisticated vulnerability scanning tools, attackers can now pinpoint security flaws in seconds, finding points of entry across the attack surface. Human security teams often struggle to keep pace with the constant wave of newly documented vulnerabilities and patches.

Darktrace recently stopped a targeted cyber-attack by an unknown attacker. After the initial entry, the attacker exploited an unpatched vulnerability (CVE-2020-0618), granting a low-privileged credential the ability to remotely execute code. This enabled the attacker to spread laterally and eventually establish a foothold in the system by creating a new user account.

The server-side attack cycle: authenticates user; scans network; infects three servers; downloads malware; c2 traffic; creates new user.

Figure 1: Overview of the server-side attack cycle.

This blog breaks down the intrusion and explores how Darktrace’s Autonomous Response technology took three surgical actions to halt the attacker’s movements.

Unknown threat actors exploit a vulnerability

Initial compromise

At a financial firm in Canada with around 3,000 devices, Cyber AI detected the use of a new credential, ‘parents’. The attacker used this credential to access the company’s internal environment through the VPN. From there, the credential authenticated to a desktop using NT LAN Manager (NTLM). No further suspicious activity was observed.

NTLM is a popular attack vector for cyber-criminals as it is vulnerable to multiple methods of compromise, including brute-force and ‘pass the hash’. The initial access to the credential could have been obtained via phishing before Darktrace had been deployed.

Figure 2: The credential was first observed on the device five days prior to reconnaissance. The attacker performed reconnaissance and lateral movement for two days, until the compromised devices were taken down.

Internal reconnaissance

Five days later, the ‘parents’ credential was seen logging onto the desktop. The desktop began scanning the network – over 80 internal IPs – on Port 443 and 445.

Shortly after the scan, the device used Nmap to attempt to establish SMBv1 sessions to 139 internal IPs, using guest / user credentials. 79 out of the 278 sessions were successful, all using the login.

Figure 3: New failed internal connections performed by an initially infected desktop, in a similar incident. The graph highlights a surge in failed internal connections and model breaches.

The network scan was the first stage after intrusion, enabling the attacker to find out which services were running, before looking for unpatched vulnerabilities.

Nmap has multiple built-in functionalities which are often exploited for reconnaissance and lateral movement. In this case, it was being used to establish the SMBv1 sessions to the domain controller, saving the attacker from having to initiate SMBv1 sessions with each destination one by one. SMBv1 has well-known vulnerabilities and best practice is to disable it where possible.

Lateral movement

The desktop began controlling services (svcctl endpoint) on a SQL server. It was observed both creating and starting services (CreateServiceW, StartServiceW).

The desktop then initiated an unencrypted HTTP connection to a SQL Reporting server. This was the first HTTP connection between the two devices and the first time the user agent had been seen on the device.

A packet capture of the connection reveals a POST that is seen in an exploit of CVE-2020-0613. This vulnerability is a deserialization issue, whereby the server mishandles carefully crafted page requests and allows low-privileged accounts to establish a reverse shell and remotely execute code on the server.

Figure 4: A partial PCAP of the HTTP connection. The traffic matches the CVE-2020-0618 exploit, which enables Remote Code Execution (RCE) in SQL Server Reporting Services (SSRS).

Most movements were seen in East-West traffic, with readily-available remote procedure call (RPC) methods. Such connections are abundant in systems. Without learning an organization’s ‘pattern of life’, it would have been near-impossible to highlight the malicious connections.

Cyber AI detected connections to the svcctl endpoint, via the DCE-RPC endpoint. This is called the 'service control' endpoint and is used to remotely control running processes on a device.

During the lateral movement from the desktop, the HTTP POST request revealed that the desktop was exploiting CVE-2020-0613. The attacker had managed to find and exploit an existing vulnerability which hadn’t been patched.

Darktrace was the only tool which alerted to the HTTP connection, revealing this underlying (and concluding) exploit. The AI determined that the user agent was unusual for the device and for the wider organization, and that the connection was highly anomalous. This connection would have gone otherwise amiss, since HTTP connections are common in most digital environments.

Because the attacker on the desktop used readily-available tools and protocols, such as Nmap, DCE-RPC, and HTTP, the device went undetected by all the other cyber defenses. However, Cyber AI noticed multiple scanning and lateral movement anomalies – triggering high-fidelity detections which would have been alerted to with Proactive Threat Notifications.

Command and control (C2) communication

The next day, the attacker connected to an SNMP server from the VPN. The connection used the ‘parents’ RDP cookie.

Immediately after the RDP connection began, the server connected to Pastebin and downloaded small amounts of encrypted data. Pastebin was likely being used as a vector to drop malicious scripts onto the device.

The SNMP server then started controlling services (svcttl) on the SQL server: again, creating and starting services.

Following this, both the SQL server and the SNMP server made a high volume of SSL connections to a rare external domain. One upload to the destination was around 21 MB, but otherwise the connections were mostly the same packet size. This, among other factors, indicated that the destination was being used as a C2 server.

Figure 5: Example Cyber AI Analyst investigation into beaconing activity by a SQL server.

With just one compromised credential, the attacker was now connecting to the VPN and infecting multiple servers on the company’s internal network.

The attacker dropped scripts onto the host using Pastebin. Darktrace alerted on this because Pastebin is highly rare for the organization. In fact, these connections were the first time it had been seen. Most security tools would miss this, as Pastebin is a legitimate site and would not be blocked by open-source intelligence (OSINT).

Even if a lesser-known Pastebin alternative had been used – say, in an environment where Pastebin was blocked on the firewall but the alternative not — Darktrace would have picked up on it in exactly the same way.

The C2 beaconing endpoint – dropbox16[.]com – has no OSINT information available online. The connections were on Port 443 and nothing about them was notable except from their rarity on the company’s system. Darktrace sent alerts because of its high rarity, rather than relying on known signatures.

Achieve persistence

After another Pastebin pull, the attacker attempted to maintain a greater foothold and escalate privileges by creating a new user using the SamrCreateUser2InDomain operation (endpoint: samr).

To establish persistence, the attacker now created a new user through a specific DCE-RPC command to the domain controller. This was highly unusual activity for the device, and was given a 100% anomaly score for ‘New or Uncommon Occurrence’.

If Darktrace had not alerted on this activity, the attacker would have continued to access files and make further inroads in the company, extracting sensitive data and potentially installing ransomware. This could have led to sensitive data loss, reputational damage, and financial losses for the company.

The value of Autonomous Response

The organization had Antigena in passive mode, so although it was not able to respond autonomously, we have visibility into the actions that it would have taken.

Antigena would have taken three actions on the initially infected desktop, as shown in the table below. The actions would have taken effect immediately in response to the first scan and the first service control requests.

During the two days of reconnaissance and lateral movement activity, these were the only steps Antigena suggested. The steps were all directly relevant to the intrusion – there was no attempt to block anything unrelated to the attack, and no other Antigena actions were triggered during this period.

By surgically blocking connections on specific ports during the scanning activity and enforcing the ‘pattern of life’ on the infected desktop, Antigena would have paralyzed the attacker’s reconnaissance efforts.

Furthermore, unusual service control attempts performed by the device would have been halted, minimizing the damage to the targeted destination.

Antigena would have delivered these blocks directly or via whatever integration was most suitable for the customer, such as firewall integrations or NAC integrations.

Lessons learned

The threat story above demonstrates the importance of controlling the access granted to low-privileged credentials, as well as remaining up-to-date with security patches. Since such attacks take advantage of existing network infrastructure, it is extremely difficult to detect these anomalous connections without the use of AI.

There was a delay of several days between the initial use of the ‘parents’ credentials and the first signs of lateral movement. This dormancy period – between compromise and the start of internal activities – is commonly seen in attacks. It likely indicates that the attacker was checking initially if their access worked, and then re-visiting the victim for further compromise once their schedule allowed for it.

Stopping a server-side attack

This compromise is reflective of many real-life intrusions: attacks cannot be easily attributed and are often conducted by sophisticated, unidentified threat actors.

Nevertheless, Darktrace managed to detect each stage of the attack cycle: initial compromise, reconnaissance, lateral movement, established foothold, and privilege escalation, and had Antigena been in active mode, it would have blocked these connections, and even prevented the initial desktop from ever exploiting the SQL vulnerability, which allowed the attacker to execute code remotely.

One day later, after seeing the power of Autonomous Response, the company decided to deploy Antigena in active mode.

Thanks to Darktrace analyst Isabel Finn for her insights on the above threat find.

Darktrace model detections:

  • Device / Anomalous Nmap SMB Activity
  • Device / Network Scan - Low Anomaly Score
  • Device / Network Scan
  • Device / ICMP Address Scan
  • Device / Suspicious Network Scan Activity
  • Anomalous Connection / New or Uncommon Service Control
  • Device / Multiple Lateral Movement Model Breaches
  • Device / New User Agent To Internal Server
  • Compliance / Pastebin
  • Device / Repeated Unknown RPC Service Bind Errors
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Unusual Connections to Rare Lets Encrypt
  • User / Anomalous Domain User Creation Or Addition To Group


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
Max Heinemeyer
Global Field CISO

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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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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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