Blog
/
/
October 29, 2017

New Botnet Utilizes IoT Drawing Pads for Attacks

Discover how a new botnet is utilizing IoT drawing pads for reflection attacks. Stay informed on evolving cyber security threats with Darktrace experts.
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
Justin Fier
SVP, Red Team Operations
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
29
Oct 2017

Earlier this year, Darktrace detected a new botnet engaged in a large-scale reflection and amplification attack targeting organizations around the world, including several governmental bodies. This attack is more pertinent than ever in light of potentially new, bigger, and more sophisticated IoT hacks, in the likes of the recently reported ‘Reaper Botnet’, we can increasingly expect to see in 2018.

This new type of botnet we detected earlier this year wasn’t using desktop computers to power its attacks – like Srizbi did when it was sending out 60 billion spam emails per day – and its methodology was distinct from Mirai – which uses DRVs and routers to generate DNS DDoS attacks with speeds of up to 1Tbps.

Instead, this new botnet was commandeering an unlikely assortment of devices made up of, among other things, IoT drawing pads. It contained far fewer devices than typical botnets, but through reflection and amplification techniques using SNMP, it was attempting to launch a powerful denial-of-service attack.

The threat began in a familiar fashion. An architectural firm introduced smart drawing into their network pads without alerting the IT team, and their internal security controls had no way of identifying the vulnerable devices. As such, the devices’ user credentials were never changed from the factory defaults.

Those credentials, along with their public string for SNMP authentication, were publicly available on Shodan, which also revealed that the devices had open ports for HTTP, HTTPS, Telnet, and SIP.

Darktrace detected the vulnerability when hundreds of external IP addresses from around the world made several thousand of SNMP connections to the devices over UDP port 161. Over 99 percent of these connections contained at least one “GetBulkRequest”, an SNMP operation used for the retrieval of large amounts of data.

In response to these requests, the devices issued an exponentially larger number of replies via “GetResponse”, some of which contained as many as 397,000 “GetResponse” objects. In 64 cases, the devices uploaded over 1MB of data.

A sample of this SNMP activity as observed by Darktrace’s AI algorithms:

Figure 1: Anomalous SNMP connections – the request and response are presented as two separate SNMP connections, but we can treat them as the same connection.

Normal network activity for these devices involved very occasional use of “GetBulkRequests” and “GetResponses.” Therefore, these spikes in activity were deemed highly anomalous by Darktrace’s AI algorithms, which had built a deep understanding of normal activity for the devices. By detecting the threat in real time, the security team discovered the threat while it was still in its early stages, and Darktrace’s network visibility provided detailed analytics on the incident.

Figure 2: The number of inbound connections on port 161 to one of the devices on port 161 is shown in orange. External data transfer from port 161 are shown in green. Time, top left, is x-axis value at right-hand origin.

The use of SNMP version 2c and “GetBulkRequests” were telltale signs of a reflection and amplification attack, which use scant resources to generate large attacks. All told, 273.2MiB left the devices on port 161.

The external data transfers on port 80 indicated that the attack went even further, as numerous external devices were attempting to access the devices’ HTTP resources, many of which were administrative PHP files.

A sample of resources that external devices attempted to access via HTTP:

/phpMyAdmin-2.11.1.0/scripts/setup.php
/a_remotecontrol.htm
/phpMyAdmin-3.1.2.0-all-languages/scripts/setup.php
/mysqladmin/scripts.setup.php

Figure 3: The total outbound data from port 161 over the reporting period

Finally, Shodan also revealed that the devices were running an accessible SIP server on port 5060. Packet analysis showed that external devices “dialed” the devices and attempted to place a VoIP – strange behavior on the attacker’s part that remains unexplained.

Figure 4: VoIP call attempts via open SIP protocol

The target IP addresses were likely spoofed. By sending hundreds of “GetBulkRequests” from the spoofed IPs of the target networks, the IoT drawing pads were forced to send back more than 100 times the number of “GetResponses.” This is testament to the power of reflection and amplification attacks. It’s unclear what other devices were used in this attack, but even a small number of IoT devices at the architectural firm were able to generate an alarming amount of traffic.

The target IPs belonged to websites owned by entertainment and design companies, and even governmental bodies. By reporting on the anomalous SNMP requests as soon as they began, the firm’s security team was able to take the drawing pads offline before damage was done.

Had the attack succeeded in sabotaging the target networks, the firm could have been subject to legal action. The company revamped their security policies and made strides to secure all the IoT devices on their network to minimize risk of future incidents.

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
Justin Fier
SVP, Red Team Operations

More in this series

No items found.

Blog

/

Identity

/

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

Default blog imageDefault blog image

The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

Proactive Security

/

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Default blog imageDefault blog image

Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
Your data. Our AI.
Elevate your network security with Darktrace AI