Blog
/
Network
/
August 14, 2025

From Exploit to Escalation: Tracking and Containing a Real-World Fortinet SSL-VPN Attack

This blog outlines a real-world attack where threat actors exploited Fortinet SSL-VPN vulnerabilities to infiltrate a network. It highlights how Darktrace identified threat and took immediate actions to contain it, demonstrating the importance of proactive detection and rapid intervention in minimizing risk and disruption.
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
Priya Thapa
Cyber Analyst
Fortinet SSL-VPN AttackDefault blog image
14
Aug 2025

Threat actors exploiting Fortinet CVEs

Over the years, Fortinet has issued multiple alerts about a wave of sophisticated attacks targeting vulnerabilities in its SSL-VPN infrastructure. Despite the release of patches to address these vulnerabilities, threat actors have continued to exploit a trio of Common Vulnerabilities and Exposures (CVEs) disclosed between 2022 and 2024 to gain unauthorized access to FortiGate devices.

Which vulnerabilities are exploited?

The vulnerabilities—CVE-2022-42475, CVE-2023-27997, and CVE-2024-21762—affect Fortinet’s SSL-VPN services and have been actively exploited by threat actors to establish initial access into target networks.

The vulnerabilities affect core components of FortiOS, allowing attackers to execute remote code on affected systems.

CVE-2022-42475

Type: Heap-Based Buffer Overflow in FortiOS SSL-VPN

Impact: Remote Code Execution (Actively Exploited)

This earlier vulnerability also targets the SSL-VPN interface and has been actively exploited in the wild. It allows attackers to execute arbitrary code remotely by overflowing a buffer in memory, often used to deploy malware or establish persistent backdoors [6].

CVE-2023-27997

Type: Heap-Based Buffer Overflow in FortiOS and FortiProxy

Impact: Remote Code Execution

This flaw exists in the SSL-VPN component of both FortiOS and FortiProxy. By exploiting a buffer overflow in the heap memory, attackers can execute malicious code remotely. This vulnerability is particularly dangerous because it can be triggered without authentication, making it ideal for an initial compromise [5].

CVE-2024-21762

Type: Out-of-Bounds Write in sslvpnd

Impact: Remote Code Execution

This vulnerability affects the SSL-VPN daemon (sslvpnd) in FortiOS. It allows unauthenticated remote attackers to send specially crafted HTTP requests that write data outside of allocated memory bounds. This can lead to arbitrary code execution, giving attackers full control over a device [4].

In short, these flaws enable remote attackers to execute arbitrary code without authentication by exploiting memory corruption issues such as buffer overflows and out-of-bounds writes. Once inside, threat actors use symbolic link (symlink) in order to maintain persistence on target devices across patches and firmware updates. This persistence then enables them to bypass security controls and manipulate firewall configurations, effectively turning patched systems into long-term footholds for deeper network compromise [1][2][3].

Darktrace’s Coverage

Darktrace detected a series of suspicious activities originating from a compromised Fortinet VPN device, including anomalous HTTP traffic, internal network scanning, and SMB reconnaissance, all indicative of post-exploitation behavior. Following initial detection by Darktrace’s real-time models, its Autonomous Response capability swiftly acted on the malicious activity, blocking suspicious connections and containing the threat before further compromise could occur.

Further investigation by Darktrace’s Threat Research team uncovered a stealthy and persistent attack that leveraged known Fortinet SSL-VPN vulnerabilities to facilitate lateral movement and privilege escalation within the network.

Phase 1: Initial Compromise – Fortinet VPN Exploitation

The attack on a Darktrace customer likely began on April 11 with the exploitation of a Fortinet VPN device running an outdated version of FortiOS. Darktrace observed a high volume of HTTP traffic originating from this device, specifically targeting internal systems. Notably, many of these requests were directed at the /cgi-bin/ directory,  a common target for attackers attempting to exploit web interfaces to run unauthorized scripts or commands. This pattern strongly indicated remote code execution attempts via the SSL-VPN interface [7].

Once access was gained, the threat actor likely modified existing firewall rules, a tactic often used to disable security controls or create hidden backdoors for future access. While Darktrace does not have direct visibility into firewall configuration changes, the surrounding activity and post-exploitation behavior indicated that such modifications were made to support long-term persistence within the network.

HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080.
Figure 1: HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080

Phase 2: Establishing Persistence & Lateral Movement

Shortly after the initial compromise of the Fortinet VPN device, the threat actor began to expand their foothold within the internal network. Darktrace detected initial signs of network scanning from this device, including the use of Nmap to probe the internal environment, likely in an attempt to identify accessible services and vulnerable systems.

Darktrace’s detection of unusual network scanning activities on the affected device.
Figure 2: Darktrace’s detection of unusual network scanning activities on the affected device.

Around the same time, Darktrace began detecting anomalous activity on a second device, specifically an internal firewall interface device. This suggested that the attacker had established a secondary foothold and was leveraging it to conduct deeper reconnaissance and move laterally through the network.

In an effort to maintain persistence within the network, the attackers likely deployed symbolic links in the SSL-VPN language file directory on the Fortinet device. While Darktrace did not directly observe symbolic link abuse, Fortinet has identified this as a known persistence technique in similar attacks [2][3]. Based on the observed post-exploitation behavior and likely firewall modifications, it is plausible that such methods were used here.

Phase 3: Internal Reconnaissance & Credential Abuse

With lateral movement initiated from the internal firewall interface device, the threat actor proceeded to escalate their efforts to map the internal network and identify opportunities for privilege escalation.

Darktrace observed a successful NTLM authentication from the internal firewall interface to the domain controller over the outdated protocol SMBv1, using the account ‘anonymous’. This was immediately followed by a failed NTLM session connection using the hostname ‘nmap’, further indicating the use of Nmap for enumeration and brute-force attempts. Additional credential probes were also identified around the same time, including attempts using the credential ‘guest’.

Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.
Figure 3: Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.

The attacker then initiated DCE_RPC service enumeration, with over 300 requests to the Endpoint Mapper endpoint on the domain controller. This technique is commonly used to discover available services and their bindings, often as a precursor to privilege escalation or remote service manipulation.

Over the next few minutes, Darktrace detected more than 1,700 outbound connections from the internal firewall interface device to one of the customer’s subnets. These targeted common services such as FTP (port 21), SSH (22), Telnet (23), HTTP (80), and HTTPS (443). The threat actor also probed administrative and directory services, including ports 135, 137, 389, and 445, as well as remote access via RDP on port 3389.

Further signs of privilege escalation attempts were observed with the detection of over 300 Netlogon requests to the domain controller. Just over half of these connections were successful, indicating possible brute-force authentication attempts, credential testing, or the use of default or harvested credentials.

Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.
Figure 4: Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.

Phase 4: Privilege Escalation & Remote Access

A few minutes later, the attacker initiated an RDP session from the internal firewall interface device to an internal server. The session lasted over three hours, during which more than 1.5MB of data was uploaded and over 5MB was downloaded.

Notably, no RDP cookie was observed during this session, suggesting manual access, tool-less exploitation, or a deliberate attempt to evade detection. While RDP cookie entries were present on other occasions, none were linked to this specific session—reinforcing the likelihood of stealthy remote access.

Additionally, multiple entries during and after this session show SSL certificate validation failures on port 3389, indicating that the RDP connection may have been established using self-signed or invalid certificates, a common tactic in unauthorized or suspicious remote access scenarios.

Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.
Figure 5: Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.

Darktrace Autonomous Response

Throughout the course of this attack, Darktrace’s Autonomous Response capability was active on the customer’s network. This enabled Darktrace to autonomously intervene by blocking specific connections and ports associated with the suspicious activity, while also enforcing a pre-established “pattern of life” on affected devices to ensure they were able to continue their expected business activities while preventing any deviations from it. These actions were crucial in containing the threat and prevent further lateral movement from the compromised device.

Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.
Figure 6: Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.

Conclusion

This incident highlights the importance of important staying on top of patching and closely monitoring VPN infrastructure, especially for internet-facing systems like Fortinet devices. Despite available patches, attackers were still able to exploit known vulnerabilities to gain access, move laterally and maintain persistence within the customer’s network.

Attackers here demonstrated a high level of stealth and persistence. Not only did they gain access to the network and carry out network scans and lateral movement, but they also used techniques such as symbolic link abuse, credential probing, and RDP sessions without cookies to avoid detection.  Darktrace’s detection of the post-exploitation activity, combined with the swift action of its Autonomous Response technology, successfully blocked malicious connections and contained the attack before it could escalate

Credit to Priya Thapa (Cyber Analyst), Vivek Rajan (Cyber Analyst), and Ryan Traill (Analyst Content Lead)

Appendices

Real-time Detection Model Alerts

·      Device / Suspicious SMB Scanning Activity

·      Device / Anomalous Nmap Activity

·      Device / Network Scan

·      Device / RDP Scan

·      Device / ICMP Address Scan

Autonomous Response Model Alerts:  

·      Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK Mapping

Initial Access – External Remote Services – T1133

Initial Access – Valid Accounts – T1078

Execution – Exploitation for Client Execution – T1203

Persistence – Account Manipulation – T1098

Persistence – Application Layer Protocol – T1071.001

Privilege Escalation – Exploitation for Privilege Escalation – T1068

Privilege Escalation – Valid Accounts – T1078

Defense Evasion – Masquerading – T1036

Credential Access – Brute Force – T1110

Discovery – Network Service Scanning – T1046

Discovery – Remote System Discovery – T1018

Lateral Movement – Remote Services – T1021

Lateral Movement – Software Deployment Tools – T1072

Collection – Data from Local System – T1005

Collection – Data Staging – T1074

Exfiltration – Exfiltration Over Alternative Protocol – T1048

References

[1]  https://www.tenable.com/blog/cve-2024-21762-critical-fortinet-fortios-out-of-bound-write-ssl-vpn-vulnerability

[2] https://thehackernews.com/2025/04/fortinet-warns-attackers-retain.html

[3] https://www.cisa.gov/news-events/alerts/2025/04/11/fortinet-releases-advisory-new-post-exploitation-technique-known-vulnerabilities

[4] https://www.fortiguard.com/psirt/FG-IR-24-015

[5] https://www.tenable.com/blog/cve-2023-27997-heap-based-buffer-overflow-in-fortinet-fortios-and-fortiproxy-ssl-vpn-xortigate

[6]  https://www.tenable.com/blog/cve-2022-42475-fortinet-patches-zero-day-in-fortios-ssl-vpns

[7] https://www.fortiguard.com/encyclopedia/ips/12475

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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
Priya Thapa
Cyber Analyst

More in this series

No items found.

Blog

/

Email

/

July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

Default blog imageDefault blog image

Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

[related-resource]

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

Blog

/

/

July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

behavioral detectionDefault blog imageDefault blog image

Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

[related-resource]

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI