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August 2, 2024

Safelink Smuggling: Enhancing Resilience Against Malicious Links

Gain insights into safelink smuggling tactics and learn strategies to protect your organization from the dangers posed by malicious links.
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
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02
Aug 2024

Darktrace security members and researchers have recently seen a rise in what we are calling Safelink Smuggling. Safelinks are URLs rewritten by security solutions to enable additional analysis when the URL is clicked. Once analyzed, they may prompt a user, redirect the browser back to the original URL, or block further access if deemed necessary.

What is Safelink Smuggling?

Safelink Smuggling is a technique that involves an attacker purposely getting their malicious payload rewritten by a security solution’s Safelink capability to then propagate the rewritten URL to others. This technique is a way for attackers to not only avoid detection by traditional email security and other solutions, but also to instill mistrust in all email security solutions. As a result, Safelinks from a range of popular email security providers are often seen in phishing or supply chain attacks. In fact, Darktrace has observed over 300,000 cases of Safelinks being included in unexpected and suspicious contexts over the last 3 months.

How does Safelink Smuggling work?

Safelink Smuggling has two key stages: Getting a malicious link rewritten by an email security solution, then propagating that rewritten link to other victims.

Step one:

Obfuscated a malicious payload through a Safelink capability rewriting the link; Darktrace has seen this attempted through two methods – Compromised Account or Reply-Chain.

  • Method 1: Compromised Account

If an attacker can gain access to a compromised account – whether that’s through brute force, malware or credential theft – they can infiltrate it with malicious links, and then exfiltrate the Safelinks created as the email passes through security filtering. In other words, attackers will send a malicious payload to the compromised inbox, with the intent that the malicious URL gets rewritten. Unlike a normal phishing email where the threat actor wants to avoid having their email blocked, in this case the objective is for the email to get through to the inbox with the link rewritten. As observed by Darktrace, attackers often send the link in isolation as any additional components (i.e., body text or other content in the email) could cause a more severe action such as the email security solution holding the message.

  • Method 2: Reply-Chain

With this method, the attacker sends a malicious link to an email security vendor’s customer in an attempt to solicit a reply from an internal user. This allows them to grab the re-written URL within the reply chain. However, this is a risky tactic which can fail at several points. The attacker has to be confident the initial email won't be blocked outright; they also risk alerting security vendors to the address and the URL intended to be used for the main campaign. They also must be confident that the checks made when the re-written URL is clicked will not lead to a block at the final destination.
Regardless of the method used, the end result will appear as follows:

For example, the original malicious URL may look like this,

faceldu[.]org/Invoice112.zip

(negative surface indicators: recently registered domain, file extension)

And after being rewritten,

securityvevndor[.]com/safe?q=aNDF80dfaAkAH930adbd

(positive surface indicators: established domain, positive reputation, associated with safe content)

Step Two:

Now that the attacker has access to a malicious URL that has been obfuscated by a safe rewrite, attackers can forward or craft an email leveraging that same link. In fact, we have even seen multiple layers of Safelink Smuggling being used to mask a payload further.

The Challenge of Link Rewriting

Traditional email security solutions rewrite all links sent to an organization, but there is an inherent risk to this methodology. Rewriting every link, whether harmless or harmful, leads employees to lose context and creates a false sense of security when interacting with rewritten links in emails. Furthermore, it provides attackers with many opportunities to exploit Safelinks. As demonstrated in Method 2 above, if an email security solution does not rewrite every link, executing such attacks would be significantly more challenging.

Traditionally, rewriting every link made sense from a security perspective, as it allowed servers to thoroughly analyze links for known attack patterns and signatures. However, this approach relies on identifying previously recognized threats. Conversely, Darktrace / EMAIL gathers sufficient information about a link without needing to rewrite it, by analyzing the context and content of the email and the link itself.

In fact, Darktrace is the pioneer in applying selective rewriting to URLs based on suspicious properties or context, a method that other solutions have since adopted. While traditional solutions rewrite links to assess them only after they are clicked, Darktrace / EMAIL takes immediate action to neutralize threats before they reach the inbox.

Darktrace achieves high success rates in detecting malicious links and emails on the first encounter using Self-Learning AI. By understanding 'normal' behavior in email communications, Darktrace identifies subtle deviations indicative of cyber threats and selectively rewrites only those links deemed suspicious, ensuring a targeted, proportionate, and non-disruptive response.

Why do traditional email security solutions miss Safelink attacks?

Traditional security solutions that focus on learning attack patterns will miss Safelink threats as they are often utilized in attacks that have a variety of layers which help the email seem legitimate. Leveraging all the classic techniques seen in a supply chain attack to disguise the sender's intent, taking advantage of the users' inherent trust in familiar sources, the user is more likely to lower their defenses.

For more information: https://darktrace.com/products/email/use-cases/supply-chain-attack

In terms of the URL, if the payload is malicious, why is it difficult for email security solutions to catch it? Primarily, other security vendors will focus on the payload in isolation, attempting to find known attack patterns or signatures such as a domain name or IP with a bad reputation. Unfortunately, with this technique, if the URL has a legitimate domain, it will return a clean track record. Common obfuscation techniques such as captchas, short-links, and click throughs can all be deployed to add layers of complexity to the analysis.

Safelink Smuggling relies heavily on link redirects, which means that web analysis tools will falter as they will only analyze the first redirect. Consequently, when more in-depth analysis on the link itself is performed, the first place the URL takes the user is not the malicious site but rather the default on-click analysis of the vendor in question. Therefore, any traditional browser or link analysis will also return a negative result.

Finally, the context itself is important. In contrast to traditional email security solutions, Darktrace / EMAIL asks who, what, when, where, and why for every single email, and compares it to the pattern of life of both the internal recipient and the external sender, rather than attempting to match patterns with historical threat data. When analyzing an email from an inbound perspective, Darktrace reveals potential deviations from normal, that, when considered sufficiently anomalous, will result in taking a proportional action to the threat assessed.

To illustrate the above, let’s take a look at an example email that Darktrace recently caught.

The following is an email a Darktrace customer received, which Darktrace / EMAIL held before it reached the inbox. In this case, the smuggled Safelink was further obfuscated behind a QR Code. The accompanying document also presented some anomalies in terms of its intent, perceived as a potential social engineering attempt. Finally, the lack of association and low mailing history meant there was no prior context for this email.  

Example of a Safelink Smuggling attack using a popular email security solution’s safelink.
Fig 1: Example of a Safelink Smuggling attack using a popular email security solution’s safelink.

How to mitigate against Safelink Smuggling?

It's difficult for email security vendors to do anything about their links being reused, and reuse should almost be expected by popular operators in the email security space. Therefore, the presence of links from a vendor’s domain in a suspicious email communication rarely indicates a compromise of the link rewrite infrastructure or a compromise of the third-party vendor.

Email security vendors can improve their defense-in-depth, especially around their email provider accounts to avoid Method 1 (Compromised Account attacks) and become more selective with their rewrites to curtail Method 2 (Reply Chain attacks).

Primary protection against Safelink Smuggling should be offered by the email security vendor responsible for inbound email analysis. They need to ensure that techniques such as Safelink Smuggling are not evaded by their detection mechanisms.

Darktrace has long been working on the betterment of security within the email community and innovating our link analysis infrastructure to mitigate against this attack methodology (read more about our major update in 6.2 here), regardless of whether the receiving organization are Darktrace customers.

How does Darktrace deal with Safelink Smuggling today?

Darktrace has been dealing with Safelink Smuggling since launch and has a standardized recommendation for customers who are looking to defend against this threat.

Customers want to avoid being 1) the propagators of this threat and potentially damaging their brand reputation, and 2) being victims of the supply chain attack thereafter.

The principal recommendation to protect customer accounts and consequently their brands is to ensure defense-in-depth. As accounts establish themselves as the crown jewels of any modern enterprise, organizations should vigilantly monitor their account activity with the same rigor they would analyze their network activity. Whether that is through the base account takeover protection offered by Darktrace / EMAIL, or the expanded defense offered by Darktrace / IDENTITY, it is crucial that the accounts themselves have a robust security solution in place.

Secondly, to avoid falling victim to the supply chain attack that leverages a third-party vendor’s link rewrite, it is imperative to use a solution that does not rely on static threat intelligence and link reputation analysis. Rather than chasing attackers by updating rules and signatures, Darktrace leverages Self-Learning AI to learn the communication patterns of both internal and external messages to reveal deviations in both content and context.

Finally, for those customers that already leverage Darktrace / EMAIL we recommend ensuring that lock links are enabled, and that the default warning page is displayed every time a link is rewritten, no matter the perceived severity of the link. This will allow any potential user that clicks on a rewritten Darktrace / EMAIL link to be alerted to the potential nature of the site they are trying to access.

Safelink smuggling example caught by Darktrace

While most cases involve other vendors, analysts recently saw a case where Darktrace's own links were used in this type of attack. A small number of links were leveraged in a campaign targeting both Darktrace and non-Darktrace customers alike. Thankfully, these attempts were all appropriately actioned by those customers that had Darktrace / EMAIL deployed.

In the example below, you will see how Darktrace Cyber AI Analyst describes the example at hand under the Anomaly Indicators section.

Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.
Fig 2: Example of Safelink Smuggling attack on Darktrace using the Darktrace Safelink Infrastructure.

First, the display name mismatch can be interpreted as an indicator of social engineering, attempting to deceive the recipient with an IT policy change.

Second, the link itself, which in this case is a hidden redirect to an unusual host for this environment.

Finally, there is a suspected account takeover due to the origin of the email being a long-standing, validated domain that contains a wide variety of suspicious elements.

Darktrace / EMAIL would have held this email from being delivered.

Conclusion

By investigating Safelink Smuggling, Darktrace wants to shine a light on the technique for security teams and help raise awareness of how it can be used to dupe users into lowering their defenses. Challenge your email security vendor on how it deals with link analysis, particularly from trusted senders and applications.

Interested in Darktrace’s approach to defense-in-depth? Check out Darktrace / EMAIL

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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman

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January 7, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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AI

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January 5, 2026

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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