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August 7, 2023

Detection of an Evasive Credential Harvester | IPFS Phishing

Discover the emerging trend of malicious actors abusing the Interplanetary File System (IPFS) file storage protocol in phishing campaigns. Learn more here!
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
Lena Yu
Cyber Security Analyst
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07
Aug 2023

IPFS Phishing Attacks

Phishing attacks continue to be one of the most common methods of infiltration utilized by threat actors and they represent a significant threat to an organization’s digital estate. As phishing campaigns typically leverage social engineering methods to evade security tools and manipulate users into following links, downloading files, or divulging confidential information. It is a relatively low effort but high-yield type of cyber-attack.

That said, in recent years security teams have become increasingly savvy to these efforts. Attackers are having to adapt and come up with novel ways to carry out their phishing campaigns. Recently, Darktrace has observed a rise in phishing attacks attempting to abuse the InterPlanetary File System (IPFS) in campaigns that are able to dynamically adapt depending on the target, making it extremely difficult for security vendors to detect and investigate.

What is a IPFS?

IPFS is a file storage protocol a peer-to-peer (P2P) network used for storing and sharing resources in a distributed file system [1]. It is also a file storage system similar in nature to other centralized file storage services like Dropbox and Google Drive.

File storage systems, like IPFS, are often abused by malicious actors, as they allow attackers to easily host their own content without maintaining infrastructure themselves. However, as these file storage systems often have legitimate usages, blocking everything related to file storages may cause unwanted problems and affect normal business operations. Thus, the challenge lies in differentiating between legitimate and malicious usage.

While centralized, web-based file storage services use a Client-Server model and typically deliver files over HTTP, IPFS uses a Peer-to-Peer model for storing and sharing files, as shown in Figure 1.

Figure 1: (a) shows the Client-Server model that centralized, web-based file storage services use. The resource is available on the server, and the clients access the resource from the server. (b) shows the Peer-to-Peer model that IPFS use. The resources are available on the peers.

To verify the authenticity and integrity of files, IPFS utilizes cryptographic hashes.

A cryptographic hash value is generated using a file’s content upon upload to IPFS. This is used to generate the Content Identifier (CID). IPFS uses Content Addressing as opposed to Location Addressing, and this CID is used to point to a resource in IPFS [4].

When a computer running IPFS requires a particular file, it asks the connected peers if they have the file with a specific hash. If a peer has the file with the matching hash, it will provide it to the requesting computer [1][6].

Taking down content on IPFS is much more difficult compared to centralized file storage hosts, as content is stored on several nodes without a centralized entity, as shown in Figure 2. To take down content from IPFS, it must be removed from all the nodes. Thus, IPFS is prone to being abused for malicious purposes.

Figure 2: When the resource is unavailable on the server for (a), all the clients are unable to access the resource. When the resource is unavailable on one of the peers for (b), the resources are still available on the other peers.

The domains used in these IPFS phishing links are gateways that enable an HTTPS URL to access resources within the distributed IPFS file system.

There are two types of IPFS links, the Path Gateway and Subdomain Gateway [1].

Path Gateways have a fixed domain/host and identifies the IPFS resource through a resource-identifying string in the path. The Path Gateway has the following structure:

•       https://<gateway-host>.tld/ipfs/<CID>/path/to/resource

•       https://<gateway-host>.tld/ipns/<dnslink/ipnsid>/path/to/resource

On the other hand, Subdomain Gateways have a resource-identifying string in the subdomain. Subdomain Gateways have the following structure:

•       https://<cidv1b32>.ipfs.<gateway-host>.tld/path/to/resource

One gateway domain serves the same role as any other, which means attackers can easily change the gateways that are used.

Thus, these link domains involved in these attacks can be much more variable than the ones in traditional file storage attacks, where a centralized service with a single domain is used (e.g., Dropbox, Google Docs), making detecting the malicious use of IPFS extremely challenging for traditional security vendors. Through its anomaly-based approach to threat detection, Darktrace/Email™ is consistently able to identify such tactics and respond to them, preventing malicious actors from abusing file storage systems life IPFS.

IPFS Campaign Details

In several recent examples of IPFS abuse that Darktrace detected on a customer’s network, the apparent end goal was to harvest user credentials. Stolen credentials can be exploited by threat actors to further their attacks on organizations by escalating their privileges within the network, or even sold on the dark web.

Darktrace detected multiple IPFS links sent in malicious emails that contained the victim’s email address. Based on the domain in this email address, users would then be redirected to a fake login page that uses their organizations’ webpage visuals and branding to convince targets to enter their login details, unknowingly compromising their accounts in the process.

Figure 3: The credential harvester changes visuals depending on the victim’s email address specified in the URL.

These IPFS credential harvesting sites use various techniques to evade detection the detection of traditional security tools and prevent further analysis, such as obfuscation by Percent Encoding and Base64 Encoding the code.

There are also other mechanisms put into place to hinder investigation by security teams. For example, some IPFS credential harvester sites investigated by Darktrace did not allow right clicking and certain keystrokes, as a means to make post-attack analysis more difficult.

Figure 4: The code shows that it attempts to prevent certain keystrokes.

In the campaign highlighted in this blog, the following IPFS link was observed:

hxxps://ipfs[.]io/ipfs/QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP?filename=at ob.html#<EmailAddress>

This uses a Path Gateway, as it identifies the IPFS resource through a resource-identifying string in the path. The CID is QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP in this case.

It makes a GET request to image[.]thum[.]io and logo[.]clearbit[.]com as shown in Figure 5. The image[.]thum[.]io is a Free Website Screenshot Generator, that provides real-time screenshot of websites [2]. The logo[.]clearbit[.]com is used to lookup company logos using the domain [3]. These visuals are integrated into the credential harvester site. Figure 6 shows the domain name being extracted from the victim’s email address and used to obtain the visuals.

Figure 5: The GET requests to image[.]thum[.]io and logo[.]clearbit[.].
Figure 6: The code shows that it utilizes the domain name from the victim’s email address to obtain the visuals from logo.clearbit[.]com and image[.]thum.io.

The code reveals the credential POST endpoint as shown in Figure 16. When credentials are submitted, it makes a POST request to this endpoint as shown in Figure 7.

Figure 7: The credential POST endpoint can be seen inside the code.
Figure 8: The Outlook credential harvester will redirect to the real Outlook page when wrong credentials are submitted multiple times.

From the IPFS link alone, it is difficult to determine whether it leads to a malicious endpoint, however Darktrace has consistently identified emails containing these IPFS credential harvesting links as phishing attempts.

Darktrace Coverage

During one case of IPFS abuse detected by Darktrace in March 2023, a threat actor sent malicious emails with the subject “Renew Your E-mail Password” to 55 different recipients at. The sender appeared to be the organization’s administrator and used their internal domain.

Figure 9: Darktrace/Email’s detection of the “Renew Your E-mail Password” emails from “administrator”. These were all sent at 2023.03.21 02:39 UTC.

However, Darktrace recognized that the email did not pass Sender Policy Framework (SPF), and therefore it could not be validated as being sent from the organization’s domain. Darktrace also detected that the email contained a link to “ipfs.io, the official IPFS gateway. This was identified as a spoofing and phishing attempt by Darktrace/Email.

Figure 10: The Darktrace/Email overview tab shows the Anomaly Indicators, History, Association, and Validation information of this sender. It contained a link to “ipfs.io”, and did not pass SPF.

Following the successful identification of the malicious emails, Darktrace RESPOND™ took immediate autonomous action to prevent them from leading to potentially damaging network compromise. For email-based threats, Darktrace RESPOND is able to carry out numerous actions to stop malicious emails and reduce the risk of compromise. In response to this specific incident, RESPOND took multiple preventative actions (as seen in Figure 11), including include lock link, an action that prevents access to URLs deemed as suspicious, send to junk, an action that automatically places emails in the recipient’s junk folder, and hold message, the most severe RESPOND action that prevents malicious emails from reaching the recipients inbox at all.

Figure 11: The Darktrace/Email model tab shows all the models that triggered on the email and the associated RESPOND actions.
Figure 12: The ipfs.io link used in this email contains the recipient’s email address, and has a CID of QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP. It has a Darktrace Domain Rarity Score of 100
Figure 13: The IPFS credential harvester that uses the organization’s website’s visuals.

Further investigation revealed that the IPFS link contained the recipients’ email address, and when clicked led to a credential harvester that utilized the same visuals and branding as the customer’s website.

Concluding Thoughts

Ultimately, despite the various tactics employed threat actors to evade the detection of traditional security tools, Darktrace was able to successfully detect and mitigate these often very fruitful phishing attacks that attempted to abuse the IPFS file storage system.

As file storage platforms like IPFS do have legitimate business uses, blocking traffic related to file storage is likely to negatively impact the day-to-day operations of an organization. The challenge security teams face is to differentiate between malicious and legitimate uses of such services, and only act on malicious cases. As such, it is more important than ever for organizations to have an effective anomaly detection tool in place that is able to identify emerging threats without relying on rules, signatures or previously observed indicators of compromise (IoC).

By leveraging its Self-Learning AI, Darktrace understands what represents expected activity on customer networks and can recognize subtle deviations from expected behavior, that may be indicative of compromise. Then, using its autonomous response capabilities, Darktrace RESPOND is able to instantly and autonomously take action against emerging threats to stop them at the earliest possible stage.

Credit to Ben Atkins, Senior Model Developer for their contribution to this blog.

Appendices

Example IOCs

Type: URL

IOC: hxxps://ipfs[.]io/ipfs/QmfDDxLWoLi qFURX6dUZcsHxVBP1ZnM21H5jXGs

1ffNxtP?filename=atob.html#<Email Address>

Description: Path Gateway link

Type: URL

IOC: hxxps://bafybeibisyerwlu46re6rxrfw doo2ubvucw7yu6zjcfjmn7rqbwcix2 mku.ipfs[.]dweb.link/webn cpmk.htm?bafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vv gbdaybafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vvgbda y#<EmailAddress>

Description: Subdomain Gateway link

Relevant Darktrace DETECT Models

•       Spoof / Internal Domain from Unexpected Source + New Unknown Link

•       Link / High Risk Link + Low Sender Association

•       Link / New Correspondent Classified Link

•       Link / Watched Link Type

•       Proximity / Phishing + New activity

•       Proximity / Phishing + New Address Known Domain

•       Spoof / Internal Domain from Unexpected Source + High Risk Link

References

[1]    https://docs.ipfs.tech/

[2]    https://www.thum.io/

[3]    https://clearbit.com/logo

[4]    https://filebase.com/blog/ipfs-content-addressing-explained/

[5]    https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/the-attack-of-the-chameleon-phishing-page/

[6]    https://wiki.ipfsblox.com/

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
Lena Yu
Cyber Security Analyst

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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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