Blog
/
Network
/
September 11, 2025

SEO Poisoning and Fake PuTTY sites: Darktrace’s Investigation into the Oyster backdoor

SEO poisoning is a malicious tactic where threat actors manipulate search engine rankings to promote deceptive websites. These sites often mimic legitimate software downloads, delivering malware like the Oyster backdoor. Learn about Darktrace’s investigation into the tactics used to deliver Oyster via fake PuTTY sites and manipulate search visibility.
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
Christina Kreza
Cyber Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
11
Sep 2025

What is SEO poisoning?

Search Engine Optimization (SEO) is the legitimate marketing technique of improving the visibility of websites in organic search engine results. Businesses, publishers, and organizations use SEO to ensure their content is easily discoverable by users. Techniques may include optimizing keywords, creating backlinks, or even ensuring mobile compatibility.

SEO poisoning occurs when attackers use these same techniques for malicious purposes. Instead of improving the visibility of legitimate content, threat actors use SEO to push harmful or deceptive websites to the top of search results. This method exploits the common assumption that top-ranking results are trustworthy, leading users to click on URLs without carefully inspecting them.

As part of SEO poisoning, the attacker will first register a typo-squatted domain, slightly misspelled or otherwise deceptive versions of real software sites, such as putty[.]run or puttyy[.]org. These sites are optimized for SEO and often even backed by malicious Google ads, increasing the visibility when users search for download links. To achieve that, threat actors may embed pages with strategically chosen, high-value keywords or replicate content from reputable sources to elevate the domain’s perceived authority in search engine algorithms [4]. In more advanced operations, these tactics are reinforced with paid promotion, such as Google ads, enabling malicious domains to appear above organic search results as sponsored links. This placement not only accelerates visibility but also impacts an unwarranted sense of legitimacy to unsuspected users.

Once a user lands on one of these fake pages, they are presented with what looks like a legitimate software download option. Upon clicking the download indicator, the user will be redirected to another separate domain that actually hosts the payload. This hosting domain is usually unrelated to the nominally referenced software. These third-party sites can involve recently registered domains but may also include legitimate websites that have been recently compromised. By hosting malware on a variety of infrastructure, attackers can prolong the availability of distribution methods for these malicious files before they are taken down.

What is the Oyster backdoor?

Oyster, also known as Broomstick or CleanUpLoader, is a C++ based backdoor malware first identified in July 2023. It enables remote access to infected systems, offering features such as command-line interaction and file transfers.

Oyster has been widely adopted by various threat actors, often as an entry point for ransomware attacks. Notable examples include Vanilla Tempest and Rhysida ransomware groups, both of which have been observed leveraging the Oyster backdoor to enhance their attack capabilities. Vanilla Tempest is known for using Oyster’s stealth persistence to maintain long-term access within targeted networks, often aligning their operations with ransomware deployment [5]. Rhysida has taken this further by deploying Oyster as an initial access tool in ransomware campaigns, using it to conduct reconnaissance and move laterally before executing encryption activities [6].

Once installed, the backdoor gathers basic system information before communicating with a command-and-control (C2) server. The malware largely relies on a ‘cmd.exe’ instance to execute commands and launch other files [1].

In previous SEO poisoning cases, the file downloaded from the fake pages is not just PuTTY, but a trojanized version that includes the stealthy Oyster backdoor. PuTTY is a free and open-source terminal emulator for Windows that allows users to connect to remote servers and devices using protocols like SSH and Telnet. In the recent campaign, once a user visits the fake software download site, ranked highly through SEO poisoning, the malicious payload is downloaded through direct user interaction and subsequently installed on the local device, initiating the compromise. The malware then performs two actions simultaneously: it installs a fully functional version of PuTTY to avoid user suspicion, while silently deploying the Oyster backdoor. Given PuTTY’s nature, it is prominently used by IT administrators with highly privileged account as opposed to standard users in a business, possibly narrowing the scope of the targets.

Oyster’s persistence mechanism involves creating a Windows Scheduled Task that runs every few minutes. Notably, the infection uses Dynamic Link Library (DLL) side loading, where a malicious DLL, often named ‘twain_96.dll’, is executed via the legitimate Windows utility ‘rundll32.exe’, which is commonly used to run DLLs [2]. This technique is frequently used by malicious actors to blend their activity with normal system operations.

Darktrace’s Coverage of the Oyster Backdoor

In June 2025, security analysts at Darktrace identified a campaign leveraging search engine manipulation to deliver malware masquerading as the popular SSH client, PuTTY. Darktrace / NETWORK’s anomaly-based detection identified signs of malicious activity, and when properly configured, its Autonomous Response capability swiftly shut down the threat before it could escalate into a more disruptive attack. Subsequent analysis by Darktrace’s Threat Research team revealed that the payload was a variant of the Oyster backdoor.

The first indicators of an emerging Oyster SEO campaign typically appeared when user devices navigated to a typosquatted domain, such as putty[.]run or putty app[.]naymin[.]com, via a TLS/SSL connection.

Figure 1: Darktrace’s detection of a device connecting to the typosquatted domain putty[.]run.

The device would then initiate a connection to a secondary domain that hosts the malicious installer, likely triggered by user interaction with redirect elements on the landing page. This secondary site may not have any immediate connection to PuTTY itself but is instead a hijacked blog, a file-sharing service, or a legitimate-looking content delivery subdomain.

Figure 2: Darktrace’s detection of the device making subsequent connections to the payload domain.

Following installation, multiple affected devices were observed attempting outbound connectivity to rare external IP addresses, specifically requesting the ‘/secure’ endpoint as noted within the declared URIs. After the initial callback, the malware continued communicating with additional infrastructure, maintaining its foothold and likely waiting for tasking instructions. Communication patterns included:

·       Endpoints with URIs /api/kcehc and /api/jgfnsfnuefcnegfnehjbfncejfh

·       Endpoints with URI /reg and user agent “WordPressAgent”, “FingerPrint” or “FingerPrintpersistent”

This tactic has been consistently linked to the Oyster backdoor, which has shown similar URI patterns across multiple campaigns [3].

Darktrace analysts also noted the sophisticated use of spoofed user agent strings across multiple investigated customer networks. These headers, which are typically used to identify the application making an HTTP request, are carefully crafted to appear benign or mimic legitimate software. One common example seen in the campaign is the user agent string “WordPressAgent”. While this string references a legitimate web application or plugin, it does not appear to correspond to any known WordPress services or APIs. Its inclusion is most likely designed to mimic background web traffic commonly associated with WordPress-based content management systems.

Figure 3: Cyber AI Analyst investigation linking the HTTP C2 activity.

Case-Specific Observations

While the previous section focused on tactics and techniques common across observed Oyster infections, a closer examination reveals notable variations and unique elements in specific cases. These distinct features offer valuable insights into the diverse operational approaches employed by threat actors. These distinct features, from unusual user agent strings to atypical network behavior, offer valuable insights into the diverse operational approaches employed by the threat actors. Crucially, the divergence in post-exploitation activity reflects a broader trend in the use of widely available malware families like Oyster as flexible entry points, rather than fixed tools with a single purpose. This modular use of the backdoor reflects the growing Malware-as-a-Service (MaaS) ecosystem, where a single initial infection can be repurposed depending on the operator’s goals.

From Infection to Data Egress

In one observed incident, Darktrace observed an infected device downloading a ZIP file named ‘host[.]zip’ via curl from the URI path /333/host[.]zip, following the standard payload delivery chain. This file likely contained additional tools or payloads intended to expand the attacker’s capabilities within the compromised environment. Shortly afterwards, the device exhibited indicators of probable data exfiltration, with outbound HTTP POST requests featuring the URI pattern: /upload?dir=NAME_FOLDER/KEY_KEY_KEY/redacted/c/users/public.

This format suggests the malware was actively engaged in local host data staging and attempting to transmit files from the target machine. The affected device, identified as a laptop, aligns with the expected target profile in SEO poisoning scenarios, where unsuspecting end users download and execute trojanized software.

Irregular RDP Activity and Scanning Behavior

Several instances within the campaign revealed anomalous or unexpected Remote Desktop Protocol (RDP) sessions occurring shortly after DNS requests to fake PuTTY domains. Unusual RDP connections frequently followed communication with Oyster backdoor C2 servers. Additionally, Darktrace detected patterns of RDP scanning, suggesting the attackers were actively probing for accessible systems within the network. This behavior indicates a move beyond initial compromise toward lateral movement and privilege escalation, common objectives once persistence is established.

The presence of unauthorized and administrative RDP sessions following Oyster infections aligns with the malware’s historical role as a gateway for broader impact. In previous campaigns, Oyster has often been leveraged to enable credential theft, lateral movement, and ultimately ransomware deployment. The observed RDP activity in this case suggests a similar progression, where the backdoor is not the final objective but rather a means to expand access and establish control over the target environment.

Cryptic User Agent Strings?

In multiple investigated cases, the user agent string identified in these connections featured formatting that appeared nonsensical or cryptic. One such string containing seemingly random Chinese-language characters translated into an unusual phrase: “Weihe river is where the water and river flow.” Legitimate software would not typically use such wording, suggesting that the string was intended as a symbolic marker rather than a technical necessity. Whether meant as a calling card or deliberately crafted to frame attribution, its presence highlights how subtle linguistic cues can complicate analysis.

Figure 4: Darktrace’s detection of malicious connections using a user agent with randomized Chinese-language formatting.

Strategic Implications

What makes this campaign particularly noteworthy is not simply the use of Oyster, but its delivery mechanism. SEO poisoning has traditionally been associated with cybercriminal operations focused on opportunistic gains, such as credential theft and fraud. Its strength lies in casting a wide net, luring unsuspecting users searching for popular software and tricking them into downloading malicious binaries. Unlike other campaigns, SEO poisoning is inherently indiscriminate, given that the attacker cannot control exactly who lands on their poisoned search results. However, in this case, the use of PuTTY as the luring mechanism possibly indicates a narrowed scope - targeting IT administrators and accounts with high privileges due to the nature of PuTTY’s functionalities.

This raises important implications when considered alongside Oyster. As a backdoor often linked to ransomware operations and persistent access frameworks, Oyster is far more valuable as an entry point into corporate or government networks than small-scale cybercrime. The presence of this malware in an SEO-driven delivery chain suggests a potential convergence between traditional cybercriminal delivery tactics and objectives often associated with more sophisticated attackers. If actors with state-sponsored or strategic objectives are indeed experimenting with SEO poisoning, it could signal a broadening of their targeting approaches. This trend aligns with the growing prominence of MaaS and the role of initial access brokers in today’s cybercrime ecosystem.

Whether the operators seek financial extortion through ransomware or longer-term espionage campaigns, the use of such techniques blurs the traditional distinctions. What looks like a mass-market infection vector might, in practice, be seeding footholds for high-value strategic intrusions.

Credit to Christina Kreza (Cyber Analyst) and Adam Potter (Senior Cyber Analyst)

Appendices

MITRE ATT&CK Mapping

·       T1071.001 – Command and Control – Web Protocols

·       T1008 – Command and Control – Fallback Channels

·       T0885 – Command and Control – Commonly Used Port

·       T1571 – Command and Control – Non-Standard Port

·       T1176 – Persistence – Browser Extensions

·       T1189 – Initial Access – Drive-by Compromise

·       T1566.002 – Initial Access – Spearphishing Link

·       T1574.001 – Persistence – DLL

Indicators of Compromise (IoCs)

·       85.239.52[.]99 – IP address

·       194.213.18[.]89/reg – IP address / URI

·       185.28.119[.]113/secure – IP address / URI

·       185.196.8[.]217 – IP address

·       185.208.158[.]119 – IP address

·       putty[.]run – Endpoint

·       putty-app[.]naymin[.]com – Endpoint

·       /api/jgfnsfnuefcnegfnehjbfncejfh

·       /api/kcehc

Darktrace Model Detections

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / Posting HTTP to IP Without Hostname

·       Compromise / HTTP Beaconing to Rare Destination

·       Compromise / Large Number of Suspicious Failed Connections

·       Compromise / Beaconing Activity to External Rare

·       Compromise / Quick and Regular Windows HTTP Beaconing

·       Device / Large Number of Model Alerts

·       Device / Initial Attack Chain Activity

·       Device / Suspicious Domain

·       Device / New User Agent

·       Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

·       Antigena / Network / External Threat / Antigena Suspicious Activity Block

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

References

[1] https://malpedia.caad.fkie.fraunhofer.de/details/win.broomstick

[2] https://arcticwolf.com/resources/blog/malvertising-campaign-delivers-oyster-broomstick-backdoor-via-seo-poisoning-trojanized-tools/

[3] https://hunt.io/blog/oysters-trail-resurgence-infrastructure-ransomware-cybercrime

[4] https://www.crowdstrike.com/en-us/cybersecurity-101/social-engineering/seo-poisoning/

[5] https://blackpointcyber.com/blog/vanilla-tempest-oyster-backdoor-netsupport-unknown-infostealers-soc-incidents-blackpoint-apg/

[6] https://areteir.com/article/rhysida-using-oyster-backdoor-in-attacks/

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
Christina Kreza
Cyber Analyst

More in this series

No items found.

Blog

/

Network

/

May 22, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

garnter ndr magic quadrantDefault blog imageDefault blog image

Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

[related-resource]

Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

Continue reading
About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

Blog

/

/

May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

prompt securityDefault blog imageDefault blog image

How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

[related-resource]

Continue reading
About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
Elevate your network security with Darktrace AI