Generative AI is set to transform the way healthcare organisations work, just like virtually every other industry, by increasing productivity and automating human tasks.
Unfortunately, tools such as ChatGPT can also help attackers to create exploits and exfiltrate sensitive data out of healthcare organisations, in the same way that we demonstrated it facilitating an OT exploitation.
The dangers of insecure protocols in the Healthcare industry
Threat actors typically obtain this data from IT databases, but there’s another source of patient data that is less often explored: medical devices.
In 2020, Forescout showed how insecure protocols used to communicate data between medical devices allow attackers to obtain PHI directly from sniffed network traffic.
Below, Forescout explores how to accomplish the same kind of attack using AI assistance. The advantages of AI in this case are that the attacker does not need to understand the protocols being used (often proprietary or very different from typical IT protocols) and the increased speed of development to obtain the targeted data.
AI-assisted cyberattack examples
The figure below shows sensitive data transmitted in clear text on some healthcare networks via three specific protocols: POCT01, LIS02 – both used by point-of-care testing and laboratory devices – and a proprietary protocol used by BD Pyxis MedStation medication dispensing systems.
The data observed includes patient names, dates of birth, test results and prescribed medications. This traffic was obtained from real healthcare networks; because of its sensitive nature, personal information has been partially redacted.
Observing the traffic, it’s easy for a human to spot the sensitive data. To extract this data in bulk for later sale on a black market, however, an attacker would need to write a parser for these protocols that extracts only the interesting data in a suitable format. These protocols are not complex, per se, but they are uncommon, so there’s no parser embedded in Wireshark, for instance.
To reach the goal of extracting the sensitive data, Forescout instructed ChatGPT to create three separate parsers, one for each protocol.
In all the examples below, information such as patient names, dates of birth and test orders has been changed from the figures above so we don’t need to redact all the images. The structure of the messages remained unchanged.
First, Forescout leveraged ChatGPT to extract patient IDs, names and birth dates from POCT01 messages.
This was an easy task because the protocol follows a standard XML format, so the generated script uses existing libraries to do the job.
The resulting script is shown below. Forescout could use the script without any modifications, although it returns an exception when there are patients without names or birth date information.
