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CRITICAL FLAW IN REPLICATE AI PLATFORM EXPOSES PROPRIETARY DATA

The finding underscores the challenges of protecting data from multiple
customers across AI-as-a-service solutions, especially in environments that run
AI models from untrusted sources.

Elizabeth Montalbano, Contributing Writer

May 23, 2024

4 Min Read
Source: Ole CNX via Shutterstock


A critical vulnerability in the Replicate AI platform could have allowed
attackers to execute a malicious AI model within the platform for a cross-tenant
attack — allowing access to the private AI models of customers and potentially
exposing proprietary knowledge or sensitive data.

Researchers at Wiz discovered the flaw as part of a series of partnerships with
AI-as-a-service providers to investigate the security of their platforms. The
discovery of the flaw demonstrates the difficulty of tenant separation across
AI-as-a-service solutions, especially in environments that run AI models from
untrusted sources.



"Exploitation of this vulnerability would have allowed unauthorized access to
the AI prompts and results of all Replicate's platform customers," and
potentially alter those results, Wiz's Shir Tamari and Sagi Tzadik wrote in a
blog post published today. Previously, Wiz researchers found flaws that led to a
similar outcome in the HuggingFace AI platform.

"As we saw in the results of our work with Hugging Face and now in Replicate,
two leading AI-as-a-service providers, when running AI models in cloud
environments, it is crucial to remember that AI models are actually code," Ami
Luttwak, Wiz CTO and co-founder, tells Dark Reading. "Like all code, the origin
must be verified, and content-scanned for malicious payloads."



Indeed, the flaw presents an immediate threat to AI-as-a-service providers, who
often allow their customers to execute untrusted code in the form of AI models
in shared environments - where there is other customers' data. It also can
impact AI teams, who could be affected when they adopt AI models from untrusted
sources and run them on their workstation or company servers, the researchers
noted.



Wiz Research responsibly disclosed the vulnerability to AI model-sharing vendor
Replicate in January 2023; the company promptly mitigated the flaw so that no
customer data was compromised. At this time, no further action is required by
customers.


EXPLOITING THE FLAW

The flaw lies in achieving remote code execution on Replicate's platform by
creating a malicious container in the Cog format, which is a proprietary format
used to containerize models on Replicate. After containerizing a model using
Cog, users can upload the resulting image to Replicate's platform and start
interacting with it.

Wiz researchers created a malicious Cog container and uploaded it to the
platform and then, with root privileges, used it to execute code on the
Replicate infrastructure.  



"We suspect this code-execution technique is a pattern, where companies and
organizations run AI models from untrusted sources, even though these models are
code that could potentially be malicious," the researchers wrote in the post. A
similar technique was used to exploit flaws found on the HuggingFace platform.

This exploitation allowed the researchers to investigate the environment move
laterally out and ultimately outside of the node on which they were running,
which was inside a Kubernetes cluster hosted on Google Cloud Platform. Though
the process was challenging, they eventually were able to conduct a cross-tenant
attack that allowed them to query other models and even modify the output of
those models.

"The exploitation of this vulnerability would have posed significant risks to
both the Replicate platform and its users," the researchers wrote. "An attacker
could have queried the private AI models of customers, potentially exposing
proprietary knowledge or sensitive data involved in the model training process.
Additionally, intercepting prompts could have exposed sensitive data, including
personally identifiable information (PII)."



Indeed, this ability to alter prompts and responses of an AI model poses a
severe threat to the functionality of AI applications, giving attackers a way to
manipulate AI behavior and compromise the decision-making processes of these
models.

"Such actions directly threaten the accuracy and reliability of AI-driven
outputs, undermining the integrity of automated decisions and potentially having
far-reaching consequences for users dependent on the compromised models," the
researchers wrote.


NEW FORMS OF MITIGATION REQUIRED

Currently there is no easy way to validate a model's authenticity, or to scan it
for threats, so malicious AI models present a new attack surface for defenders
that needs other forms of mitigation, Luttwak says.

The best way to do this is to ensure that production workloads only use AI
models in secure formats, like so-called safetensors. "We recommend that
security teams monitor for usage of unsafe models and work with their AI teams
to transition into safetensors or similar formats," he says.

Using only safe AI formats can the attack surface "dramatically," as "these
formats are designed to prevent attackers from taking over the AI model
instance," Luttwak says.  

Further, cloud providers who run their customers' models in a shared environment
should enforce tenant-isolation practices to ensure that a potential attacker
who managed to execute a malicious model cannot access the data of other
customers or the service itself, he adds.




ABOUT THE AUTHOR(S)

Elizabeth Montalbano, Contributing Writer



Elizabeth Montalbano is a freelance writer, journalist, and therapeutic writing
mentor with more than 25 years of professional experience. Her areas of
expertise include technology, business, and culture. Elizabeth previously lived
and worked as a full-time journalist in Phoenix, San Francisco, and New York
City; she currently resides in a village on the southwest coast of Portugal. In
her free time, she enjoys surfing, hiking with her dogs, traveling, playing
music, yoga, and cooking.


See more from Elizabeth Montalbano, Contributing Writer
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