Data has always been valuable, but AI has sent its worth skyrocketing.
In the last few years, AI has dramatically boosted data’s value, and here’s why:
First, AI condenses thousands of hours of expensive computation into a single small weights file. This tiny file is worth hundreds of millions of dollars in training costs. If someone steals it, they’re effectively robbing you blind.
Second, AI models constantly handle sensitive user and business data, making critical decisions and predictions. If this data leaks, the consequences could be devastating.
The scary part? While most cloud-native workflows attempt to be secure, large-scale AI training setups utilizing thousands of GPUs often have minimal security because they need to focus on improving their performance.
Simply put, the security for AI workloads hasn’t kept pace with their importance. AI isn’t just another application; it needs special protections.
The Hidden Vulnerability in GPU Memory Sharing
GPUs seem secure enough at first glance since they usually isolate each program’s memory. But AI workloads often require programs to share data directly, and this is where things get risky.
To speed up the process, AI workloads utilize Inter-Process Communication (IPC). IPC “pins” memory in place and creates a special address, stored as a File Descriptor (FD), from which other processes can directly read.
Here’s the problem: if a malicious process grabs this FD, it has full access to your data. Sure, frameworks like NVIDIA’s NCCL try to protect these FDs, but they aren’t foolproof.
The Critical Security Gap
And it gets worse: on Linux, any program with the same user ID as the FD creator can read the FD directly from /proc/(pid)/fd. No hacking skills are needed. No privilege escalation is required. Just one directory access and everything you’ve poured into your AI workload—money, time, and sensitive data—is instantly exposed.
Your AI data is at risk now, and the danger is much closer than you think.