Black Hat Asia 2026 | Exploiting Message Queue Flaws in AI Inference Servers for Widespread RCE

AI inference servers have become critical infrastructure for deploying large language models at scale, with platforms like vLLM, SGLang, TensorRT-LLM, and others processing millions of enterprise AI workloads daily. These systems rely on inter-process communication channels to coordinate distributed model inference across GPU clusters, but we discovered that the standard architectural patterns used across the industry contain fundamental security flaws.

We identified multiple Remote Code Execution vulnerabilities in the message queue implementations used by all major open-source AI inference platforms, affecting deployments from Meta, NVIDIA, Microsoft, and the PyTorch ecosystem. Through our internet-wide scanning, we confirmed thousands of vulnerable instances are directly exposed online, creating an attacker-accessible pathway into AI infrastructure at organizations worldwide. The vulnerabilities stem from dangerous code reuse patterns where insecure “reference implementations” were copied across projects, propagating the same logical flaws throughout the AI supply chain.

In this Briefing, we will reveal the complete technical details and working exploits for these RCE vulnerabilities, demonstrate attacks against multiple platforms, and share our complex coordinated disclosure process across numerous vendors. We’ll show how attackers can gain code execution on AI servers, potentially stealing models worth millions in training costs, poisoning inference results, or pivoting into broader enterprise networks. This research exposes critical lessons about security versus performance trade-offs in AI systems and the unique risks of rapid open-source code sharing in the AI community.

Avi Lumelsky | Senior AI Researcher, Oligo Security
Uri Katz | Senior Vulnerability Researcher, Oligo Security
Gal Elbaz | CTO and Co-Founder, Oligo Security

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