Hackers Could Weaponize GGUF Models to Achieve RCE on SGLang Inference Servers

Cybersecuritynews •Abinaya • April 21, 2026

A critical vulnerability in the SGLang inference server that allows threat actors to execute arbitrary code. Tracked as CVE-2026-5760, this flaw allows hackers to weaponize standard GGUF machine learning models to compromise the underlying servers that host them. As enterprise artificial intelligence deployments grow, this discovery highlights the severe infrastructure risks posed by loading untrusted […]

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This article is one source in a clustered incident — the cluster page carries the summary, timeline and every other outlet covering it.

Critical RCE Vulnerability Discovered in SGLang Framework via GGUF Models A remote code execution vulnerability has been identified in the SGLang framework, specifically affecting the reranking endpoint (/v1/rerank) and tracked as CVE-2026-5760. This flaw allows attackers to exploit maliciously crafted GGUF model files containing Jinja2 templates, leading to the execution of arbitrary Python code on the server. The vulnerability arises from the unsandboxed use of jinja2.Environment() in the rendering process of chat templates. Successful exploitation could result in host compromise, lateral movement, data exfiltration, or denial-of-service attacks. The issue affects deployments of SGLang that expose the vulnerable interface to untrusted networks. Despite attempts to coordinate with project maintainers, no response or patch has been issued. The severity of this vulnerability is underscored by its potential impact on enterprise AI deployments. Security researchers recommend using ImmutableSandboxedEnvironment to mitigate the risk of exploitation. 7 sources 70

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Hackers Could Weaponize GGUF Models to Achieve RCE on SGLang Inference Servers

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