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Home » RCE Exploit via Malicious GGUF Files in SGLang CVE-2026-5760
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RCE Exploit via Malicious GGUF Files in SGLang CVE-2026-5760

Staff WriterBy Staff WriterApril 20, 2026No Comments3 Mins Read1 Views
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Fast Facts

  1. Attackers can exploit CVE-2026-5760 by using malicious GGUF model files with embedded Jinja2 SSTI payloads, leading to remote code execution on SGLang servers.
  2. The vulnerability allows remote attackers to execute arbitrary Python code through the "/v1/rerank" endpoint when loading compromised models from sources like Hugging Face.
  3. Mitigation involves replacing jinja2.Environment() with ImmutableSandboxedEnvironment to prevent server-side Python code execution.

Threat, Attack Techniques, and Targets

A critical security vulnerability, CVE-2026-5760, affects SGLang, a popular open-source framework for large language models. The CVSS score is 9.8, indicating a high severity. Attackers can exploit this flaw by creating a malicious GGUF model file, which contains a damaging tokenizer.chat_template parameter. This parameter includes a Jinja2 server-side template injection (SSTI) payload with a trigger phrase.

The attacker then tricks the victim into downloading and loading this malicious model, often from sources like Hugging Face. When a request reaches the “/v1/rerank” endpoint, SGLang renders the chat_template using jinja2.Environment(). Since this environment does not use sandboxing by default, the SSTI payload executes arbitrary Python code on the server. This results in remote code execution (RCE), which allows the attacker to gain control of the server.

Targets of this attack mainly include systems running SGLang servers exposed to user requests, such as API endpoints used in large language model applications. These systems are especially vulnerable when they process models supplied from untrusted sources.

Impact, Security Implications, and Remediation Guidance

This vulnerability can have serious consequences. Successful exploitation allows an attacker to execute any code on the SGLang server remotely. This could lead to data theft, server control, or disruption of services. Because the flaw resides in how user-supplied models are processed, it presents a significant security risk to affected systems.

The main security concern is the execution of arbitrary Python code caused by the SSTI payload in the malicious GGUF model file. This could enable attackers to take full control of the server, compromise data, and disrupt operations.

To mitigate this vulnerability, it is recommended to replace jinja2.Environment() with ImmutableSandboxedEnvironment when rendering chat templates. This change prevents the execution of harmful code and reduces the attack surface. No patch or response was available at the time of the advisory. Organizations should consult the official sources or the vendor for further remediation guidance and updates.

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John Marcelli is a staff writer for the CISO Brief, with a passion for exploring and writing about the ever-evolving world of technology. From emerging trends to in-depth reviews of the latest gadgets, John stays at the forefront of innovation, delivering engaging content that informs and inspires readers. When he's not writing, he enjoys experimenting with new tech tools and diving into the digital landscape.

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