CVE-2026-112080 in Llama-Chinese
Summary
by MITRE • 10/19/2026
A vulnerability was found in LlamaChinese Llama-Chinese up to 6fa0fffb0dd82fe3cfaa1449ee54a5806d26ae9b. The impacted element is the function get_class_from_dynamic_module of the file examples/chat_gradio_no_merge.py of the component Adapter Config. The manipulation of the argument base_model_name_or_path results in code injection. The attack may be performed from remote. The exploit has been made public and could be used. This product utilizes a rolling release system for continuous delivery, and as such, version information for affected or updated releases is not disclosed. The project was informed of the problem early through an issue report but has not responded yet.
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Analysis
by VulDB Data Team • 10/19/2026
The identified vulnerability resides within the LlamaChinese framework, specifically affecting versions up to commit 6fa0fffb0dd82fe3cfaa1449ee54a5806d26ae9b. This security flaw is located in the get_class_from_dynamic_module function found in the examples/chat_gradio_no_merge.py file, which serves as part of the Adapter Config component. The core technical issue stems from improper validation and sanitization of user-supplied input passed via the base_model_name_or_path argument. When this parameter is manipulated by an attacker, it allows for arbitrary code injection, effectively bypassing intended security controls within the dynamic module loading mechanism. This type of flaw is characteristic of CWE-94 Improper Control of Generation of Code or CWE-502 Deserialization of Untrusted Data, depending on the specific implementation details of how the string is processed and executed by the underlying Python interpreter.
The operational impact of this vulnerability is severe due to its remote exploitable nature. An attacker can interact with the application over a network connection without requiring prior authentication or local access, provided they have knowledge of the endpoint structure. By crafting a malicious payload within the base_model_name_or_path parameter, an adversary can execute arbitrary commands on the host system running the LlamaChinese instance. This could lead to complete compromise of the underlying infrastructure, including data exfiltration, modification of model weights or configurations, and potential pivoting into internal networks if the server is connected to them. The fact that a public exploit exists significantly lowers the barrier for entry, enabling less sophisticated threat actors to leverage this weakness against vulnerable deployments.
The context of rolling release systems complicates traditional patching strategies as version identifiers are not static or easily mapped to specific security fixes. In such continuous delivery models, vulnerabilities may be present in any given build until a corrective commit is merged and deployed. Although the project maintainers were notified early via an issue report, there has been no public acknowledgment or remediation update at this time. This lack of response leaves users relying on older commits exposed to known risks without official guidance on mitigation timelines. Organizations utilizing these models must treat all recent builds as potentially vulnerable until explicit confirmation is received from the development team that the specific code path in get_class_from_dynamic_module has been secured against injection attacks.
Mitigation strategies should focus on immediate defensive measures given the absence of an official patch. Administrators running LlamaChinese instances with this component exposed to network traffic must restrict access using strict firewall rules, allowing connections only from trusted IP addresses or internal networks where possible. Input validation at the application level is critical; developers and users modifying local deployments should implement rigorous sanitization routines for the base_model_name_or_path argument, ensuring that it matches a whitelist of known safe model identifiers rather than accepting arbitrary strings. Additionally, running the service with minimal privileges can limit the damage if an injection attempt succeeds. From a threat intelligence perspective, this vulnerability aligns with ATT&CK techniques related to Command and Scripting Interpreter abuse, specifically T1059 or its variants depending on whether shell commands are executed directly through Python's eval or exec functions. Continuous monitoring of logs for unusual process creation events originating from the web server can aid in early detection of exploitation attempts while waiting for a formal software update.