CVE-2026-108957 in TinyLLaVA_Factory
Summary
by MITRE • 10/13/2026
A security flaw has been discovered in TinyLLaVA TinyLLaVA_Factory up to 1abc6ac5e76a68d0fd087a071b56da968e69ca9b. The impacted element is the function _load_text_config of the file tinyllava/model/configuration_tinyllava.py of the component TinyLlavaConfig. Performing a manipulation of the argument llm_model_name_or_path results in code injection. Remote exploitation of the attack is possible. The exploit has been released to the public and may be used for attacks. This product follows a rolling release approach for continuous delivery, so version details for affected or updated releases are not provided. 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 • 10/13/2026
The vulnerability identified in TinyLLaVA prior to commit 1abc6ac5e76a68d0fd087a071b56da968e69ca9b represents a critical security defect within the model configuration loading mechanism. Specifically, the flaw resides in the _load_text_config function located in the tinyllava/model/configuration_tinyllava.py file, which is responsible for instantiating and configuring the TinyLlavaConfig component. This architectural element serves as a bridge between the vision encoder and the large language model backend, making it a central point of trust within the application's initialization sequence. The severity of this issue stems from its classification as an arbitrary code execution vulnerability triggered by improper input validation during the configuration loading process.
The technical root cause is categorized under CWE-94 Improper Control of Generation of Code or Script, commonly known as injection. When the llm_model_name_or_path argument is passed to the _load_text_config function, the system fails to adequately sanitize or validate the provided string before using it in dynamic code execution contexts. This lack of validation allows an attacker to supply a maliciously crafted path or identifier that exploits internal library functions, such as those found in Python's importlib module, to execute arbitrary commands on the host system. The vulnerability effectively bypasses standard security boundaries because the configuration loading phase typically runs with elevated privileges relative to user-level application logic, allowing injected code to operate within the same context as the model inference engine.
From an operational perspective, this flaw enables remote exploitation if the affected service is exposed over a network interface that accepts untrusted input for model initialization parameters. An attacker can craft a specific request containing a malicious llm_model_name_or_path value designed to trigger side-channel effects or direct command execution upon processing by the server. The availability of public exploits significantly lowers the barrier to entry for adversaries, facilitating widespread automated attacks against vulnerable instances. Given that TinyLLaVA employs a rolling release model, there are no discrete version numbers to patch; instead, mitigation requires updating to a commit hash strictly newer than 1abc6ac5e76a68d0fd087a071b56da968e69ca9b. The lack of response from the project maintainers further exacerbates the risk landscape for users relying on this open-source toolchain without immediate upstream fixes.
The attack vector aligns with MITRE ATT&CK techniques related to Command and Scripting Interpreter abuse, specifically those involving dynamic code evaluation or import manipulation. This type of vulnerability is particularly dangerous in machine learning pipelines where configuration files are often loaded from remote repositories or user-uploaded artifacts. The absence of strict allow-listing for model identifiers means that any string provided by an external source can potentially be interpreted as a module path, leading to the loading and execution of arbitrary Python modules. This behavior violates the principle of least privilege and secure defaults, as it assumes all input paths are safe without verification against known legitimate repositories or local directories.
To mitigate this risk, organizations deploying TinyLLaVA must immediately restrict access to configuration endpoints that accept model path arguments from untrusted sources. Input validation should be implemented at the application layer to ensure that llm_model_name_or_path only contains alphanumeric characters and standard directory separators corresponding to approved internal models. Additionally, running inference services in isolated containers with restricted filesystem permissions can limit the impact of successful code injection attempts by preventing access to sensitive system files or network resources. Until an official patch is released via a new commit hash, users should treat any external input for model configuration as hostile and implement strict filtering rules to prevent arbitrary module imports during the initialization phase.