Tencent HunyuanDiT model_resume Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent HunyuanDiT. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the model_resume function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27183.
Tencent HunyuanDiT merge Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent HunyuanDiT. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the merge endpoint. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27190.
Tencent Hunyuan3D-1 load_pretrained Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent Hunyuan3D-1. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the load_pretrained function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27191.
Hugging Face Transformers GLM4 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the parsing of weights. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-28309.
CVE-2025-14929 is a high-severity vulnerability in Hugging Face Transformers affecting the X-CLIP checkpoint conversion process. It arises from improper validation of user-supplied data during deserialization, enabling remote code execution if a user opens a malicious file or visits a crafted webpage. Exploitation requires user interaction but no prior authentication. The vulnerability impacts confidentiality, integrity, and availability by allowing arbitrary code execution within the affected process context. No known exploits are currently in the wild. European organizations using vulnerable versions of Hugging Face Transformers, especially in AI/ML workflows, face significant risk. Mitigation involves avoiding untrusted checkpoint files, applying patches when available, and implementing strict input validation and sandboxing. Countries with strong AI sectors and extensive use of Hugging Face tools, such as Germany, France, the UK, and the Netherlands, are most likely affected.
Hugging Face Accelerate Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Accelerate. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27985.
Hugging Face Diffusers CogView4 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Diffusers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27424.
Hugging Face smolagents Remote Python Executor Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face smolagents. Authentication is not required to exploit this vulnerability.
The specific flaw exists within the parsing of pickle data. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-28312.
Hugging Face Transformers megatron_gpt2 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27984.
CVE-2025-14920 is a high-severity remote code execution vulnerability in the Hugging Face Transformers library, specifically affecting the Perceiver model deserialization process. The flaw arises from improper validation of user-supplied model files, allowing deserialization of untrusted data. Exploitation requires user interaction, such as opening a malicious file or visiting a crafted webpage. Successful exploitation enables attackers to execute arbitrary code with the privileges of the current user. Although no known exploits are reported in the wild, the vulnerability poses significant risks to confidentiality, integrity, and availability. The vulnerability has a CVSS score of 7.8, reflecting its high impact and relatively low complexity to exploit. European organizations using Hugging Face Transformers in AI/ML workflows, especially those processing untrusted or external model files, are at risk. Mitigation involves strict validation of model inputs, restricting model file sources, and applying patches once available. Countries with strong AI development sectors and extensive use of open-source ML frameworks, such as Germany, France, and the UK, are most likely affected.
CVE-2025-14921 is a high-severity remote code execution vulnerability in the Hugging Face Transformers library, specifically affecting the Transformer-XL model deserialization process. The flaw arises from improper validation of user-supplied model files, enabling deserialization of untrusted data. Exploitation requires user interaction, such as visiting a malicious webpage or opening a crafted file, allowing attackers to execute arbitrary code with the privileges of the current user. The vulnerability impacts confidentiality, integrity, and availability of affected systems. No known exploits are currently reported in the wild. European organizations using vulnerable versions of Hugging Face Transformers are at risk, especially those integrating Transformer-XL models in their AI workflows. Mitigation involves strict validation of model files, restricting model sources, and applying updates once patches become available. Countries with significant AI development and adoption, such as Germany, France, and the UK, are more likely to be affected due to higher usage of these tools.
The Live Composer – Free WordPress Website Builder plugin for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 2.0.2 via deserialization of untrusted input in the dslc_module_posts_output shortcode. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is present in the vulnerable plugin, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present.
pluginsGLPI's Database Inventory Plugin "manages" the Teclib' inventory agents in order to perform an inventory of the databases present on the workstation. Prior to version 1.1.2, in certain conditions (database write access must first be obtained through another vulnerability or misconfiguration), user-controlled data is stored insecurely in the database via computergroup, and is later unserialized on every page load, allowing arbitrary PHP object instantiation. Version 1.1.2 fixes the issue.
Apache NiFi 1.20.0 through 2.6.0 include the GetAsanaObject Processor, which requires integration with a configurable Distribute Map Cache Client Service for storing and retrieving state information. The GetAsanaObject Processor used generic Java Object serialization and deserialization without filtering. Unfiltered Java object deserialization does not provide protection against crafted state information stored in the cache server configured for GetAsanaObject. Exploitation requires an Apache NiFi system running with the GetAsanaObject Processor, and direct access to the configured cache server. Upgrading to Apache NiFi 2.7.0 is the recommended mitigation, which replaces Java Object serialization with JSON serialization. Removing the GetAsanaObject Processor located in the nifi-asana-processors-nar bundle also prevents exploitation.
Genymobile/scrcpy versions up to and including 3.3.3, prior to commit 3e40b24, contain a buffer overflow vulnerability in the sc_device_msg_deserialize() function. A compromised device can send crafted messages that cause out-of-bounds reads, which may result in memory corruption or a denial-of-service condition. This vulnerability may allow further exploitation on the host system.
Deserialization of Untrusted Data vulnerability in BoldThemes Codiqa codiqa allows Object Injection.This issue affects Codiqa: from n/a through < 1.2.8.
Deserialization of Untrusted Data vulnerability in BoldGrid Client Invoicing by Sprout Invoices sprout-invoices allows Object Injection.This issue affects Client Invoicing by Sprout Invoices: from n/a through <= 20.8.7.
CVE-2025-33210 is a critical deserialization vulnerability in NVIDIA Isaac Lab versions prior to 2.3.0. Exploiting this flaw allows an attacker with limited privileges and requiring user interaction to execute arbitrary code, impacting confidentiality, integrity, and availability. The vulnerability arises from unsafe deserialization of untrusted data (CWE-502). Although no known exploits are currently in the wild, the high CVSS score (9.0) indicates severe risk. European organizations using NVIDIA Isaac Lab, especially in robotics and AI research sectors, face significant threats. Mitigation requires upgrading to version 2.3.
NVIDIA NeMo Framework for all platforms contains a vulnerability where malicious data created by an attacker may cause a code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.
NVIDIA NeMo Framework contains a vulnerability in model loading that could allow an attacker to exploit improper control mechanisms if a user loads a maliciously crafted file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, denial of service, and data tampering.
