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Multiple high-severity deserialization vulnerabilities have been identified in widely used AI and analytics frameworks, including NVIDIA Isaac Lab, MooreThreads torch_musa, and NVIDIA Merlin components. These flaws allow attackers to exploit unsafe deserialization processes, potentially leading to remote code execution or denial-of-service conditions on affected systems. In the case of MooreThreads torch_musa, the vulnerability arises from the use of pickle.load() on user-controlled files without validation, enabling arbitrary code execution with the privileges of the victim process. Similarly, NVIDIA Isaac Lab and Merlin frameworks are affected by deserialization issues that could be exploited remotely, with Merlin's NVTabular and Transformers4Rec components specifically highlighted for their susceptibility to code execution and data tampering attacks.
Security advisories urge immediate patching, as these vulnerabilities are remotely exploitable and pose significant risks to enterprise environments. The affected products span various versions, and organizations using these frameworks are advised to review vendor guidance and apply available security updates to mitigate the threat. The vulnerabilities have been assigned high or critical CVSS scores, underscoring the urgency for remediation to prevent potential exploitation in production environments.
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NVIDIA's PSIRT disclosed CVE-2025-33210, a critical deserialization vulnerability in NVIDIA Isaac Lab with potential for remote code execution. The issue was confirmed to affect Isaac Lab and prompted guidance to update to the latest version and apply available security patches.
Proof-of-concept exploit code for CVE-2025-65213 was reported as publicly available on GitHub, increasing the likelihood of exploitation in the wild. The PoC relates to unsafe use of pickle.load() on user-controlled file paths in torch_musa.
A critical deserialization vulnerability, CVE-2025-65213, was published for all versions of MooreThreads torch_musa, affecting functions in the torch_musa.utils.compare_tool module that use unsafe pickle deserialization. The issue can be exploited remotely without privileges or user interaction to achieve arbitrary code execution.
NVIDIA released security patches for its Merlin framework to fix CVE-2025-33214 and CVE-2025-33213, two high-severity deserialization vulnerabilities affecting the NVTabular Workflow and Transformers4Rec Trainer components on Linux. The flaws could allow arbitrary code execution, denial of service, sensitive information disclosure, and data tampering.
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A critical deserialization vulnerability (CVE-2025-32210) was discovered in NVIDIA Isaac Lab, a component of the NVIDIA Isaac Sim framework, allowing attackers with low privileges and minimal user interaction to execute arbitrary code on affected systems. The flaw, categorized as CWE-502, affects all versions of Isaac Lab prior to v2.3.0 and carries a CVSS score of 9.0, indicating a high risk to confidentiality, integrity, and availability. NVIDIA responded by releasing a security update that implements proper input validation and secure data handling, urging all users to upgrade to Isaac Lab v2.3.0 immediately to mitigate the risk of exploitation. The vulnerability also prompted broader advisories regarding critical security patches for NVIDIA's AI platforms, including both Isaac Lab and the NeMo Framework, due to the risk of full code execution if left unpatched. Organizations using these AI tools are strongly advised to verify their deployments and apply the latest security updates across all environments. The urgency of the patch is underscored by the potential for attackers to achieve complete system compromise through network-based attacks requiring only low-level access and user interaction.
Jun 29, 2026
NVIDIA disclosed two high-severity deserialization vulnerabilities affecting **BioNeMo** and the **FLARE SDK**, both tracked under `CWE-502` and capable of compromising confidentiality, integrity, and availability. `CVE-2026-24164` impacts BioNeMo and could allow code execution, denial of service, information disclosure, and data tampering through deserialization of untrusted data. The flaw carries a `CVSS:3.1` vector of `AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H`, indicating remote attack potential with low complexity and high impact. A second flaw, `CVE-2026-24186`, affects the NVIDIA FLARE SDK in **FOBS** handling, where a malicious FOBS-encoded message can trigger unsafe deserialization and lead to code execution. That issue was scored `CVSS:3.1 AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H`, reflecting similarly severe impact with no user interaction required. NVIDIA advisory material, NVD entries, and official CVE records were published for both vulnerabilities, signaling that organizations using either platform should review vendor guidance and prioritize remediation.
Jun 29, 2026
NVIDIA issued security updates for multiple products across its AI and enterprise software stack, fixing vulnerabilities that could allow **remote code execution**, denial of service, privilege escalation, data tampering, and information disclosure. The most severe issue disclosed in its broader March advisories was `CVE-2025-33244` in **NVIDIA Apex**, a widely used PyTorch extension for mixed-precision and distributed training. High-severity flaws were also addressed in **Triton Inference Server**, **Megatron LM**, **NeMo Framework**, and **Model Optimizer**, with NVIDIA warning that exploitation could disrupt AI workloads, expose sensitive training data, or enable unauthorized access. Medium-severity issues were additionally listed in **VIRTIO-Net**, **SNAP4**, and **B300 MCU**. NVIDIA later released urgent fixes for **TensorRT-LLM** and **Isaac Launchable**, including TensorRT-LLM deserialization flaws in MPI server and RPC testing components that could lead to RCE, data manipulation, information disclosure, or service outages. It also patched a Linux issue in Isaac Launchable involving sensitive information sent in clear text, creating risks of privilege escalation and system disruption. NVIDIA advised customers to upgrade **TensorRT-LLM** to `1.2.1` or later, and said organizations running multi-GPU deployments should use: ```bash trtllm-llmapi-launch ``` to reduce network exposure. The company has also begun publishing PSIRT bulletins on GitHub in Markdown and CSAF formats to support automated vulnerability management and faster remediation.
Jun 29, 2026