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A critical remote code execution (RCE) vulnerability, tracked as CVE-2025-49655, has been identified in the Keras machine learning framework, specifically affecting versions 3.11.0 up to but not including 3.11.3. The flaw arises from unsafe deserialization of untrusted data within the TorchModuleWrapper class, which can be exploited when a maliciously crafted Keras file is loaded. This vulnerability allows attackers to execute arbitrary code on the victim's system, even if Keras is running in safe mode, significantly increasing the risk profile for users who routinely load external or shared model files. The issue can be triggered through both local and remote files, making it exploitable in a variety of deployment scenarios, including cloud-based and on-premises environments. The vulnerability has been assigned a CVSS v3.1 base score of 9.8, categorizing it as critical due to the ease of exploitation and the potential impact. Security researchers have highlighted that the attack does not require authentication, and exploitation can occur simply by loading a malicious model file. The vulnerability was publicly disclosed in October 2025, with security advisories urging immediate action. At the time of disclosure, the affected product versions were clearly identified, but some databases had not yet listed all impacted vendors or products. The Keras development team has released a patch in version 3.11.3 to address the issue, and users are strongly advised to upgrade to this version or later. Organizations using Keras in production or research environments should audit their model loading workflows and restrict the use of untrusted model files. The vulnerability underscores the broader risks associated with deserialization flaws in machine learning frameworks, which are increasingly targeted due to their widespread adoption. Security teams are encouraged to monitor for suspicious activity related to model file handling and to implement additional controls where feasible. The incident has prompted renewed calls for secure coding practices and rigorous input validation in AI and machine learning software. The rapid response from the Keras maintainers has been noted, but the incident serves as a warning for similar frameworks to review their own deserialization logic. The vulnerability's critical rating reflects both the technical severity and the potential for widespread exploitation if left unaddressed. Industry experts recommend that all organizations using Keras review their current deployments and apply the necessary updates without delay. The disclosure of CVE-2025-49655 has also led to increased scrutiny of third-party model sharing platforms, which may inadvertently distribute malicious files. This event highlights the importance of maintaining up-to-date software and following best practices for secure machine learning operations.
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Advisories recommended updating Keras to a version later than 3.11.3 and avoiding untrusted Keras files as mitigation for CVE-2025-49655. References to a Keras GitHub pull request and a HiddenLayer security advisory indicate a fix and coordinated remediation guidance were available.
A critical deserialization vulnerability in Keras, tracked as CVE-2025-49655, was disclosed affecting versions 3.11.0 through 3.11.2. The flaw allows arbitrary code execution when a malicious Keras file containing a TorchModuleWrapper class is loaded, even with safe mode enabled.
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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.
Jun 29, 2026
NVIDIA disclosed **CVE-2026-24233**, a high-severity vulnerability in **TensorRT-LLM for Linux** caused by insecure deserialization in the product’s restricted unpickler for model weight loading. The flaw affects versions **up to and including `v1.3.0 rc14`** and allows a **local, unauthenticated attacker** to trigger deserialization of untrusted data, potentially leading to **remote code execution, privilege escalation, data tampering, and information disclosure**. The issue carries a **CVSS v3.1 score of 8.4** with vector `AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H`. The vulnerability is classified as **`CWE-502: Deserialization of Untrusted Data`**, a weakness that arises when software processes attacker-controlled serialized input without sufficient validation. MITRE notes that such flaws can let attackers abuse gadget chains or unsafe object handling to alter program state, cause denial of service, or execute arbitrary code, and recommends mitigations including integrity protection for serialized data, strict allowlisting of acceptable classes, reducing available gadget types, and defensive coding patterns that block unsafe deserialization paths.
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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.
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