CVE-2026-24228 Overview
CVE-2026-24228 is a deserialization of untrusted data vulnerability [CWE-502] affecting the NVIDIA NeMo Framework for Linux. An attacker with local, low-privileged access can supply crafted serialized data that the framework deserializes without sufficient validation. Successful exploitation can lead to code execution, privilege escalation, data tampering, and information disclosure within the context of the NeMo process.
NVIDIA NeMo is a generative AI framework used for developing and training large language models, speech, and multimodal models. Because NeMo workloads often run on GPU hosts processing sensitive training data and model artifacts, exploitation directly threatens AI development pipelines.
Critical Impact
Local exploitation yields code execution, privilege escalation, data tampering, and information disclosure on hosts running NVIDIA NeMo Framework for Linux.
Affected Products
- NVIDIA NeMo Framework for Linux
- Component identifier: nvidia:nemo
- CPE: cpe:2.3:a:nvidia:nemo:*:*:*:*:*:*:*:*
Discovery Timeline
- 2026-06-16 - CVE-2026-24228 published to the National Vulnerability Database (NVD)
- 2026-06-17 - Last updated in NVD database
- Vendor advisory - Published by NVIDIA as NVIDIA Support Answer 5839
Technical Details for CVE-2026-24228
Vulnerability Analysis
The vulnerability is classified as Deserialization of Untrusted Data [CWE-502]. NeMo accepts serialized objects, such as model checkpoints, configuration artifacts, or pickled Python objects, and reconstructs them at runtime without enforcing safe-loading constraints. When an attacker controls the serialized input, the deserialization process instantiates attacker-chosen classes and invokes their reducer or constructor logic.
Exploitation requires local access and low privileges, with no user interaction. The attack does not cross a security boundary by itself, but the resulting code runs with the privileges of the NeMo process, which on shared GPU servers is frequently a service or research account with broad data access.
Impact spans confidentiality, integrity, and availability. Attackers can read training data and model weights, modify checkpoints to introduce model backdoors, or terminate workloads.
Root Cause
The root cause is the use of an unsafe deserialization routine on data that crosses a trust boundary. Python machine learning frameworks frequently rely on pickle or torch.load semantics, which execute embedded code during object reconstruction. Without an allowlist of permitted classes or a signed-artifact verification step, any locally accessible serialized file becomes an execution primitive.
Attack Vector
An attacker first achieves local, low-privileged access to a host running NeMo. They then plant a malicious serialized artifact, such as a checkpoint, configuration, or cached object, in a location that NeMo reads. When a user or scheduled job loads the artifact, embedded code executes in the NeMo process context, granting the attacker code execution and a path to privilege escalation.
No public proof-of-concept exploit has been published for CVE-2026-24228, and it is not listed in the CISA Known Exploited Vulnerabilities catalog. See the NVIDIA Support Answer and the NVD CVE-2026-24228 Detail for vendor and registry details.
Detection Methods for CVE-2026-24228
Indicators of Compromise
- Unexpected child processes spawned by NeMo Python workers, such as sh, bash, curl, wget, or python invoked outside the training workflow.
- Modification timestamps on checkpoint files (.ckpt, .nemo, .pt, .pkl) that do not align with known training runs.
- Outbound network connections from NeMo processes to non-allowlisted destinations during artifact loading.
- New or modified files under model cache directories owned by accounts other than the training service account.
Detection Strategies
- Monitor process lineage for Python interpreters loading NeMo modules that subsequently execute shell utilities or compilers.
- Hash and inventory all model artifacts on disk, then alert on changes outside controlled deployment pipelines.
- Enable Linux audit rules on directories containing NeMo checkpoints to capture write events from unexpected UIDs.
Monitoring Recommendations
- Centralize host telemetry from GPU and AI training nodes into a SIEM or data lake for cross-host correlation.
- Track torch.load, pickle.load, and equivalent calls in NeMo workflows through application logging where feasible.
- Alert on first-seen binaries or scripts executed under the NeMo service account.
How to Mitigate CVE-2026-24228
Immediate Actions Required
- Apply the fixed NeMo Framework version referenced in NVIDIA Support Answer 5839 as soon as it is available in your environment.
- Restrict local access to NeMo hosts to vetted administrators and training operators only.
- Treat all existing model checkpoints and serialized configs from untrusted or shared sources as suspect until verified.
- Run NeMo workloads under a dedicated, least-privileged service account with no write access to system directories.
Patch Information
NVIDIA has published guidance and fixed-version details in the vendor advisory at NVIDIA Support Answer 5839. Administrators should upgrade NeMo Framework on all Linux hosts to the version specified by NVIDIA and redeploy any affected container images.
Workarounds
- Load model artifacts only from trusted, integrity-verified sources, ideally validated with cryptographic signatures or hash allowlists.
- Disable or replace unsafe deserialization paths with safe loaders such as weights_only=True style options where supported by the underlying framework.
- Isolate NeMo training and inference workloads in containers or VMs with restricted filesystem and network egress.
- Apply mandatory access controls (SELinux, AppArmor) to confine the NeMo process and its child processes.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
