CVE-2026-24245: Nvidia Nemo Megatron Bridge RCE Flaw

CVE-2026-24245 Overview

CVE-2026-24245 affects NVIDIA Megatron Bridge for Linux. The vulnerability allows an attacker to trigger deserialization of untrusted data [CWE-502]. Successful exploitation can lead to arbitrary code execution, privilege escalation, data tampering, and information disclosure on affected systems.

The flaw requires local access and user interaction to exploit. Because Megatron Bridge is used in large-scale machine learning pipelines, compromise of an affected host can expose training data, model artifacts, and adjacent GPU infrastructure. The issue is tracked as an insecure deserialization vulnerability in the nvidia:nemo_megatron_bridge component.

Critical Impact
An attacker with local access can execute arbitrary code, escalate privileges, tamper with data, and access sensitive information by supplying a crafted serialized payload to NVIDIA Megatron Bridge.

Affected Products

NVIDIA NeMo Megatron Bridge (all versions prior to the vendor-supplied fix)
Linux platforms running the affected Megatron Bridge component
Machine learning environments integrating nvidia:nemo_megatron_bridge with the Linux kernel

Discovery Timeline

2026-07-01 - CVE-2026-24245 published to NVD
2026-07-02 - Last updated in NVD database

Technical Details for CVE-2026-24245

Vulnerability Analysis

CVE-2026-24245 is an insecure deserialization vulnerability [CWE-502] in NVIDIA Megatron Bridge for Linux. Megatron Bridge is used to move model state, checkpoints, and configuration data between NVIDIA NeMo and Megatron-LM training workflows. The affected code path reconstructs Python objects from serialized input without sufficient validation of type or content.

When the component processes a crafted serialized artifact, the deserialization routine instantiates attacker-controlled objects. This behavior lets an adversary chain object constructors or magic methods to run arbitrary Python or native code inside the training process. The process typically runs with elevated privileges and holds credentials for GPU resources, storage, and cluster orchestration.

Exploitation impacts confidentiality, integrity, and availability. Attackers can tamper with model weights, exfiltrate training datasets, or pivot to other nodes in a distributed training job.

Root Cause

The root cause is trust placed in serialized input consumed by Megatron Bridge. The component deserializes objects such as checkpoints or configuration blobs without enforcing an allowlist of safe classes. Any user who can deliver a crafted file into the workflow controls the object graph reconstructed at load time.

Attack Vector

The attack vector is local and requires user interaction. An attacker plants a malicious serialized artifact, such as a poisoned checkpoint, model file, or configuration bundle, and convinces a legitimate user to load it through Megatron Bridge. Common delivery paths include shared file systems, model registries, and downloaded checkpoints from untrusted sources.

No verified proof-of-concept code is publicly available for CVE-2026-24245. See the GitHub NVIDIA Security Repository and the NVD CVE-2026-24245 Detail for vendor technical context.

Detection Methods for CVE-2026-24245

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running Megatron Bridge or NeMo workloads.
Loading of checkpoint or configuration files from paths outside approved model registries.
Outbound network connections initiated by training processes to non-approved hosts during checkpoint load.
New or modified files in user home directories or /tmp immediately after a model load event.

Detection Strategies

Hunt for pickle, torch.load, or equivalent deserialization calls that consume files from writable, shared, or user-controlled directories.
Alert on Megatron Bridge processes executing shells, package managers, or interpreters as child processes.
Correlate file writes to model directories with subsequent process execution to identify weaponized checkpoints.

Monitoring Recommendations

Log all checkpoint and configuration file loads with source path, hash, and initiating user.
Monitor GPU host telemetry for privilege changes and unusual credential access following a training job start.
Track integrity of files under model registry and checkpoint directories using file integrity monitoring.

How to Mitigate CVE-2026-24245

Immediate Actions Required

Upgrade NVIDIA NeMo Megatron Bridge to the fixed version listed in the NVIDIA product security advisory.
Inventory all hosts running nvidia:nemo_megatron_bridge and prioritize shared multi-tenant training systems.
Restrict write access to checkpoint, model, and configuration directories to trusted service accounts only.
Quarantine and re-hash any checkpoints obtained from untrusted sources before loading.

Patch Information

NVIDIA has published fix information for CVE-2026-24245 in its product security repository. Refer to the GitHub NVIDIA Security Repository for affected versions, fixed builds, and upgrade instructions. Cross-reference the CVE.org CVE-2026-24245 Record for authoritative CVE metadata.

Workarounds

Load only checkpoints and configuration bundles produced by trusted internal pipelines with verified cryptographic signatures.
Run Megatron Bridge workloads under least-privilege service accounts inside isolated containers or namespaces.
Disable or gate any workflow that ingests serialized artifacts from external users, shared uploads, or public model hubs.
Apply mandatory access controls such as SELinux or AppArmor profiles to restrict what training processes can execute.

bash

# Configuration example: restrict checkpoint directory and verify signatures before load
chown -R mlops:mlops /opt/nemo/checkpoints
chmod -R 750 /opt/nemo/checkpoints

# Verify checkpoint signature before invoking Megatron Bridge
sha256sum -c /opt/nemo/checkpoints/model.ckpt.sha256 \
  && gpg --verify /opt/nemo/checkpoints/model.ckpt.sig /opt/nemo/checkpoints/model.ckpt \
  || { echo "Checkpoint verification failed"; exit 1; }