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Vulnerability Database/CVE-2026-61779

CVE-2026-61779: Nvidia Nemo Megatron Bridge RCE Vulnerability

CVE-2026-61779 is a deserialization vulnerability in Nvidia Nemo Megatron Bridge that enables remote code execution, data tampering, and information disclosure. This article covers technical details, affected versions, and mitigation.

Published:

CVE-2026-61779 Overview

NVIDIA Megatron Bridge contains an insecure deserialization vulnerability [CWE-502] that allows a local authenticated attacker to trigger deserialization of untrusted data. Successful exploitation can lead to arbitrary code execution, data tampering, and information disclosure on the affected host.

The issue affects the nvidia:nemo_megatron_bridge component used within NVIDIA's NeMo Megatron machine-learning stack. Because the flaw executes under the privileges of the process handling the serialized input, attackers can pivot from limited local access to full compromise of the training or inference environment.

Critical Impact

A local attacker with low privileges can execute arbitrary code, tamper with model data, and disclose sensitive information stored on the host.

Affected Products

  • NVIDIA Nemo Megatron Bridge (nvidia:nemo_megatron_bridge)
  • Deployments consuming Megatron Bridge serialized artifacts (checkpoints, configs)
  • ML training and inference pipelines integrating the affected component

Discovery Timeline

  • 2026-09-01 - CVE-2026-61779 published to the National Vulnerability Database (NVD)
  • 2026-09-02 - Last updated in NVD database

Technical Details for CVE-2026-61779

Vulnerability Analysis

The vulnerability stems from the deserialization of attacker-controlled data without validating its origin or integrity. When Megatron Bridge loads a serialized object, such as a Python pickle payload or an equivalent object graph, embedded gadget chains execute during reconstruction.

An attacker who can place or modify a serialized artifact consumed by the bridge gains code execution in the loading process. Because ML pipelines commonly ingest checkpoints, tokenizer state, and configuration files from shared storage, the attack surface extends across data-loading paths used during training and inference.

The attack is local and requires low privileges, but no user interaction. Impact spans confidentiality, integrity, and availability, matching a full compromise of the process context.

Root Cause

The root cause is insecure deserialization of untrusted data [CWE-502]. Megatron Bridge reconstructs objects from serialized input without cryptographic verification or allow-listed type constraints, allowing arbitrary callable invocation during unpickling.

Attack Vector

Exploitation requires local access with low privileges. An attacker stages a malicious serialized artifact, such as a poisoned model checkpoint or configuration blob, on a path the bridge subsequently loads. When Megatron Bridge deserializes the file, embedded gadgets execute under the caller's identity.

No verified proof-of-concept code is public. For technical detail refer to the NVIDIA Security Resource and the NVD CVE-2026-61779 Details.

Detection Methods for CVE-2026-61779

Indicators of Compromise

  • Unexpected child processes spawned by Python interpreters loading Megatron Bridge modules
  • Modified or newly written checkpoint, tokenizer, or config files in shared model directories with unusual owners or timestamps
  • Outbound network connections initiated from training or inference processes to unknown hosts shortly after checkpoint loads

Detection Strategies

  • Instrument ML pipelines to log the full path, hash, and provenance of every deserialized artifact and alert on unsigned or unknown hashes
  • Monitor for calls to pickle.load, torch.load, or equivalent APIs against files outside approved storage locations
  • Correlate process-tree anomalies where an ML training job forks shells, package managers, or credential-access binaries

Monitoring Recommendations

  • Baseline expected file operations and network egress for Megatron Bridge workloads and alert on deviations
  • Forward endpoint, container, and Kubernetes audit logs to a centralized analytics platform for retrospective hunting
  • Track access to model registries and object storage buckets that feed the bridge, flagging writes from non-CI identities

How to Mitigate CVE-2026-61779

Immediate Actions Required

  • Update NVIDIA Megatron Bridge to the fixed version referenced in the NVIDIA Security Resource
  • Restrict local access to hosts running Megatron Bridge to trusted operators and service accounts only
  • Audit existing checkpoints, tokenizers, and configuration files for unauthorized modifications and rebuild from trusted sources if needed

Patch Information

NVIDIA has published guidance in its product-security repository. Apply the vendor-supplied update per the NVIDIA Security Resource. Consult the CVE-2026-61779 Record for cross-references.

Workarounds

  • Load only serialized artifacts from cryptographically signed, integrity-verified sources controlled by your organization
  • Run Megatron Bridge processes under least-privilege service accounts inside isolated containers with read-only model directories
  • Enforce mandatory access controls on directories that host checkpoints and configuration files consumed by the bridge
bash
# Configuration example: restrict permissions on model artifact directories
chown -R mluser:mlgroup /opt/models/megatron
chmod -R 750 /opt/models/megatron
find /opt/models/megatron -type f -exec chmod 640 {} \;

# Verify checkpoint integrity before loading
sha256sum -c /opt/models/megatron/checkpoints.sha256

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

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