CVE-2025-23357: NVIDIA Megatron-LM RCE Vulnerability

CVE-2025-23357 Overview

CVE-2025-23357 is a code injection vulnerability [CWE-94] in NVIDIA Megatron-LM affecting all supported platforms. A script within Megatron-LM improperly processes attacker-supplied data, allowing arbitrary code to execute in the context of the invoking user. NVIDIA disclosed the issue in NVIDIA Support Article #5712.

Successful exploitation can lead to code execution, privilege escalation, information disclosure, and data tampering on systems running the affected framework. The vulnerability requires local access with low privileges and no user interaction.

Critical Impact
A local attacker who can supply crafted data to a vulnerable Megatron-LM script can execute arbitrary code with the privileges of the process, compromising confidentiality, integrity, and availability of the AI training environment.

Affected Products

NVIDIA Megatron-LM (all supported platforms)
Machine learning training pipelines that consume untrusted data through Megatron-LM scripts
Multi-tenant GPU environments running Megatron-LM workloads

Discovery Timeline

2025-11-11 - CVE-2025-23357 published to NVD
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-23357

Vulnerability Analysis

CVE-2025-23357 is classified as improper control of code generation [CWE-94]. A script shipped with Megatron-LM interprets or evaluates data controlled by an attacker without sufficient validation. When that data reaches an unsafe execution sink, the runtime treats it as code rather than inert input.

Megatron-LM is a large language model training framework widely used to build transformer models on GPU clusters. Scripts within the framework routinely process configuration files, dataset descriptors, and tokenizer inputs. When these inputs are attacker-influenced, the code injection primitive becomes reachable.

Exploitation yields the full triad of confidentiality, integrity, and availability impact. An attacker can steal model weights, alter training data, or disrupt long-running jobs on shared infrastructure.

Root Cause

The root cause is the use of an unsafe execution or evaluation pattern on attacker-controlled input inside a Megatron-LM script. Common patterns that produce this behavior include dynamic evaluation of expressions, unsafe deserialization, and unchecked string interpolation into shell or interpreter commands. NVIDIA's advisory identifies the flaw as a script-level code injection defect and provides fixed builds.

Attack Vector

The attack vector is local. An attacker with low-privileged access to the host, or the ability to place malicious content into a location the vulnerable script reads, can trigger execution. Realistic delivery paths include poisoned training datasets, malicious configuration files, and shared workspace directories on multi-user GPU nodes. The vulnerability manifests when the affected script parses this crafted content. See the NVIDIA advisory and the NVD entry for authoritative technical details.

Detection Methods for CVE-2025-23357

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running Megatron-LM training scripts, especially shells or network utilities.
Modifications to training artifacts, checkpoints, or dataset files that do not correspond to a scheduled job.
Outbound network connections from GPU worker nodes to unfamiliar destinations during training runs.
New or modified files in user home directories or shared dataset paths immediately before a Megatron-LM script executes.

Detection Strategies

Inventory hosts running Megatron-LM and correlate installed versions against the fixed release in NVIDIA Support Article #5712.
Monitor process ancestry for python processes launching interpreters, sh, bash, or curl under Megatron-LM working directories.
Alert on writes to Megatron-LM configuration files, dataset manifests, and tokenizer files by non-owner accounts.

Monitoring Recommendations

Enable audit logging on directories that hold training data, model configs, and checkpoints consumed by Megatron-LM.
Capture command-line arguments for all Python processes on GPU nodes and forward them to a central analytics platform for anomaly review.
Baseline normal training-job behavior and alert on deviations in child processes, file writes, and outbound connections.

How to Mitigate CVE-2025-23357

Immediate Actions Required

Upgrade Megatron-LM to the fixed version identified in NVIDIA Support Article #5712.
Restrict local shell and filesystem access on GPU training hosts to a minimal set of trusted operators.
Treat all externally sourced datasets, configuration files, and tokenizers as untrusted input and validate them before use.
Rotate credentials, tokens, and keys that were accessible to Megatron-LM processes if compromise is suspected.

Patch Information

NVIDIA has published a fix as described in NVIDIA Support Article #5712. Apply the updated Megatron-LM release on every host and container image used for training. Rebuild any downstream container images that pin an affected version and redeploy training jobs against the patched artifacts.

Workarounds

Run Megatron-LM training jobs inside isolated containers with read-only mounts for datasets and no outbound internet access.
Enforce least privilege on service accounts that execute Megatron-LM scripts, removing sudo and unnecessary group memberships.
Move dataset and configuration ingestion behind a validation gateway that rejects executable content and unexpected file types.
Segment multi-tenant GPU environments so that a compromised training job cannot reach adjacent tenants or shared secrets.

bash

# Verify installed Megatron-LM version and confirm it matches the fixed release
pip show megatron-lm | grep -i version

# Run training jobs as a non-privileged user in a restricted container
docker run --rm \
  --user 10001:10001 \
  --read-only \
  --network=none \
  -v /data/datasets:/data:ro \
  megatron-lm:patched python pretrain_gpt.py --config /data/config.yaml