CVE-2025-23265: Nvidia Megatron-LM RCE Vulnerability

CVE-2025-23265 Overview

CVE-2025-23265 is a code injection vulnerability [CWE-94] in NVIDIA Megatron-LM, a Python-based framework for training large-scale transformer language models. The flaw resides in a Python component that improperly processes attacker-supplied files. An attacker with local access and low privileges can supply a crafted malicious file to trigger arbitrary code execution in the context of the Megatron-LM process. Successful exploitation leads to code execution, privilege escalation, information disclosure, and data tampering. The vulnerability affects Megatron-LM on all supported platforms.

Critical Impact
A local attacker can execute arbitrary Python code, escalate privileges, disclose sensitive training data, and tamper with model artifacts by delivering a malicious file to Megatron-LM.

Affected Products

NVIDIA Megatron-LM (all platforms)
Python components used for model or checkpoint loading in Megatron-LM
Environments running affected Megatron-LM releases prior to the NVIDIA-supplied fix

Discovery Timeline

2025-06-24 - CVE-2025-23265 published to the National Vulnerability Database (NVD)
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-23265

Vulnerability Analysis

The vulnerability is a code injection issue [CWE-94] in a Python component of NVIDIA Megatron-LM. Megatron-LM ingests external files such as configuration files, checkpoints, and serialized model artifacts during training and inference workflows. When the framework processes a maliciously crafted file, attacker-controlled content is interpreted as executable Python code rather than treated as inert data.

Exploitation requires local access and low privileges on the host running Megatron-LM. No user interaction is required. Once triggered, the injected code executes with the same privileges as the Megatron-LM process. In shared machine learning infrastructure, this often means access to GPU resources, training datasets, checkpoints, and credentials mounted into the workload.

Because the impact spans confidentiality, integrity, and availability, an attacker can steal proprietary model weights, poison training data, alter checkpoints, or pivot to other tenants sharing the host.

Root Cause

The root cause is unsafe handling of untrusted file content in a Python component. The affected code path deserializes or evaluates file contents without validating that the payload contains only data. Common patterns behind this class of flaw include the use of pickle.load or equivalent deserializers on attacker-controlled inputs, or eval/exec invocations against configuration fields.

Attack Vector

The attack vector is local. A user with access to the training host, notebook environment, or shared storage places a malicious file where Megatron-LM will read it, such as a checkpoint directory, dataset path, or configuration file referenced by a training job. When a higher-privileged user or automated pipeline loads the file, the embedded payload executes. Verified public exploit code is not currently available for CVE-2025-23265.

No verified public exploit code is available. See the NVIDIA Support Response for vendor-supplied technical details.

Detection Methods for CVE-2025-23265

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running Megatron-LM training or inference jobs
Outbound network connections from ML training hosts to unfamiliar destinations during checkpoint or dataset load operations
Modified or newly written checkpoint, configuration, or dataset files with mismatched hashes compared to source-of-truth artifacts
Python processes reading files from user-writable paths immediately before executing shell commands or file writes outside the training working directory

Detection Strategies

Monitor for python processes executing os.system, subprocess.Popen, or socket calls shortly after loading .pt, .ckpt, .pkl, or YAML configuration files
Baseline expected system calls for Megatron-LM training jobs and alert on deviations such as new outbound connections or writes to /etc, /root, or SSH key locations
Apply file integrity monitoring to model checkpoint and configuration directories to detect tampering that could deliver a payload

Monitoring Recommendations

Enable detailed process and command-line auditing on hosts and containers running Megatron-LM workloads
Forward GPU host telemetry, container runtime events, and Python audit hooks to a centralized data lake for correlation across jobs and users
Review access logs for shared storage (NFS, S3, object stores) holding training data and checkpoints for uploads by non-standard identities

How to Mitigate CVE-2025-23265

Immediate Actions Required

Upgrade NVIDIA Megatron-LM to the fixed version identified in the NVIDIA Support Response
Restrict write access to checkpoint, dataset, and configuration directories to trusted identities only
Audit shared training environments for untrusted files placed by users who should not be able to influence higher-privileged training jobs
Rotate any credentials, tokens, or SSH keys that were accessible to a potentially compromised Megatron-LM process

Patch Information

NVIDIA has published a security bulletin with fixed versions and remediation guidance. Refer to the NVIDIA Support Response for the specific patched release of Megatron-LM and apply it to all affected training and inference environments.

Workarounds

Load model checkpoints and configuration files only from trusted, integrity-verified sources and enforce cryptographic signature or hash checks before use
Run Megatron-LM under a dedicated low-privilege service account isolated from other tenants, secrets, and host management interfaces
Execute training jobs inside hardened containers with read-only mounts for code and strict egress network policies to limit post-exploitation movement
Disable or replace unsafe deserialization paths in custom loaders by preferring safe formats such as safetensors over pickle-based checkpoints where feasible

bash

# Configuration example: run Megatron-LM in a restricted container
docker run --rm \
  --user 10001:10001 \
  --read-only \
  --tmpfs /tmp:rw,size=512m \
  --cap-drop=ALL \
  --security-opt no-new-privileges \
  -v /srv/megatron/checkpoints:/checkpoints:ro \
  -v /srv/megatron/data:/data:ro \
  --network=training-egress-restricted \
  megatron-lm:patched \
  python pretrain_gpt.py --load /checkpoints/verified --data-path /data/verified