CVE-2025-23305: Nvidia Megatron-lm RCE Vulnerability

CVE-2025-23305 Overview

NVIDIA Megatron-LM contains a code injection vulnerability [CWE-94] in its tools component. An attacker with local access and low privileges can inject arbitrary code that the framework subsequently executes. Successful exploitation leads to code execution, privilege escalation, information disclosure, and data tampering on the host running Megatron-LM.

Megatron-LM is a widely deployed framework for training large transformer language models. Systems used for model training, fine-tuning, and inference pipelines are the primary risk surface. The vulnerability affects all platforms where Megatron-LM is installed.

Critical Impact
A local attacker with low privileges can achieve arbitrary code execution on Megatron-LM training hosts, compromising model integrity, training data, and GPU compute resources.

Affected Products

NVIDIA Megatron-LM (all platforms)
Machine learning training environments using Megatron-LM tools scripts
GPU compute clusters hosting Megatron-LM workflows

Discovery Timeline

2025-08-13 - CVE-2025-23305 published to NVD
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-23305

Vulnerability Analysis

The flaw resides in the tools component of NVIDIA Megatron-LM. The framework processes attacker-controlled input in a context that allows dynamic code evaluation, mapping to CWE-94 (Improper Control of Generation of Code). When a low-privileged local user supplies crafted input, Megatron-LM interprets that input as executable code rather than data.

Exploitation yields full compromise of confidentiality, integrity, and availability on the affected host. Because Megatron-LM commonly runs on multi-GPU training servers with elevated service accounts, code execution can pivot into training datasets, model checkpoints, and adjacent orchestration systems. Attackers can tamper with model weights, exfiltrate proprietary training corpora, or persist across training runs.

Root Cause

The root cause is unsanitized input processed through a code-generation or evaluation path inside a tools script. Without strict validation and allow-listing, the component treats input intended as configuration or parameters as code. This is a classic code injection pattern in Python ML frameworks where eval, exec, pickle, or dynamic import mechanisms operate on untrusted data.

Attack Vector

The attack vector is local. An attacker needs a foothold on the host with low privileges and no user interaction. Typical scenarios include a shared training server where multiple users submit jobs, a compromised CI/CD runner invoking Megatron-LM utilities, or a container where an attacker can influence input files passed to the tools scripts. The attacker crafts input that the vulnerable tool parses, triggering code execution under the identity of the process invoking Megatron-LM.

No public proof-of-concept exploit is available for CVE-2025-23305, and the vulnerability is not listed in the CISA Known Exploited Vulnerabilities catalog. See the NVIDIA Support Response for vendor guidance.

Detection Methods for CVE-2025-23305

Indicators of Compromise

Unexpected child processes spawned by Megatron-LM Python interpreters, particularly shells (/bin/sh, bash), curl, or wget.
New or modified files in Megatron-LM tools directories, model checkpoint paths, or user home directories outside normal training workflows.
Outbound network connections from GPU training hosts to unrecognized destinations during or after tools script execution.

Detection Strategies

Monitor Python processes launched from Megatron-LM tools scripts for anomalous system calls, file writes outside the training workspace, and process forks.
Alert on invocations of eval, exec, pickle.loads, or subprocess with attacker-influenceable arguments in ML tooling telemetry.
Correlate local user activity on shared GPU hosts with Megatron-LM job submissions to identify low-privileged users triggering privileged behavior.

Monitoring Recommendations

Enable process-level auditing (auditd or eBPF) on hosts running Megatron-LM and forward events to a centralized data lake.
Track integrity of model checkpoints, training datasets, and tools source files with hash-based file integrity monitoring.
Review GPU workload logs for unexpected script arguments, environment variables, or input file paths passed to Megatron-LM utilities.

How to Mitigate CVE-2025-23305

Immediate Actions Required

Apply the fixed version of NVIDIA Megatron-LM as directed in the NVIDIA Support Response.
Inventory all hosts, containers, and CI/CD pipelines that install or invoke Megatron-LM tools scripts.
Restrict local access to training hosts so only trusted operators can submit jobs or supply input files.

Patch Information

NVIDIA has published remediation guidance in security bulletin 5685. Refer to the NVIDIA Support Response for fixed versions and upgrade instructions. Additional record details are available at the NVD CVE-2025-23305 entry and CVE.org record.

Workarounds

Run Megatron-LM under a dedicated, non-privileged service account with no access to secrets, orchestration credentials, or unrelated datasets.
Isolate training workloads in per-job containers with read-only mounts for source code and strict egress network controls.
Validate and allow-list all input files, configuration parameters, and dataset paths passed to Megatron-LM tools scripts before execution.
Remove or disable unused tools scripts to reduce the attack surface on shared training hosts.

bash

# Example: run Megatron-LM under a restricted user in an isolated container
docker run --rm \
  --user 1500:1500 \
  --read-only \
  --tmpfs /tmp:rw,size=512m \
  --network none \
  -v /data/training:/data/training:ro \
  -v /models/checkpoints:/models/checkpoints:rw \
  nvcr.io/nvidia/megatron-lm:<patched-tag> \
  python tools/<script>.py --config /data/training/config.yaml