CVE-2025-23354 Overview
CVE-2025-23354 is a code injection vulnerability affecting NVIDIA Megatron-LM, a large-scale transformer training framework used for training large language models. The flaw resides in the ensemble_classifier script, where attacker-supplied data can be interpreted as code during processing. An authenticated local attacker with low privileges can trigger the injection by feeding crafted input to the vulnerable script. Successful exploitation leads to arbitrary code execution, privilege escalation, information disclosure, and data tampering within the host environment. The issue is tracked under CWE-94: Improper Control of Generation of Code.
Critical Impact
Local attackers can execute arbitrary code in the context of the Megatron-LM process, compromising confidentiality, integrity, and availability of AI training workloads and any sensitive model data or credentials present on the host.
Affected Products
- NVIDIA Megatron-LM versions prior to the fixed release
- NVIDIA Megatron-LM 0.13.0 and its release candidates (rc0 through rc4)
- All platforms supported by Megatron-LM (Linux-based AI/ML training environments)
Discovery Timeline
- 2025-09-24 - CVE-2025-23354 published to the National Vulnerability Database
- 2025-09-24 - NVIDIA publishes support advisory ID 5698
- 2026-06-17 - Last updated in NVD database
Technical Details for CVE-2025-23354
Vulnerability Analysis
The vulnerability exists in the ensemble_classifier script shipped with NVIDIA Megatron-LM. The script processes input data in a manner that permits attacker-controlled content to be evaluated as executable code rather than treated as inert data. This class of flaw, categorized under CWE-94, occurs when a program constructs code segments from untrusted input without sufficient neutralization.
Exploitation requires local access and low privileges, but does not require user interaction. The scope remains unchanged, and the impact on confidentiality, integrity, and availability is high because the injected code runs with the privileges of the Megatron-LM process. In shared AI training clusters, this can extend to other tenants' data and model artifacts.
Root Cause
The root cause is improper neutralization of directives in a code interpreter path within the ensemble_classifier script. Input parsed by the script is passed into a code-generation or evaluation routine without sanitization, allowing embedded payloads to be executed. NVIDIA has not published the full technical breakdown; refer to the NVIDIA Support Advisory for vendor-confirmed details.
Attack Vector
An attacker with local access to a system running Megatron-LM stages a malicious input file or dataset that the ensemble_classifier script will consume. When an authorized user or automated pipeline invokes the script against the poisoned data, the crafted payload is interpreted as code and executed within the training process context. This yields the attacker the ability to run arbitrary commands, exfiltrate proprietary model weights, tamper with training data, or pivot to escalate privileges on the host.
No verified public exploit code is available. Technical details are provided in the NVD CVE-2025-23354 Details record and the CVE-2025-23354 Record.
Detection Methods for CVE-2025-23354
Indicators of Compromise
- Unexpected child processes spawned by Python interpreters running ensemble_classifier or related Megatron-LM training scripts.
- Outbound network connections from Megatron-LM training nodes to non-approved destinations during or after ensemble classification runs.
- Modifications to model checkpoints, tokenizer files, or training datasets outside of scheduled training windows.
- New or modified files in Megatron-LM working directories that do not match expected training artifacts.
Detection Strategies
- Monitor process trees originating from Megatron-LM scripts for shell invocations, package installations, or interpreter calls that are not part of standard training workflows.
- Alert on writes to sensitive paths such as ~/.ssh, /etc, or credential stores from user contexts running AI training jobs.
- Baseline the argument patterns and input file paths supplied to ensemble_classifier and flag deviations.
Monitoring Recommendations
- Enable command-line auditing on GPU compute nodes and forward events to a centralized analytics platform for correlation.
- Track file integrity on training data directories and Megatron-LM installation paths.
- Correlate authenticated local user activity with script executions to identify accounts running Megatron-LM outside expected schedules.
How to Mitigate CVE-2025-23354
Immediate Actions Required
- Inventory all systems running NVIDIA Megatron-LM, including containerized deployments and Jupyter environments, and identify versions in use.
- Apply the fixed release published by NVIDIA per the vendor advisory as soon as it is validated in staging.
- Restrict local access to Megatron-LM hosts to trusted operators and enforce least-privilege on service accounts running training jobs.
- Audit historical ensemble_classifier invocations and input datasets for signs of tampering.
Patch Information
NVIDIA has issued a security advisory covering CVE-2025-23354. Consult the NVIDIA Support Advisory (ID 5698) for the fixed version, download instructions, and release notes. Upgrade all vulnerable instances of Megatron-LM 0.13.0 and earlier release candidates to the patched release.
Workarounds
- Do not run ensemble_classifier against datasets sourced from untrusted or unverified locations until patched.
- Execute Megatron-LM training jobs inside isolated containers or dedicated user accounts with no access to secrets, credentials, or production data.
- Enforce input validation and cryptographic integrity checks on datasets before they are consumed by ensemble scripts.
- Apply mandatory access controls such as SELinux or AppArmor profiles to constrain the Megatron-LM process's file system and network reach.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
