CVE-2026-24239: NVIDIA NeMo Speech RCE Vulnerability

CVE-2026-24239 Overview

CVE-2026-24239 is a deserialization vulnerability [CWE-502] affecting NVIDIA NeMo Speech across all supported platforms. An attacker who supplies malicious data to the framework can trigger remote code execution when the data is processed. Successful exploitation can lead to code execution, information disclosure, and data tampering on the affected host.

The vulnerability requires local access and user interaction, which limits opportunistic exploitation. However, machine learning pipelines that ingest untrusted model artifacts or speech data are exposed. NVIDIA published a security advisory tracking this issue under advisory number 5885.

Critical Impact

Processing attacker-supplied data with NVIDIA NeMo Speech can grant an attacker code execution in the context of the ML workload, along with the ability to read or modify data accessible to that process.

Affected Products

• NVIDIA NeMo Speech (all platforms)
• Downstream applications and containers embedding NeMo Speech components
• ML pipelines that load NeMo Speech models or checkpoints from untrusted sources

Discovery Timeline

• 2026-09-22 - CVE CVE-2026-24239 published to the National Vulnerability Database (NVD)
• 2026-09-22 - Last updated in NVD database

Technical Details for CVE-2026-24239

Vulnerability Analysis

The issue is classified as Deserialization of Untrusted Data [CWE-502]. NVIDIA NeMo Speech processes input artifacts, such as model checkpoints or configuration payloads, without sufficient validation of embedded serialized objects. When the framework reconstructs these objects, attacker-controlled code paths can be reached and executed inside the Python runtime hosting NeMo.

Because NeMo Speech is commonly integrated into GPU-accelerated inference and training workflows, code execution frequently occurs inside privileged data-science environments with access to models, datasets, and cloud credentials. The impact extends to information disclosure and data tampering because the attacker inherits the process's read and write permissions.

Exploitation depends on a user loading or otherwise processing the malicious artifact. Automated pipelines that fetch third-party models or user-uploaded audio processing bundles remove the interaction barrier by design and increase real-world exposure.

Root Cause

The root cause is unsafe deserialization of untrusted input inside NeMo Speech. Python serialization formats such as pickle execute arbitrary constructors during object reconstruction, and NeMo checkpoint loaders that rely on this mechanism will invoke attacker-defined code without integrity or origin checks.

Attack Vector

An attacker crafts a malicious NeMo Speech artifact, for example a poisoned .nemo archive or checkpoint, and delivers it through a model hub, shared storage, or a repository the victim trusts. When a developer, researcher, or automated job loads the artifact locally, deserialization triggers the embedded payload and grants the attacker code execution as the loading user.

No verified public exploit code is available. Refer to the NVIDIA Security Advisory for vendor-provided technical detail.

Detection Methods for CVE-2026-24239

Indicators of Compromise

• Unexpected child processes spawned by Python interpreters running NeMo Speech, especially shells, curl, wget, or compiler invocations.
• NeMo checkpoint files (.nemo, .ckpt, .pt) sourced from unverified locations or with recent modification timestamps that do not match trusted build pipelines.
• Outbound network connections from GPU worker nodes to previously unseen hosts shortly after a model load event.

Detection Strategies

• Monitor process lineage on ML workstations and inference servers for non-ML binaries executed as descendants of NeMo processes.
• Alert on writes to sensitive paths such as ~/.ssh/authorized_keys, cloud credential files, or scheduled task locations by any Python process hosting NeMo.
• Inspect model artifacts prior to loading using tools that scan for pickle opcodes referencing os.system, subprocess, or builtins.eval.

Monitoring Recommendations

• Enable audit logging on directories that store shared model checkpoints and forward events to a centralized SIEM.
• Baseline expected network destinations for training and inference nodes to detect deviation after checkpoint loads.
• Track user interaction telemetry that shows which accounts load third-party NeMo artifacts and correlate with subsequent host activity.

How to Mitigate CVE-2026-24239

Immediate Actions Required

• Apply the fixed NeMo Speech release identified in the NVIDIA Security Advisory as soon as it is available in your environment.
• Inventory all systems and container images that bundle NeMo Speech and prioritize patching hosts that ingest external models.
• Quarantine and rescan any NeMo checkpoints obtained from untrusted or unverified sources before further use.

Patch Information

NVIDIA distributes fixes for NeMo through its product-security advisories. Review the NVIDIA Security Advisory for the fixed version numbers, container image tags, and upgrade guidance. Verify installed versions with pip show nemo_toolkit and update within your standard change window.

Workarounds

• Restrict NeMo Speech workloads to isolated, non-privileged accounts and containers with minimal host and cloud credential exposure.
• Load only checkpoints produced by trusted, signed pipelines, and reject artifacts of unknown provenance.
• Disable automated ingestion of user-uploaded models until patched versions are deployed.

# Verify installed NeMo version and upgrade to the patched release
pip show nemo_toolkit
pip install --upgrade nemo_toolkit

# Constrain runtime to a dedicated, unprivileged user
useradd --system --shell /usr/sbin/nologin nemo-runner
sudo -u nemo-runner python -m nemo.collections.asr.models --help