CVE-2026-24267: NVIDIA NeMo Speech RCE Vulnerability

CVE-2026-24267 Overview

CVE-2026-24267 is a remote code execution vulnerability in the NVIDIA NeMo Speech toolkit, specifically within the speech data explorer component. An attacker who crafts malicious data can trigger code execution when a user loads that data through the affected component. The flaw is classified under CWE-502: Deserialization of Untrusted Data.

The vulnerability affects NVIDIA NeMo Speech across all supported platforms. Successful exploitation can lead to code execution, privilege escalation, information disclosure, and data tampering in the context of the user running the tool.

Critical Impact

Local exploitation with user interaction can grant an attacker full control over the affected process, including access to model artifacts, training data, and any credentials available to the user.

Affected Products

• NVIDIA NeMo Speech (all platforms) — speech data explorer component
• Deployments processing untrusted speech datasets or third-party model artifacts
• Machine learning workstations and pipelines that ingest attacker-controlled files

Discovery Timeline

• 2026-09-22 - CVE-2026-24267 published to NVD
• 2026-09-22 - Last updated in NVD database
• Reference - NVIDIA Product Security Advisory 5885

Technical Details for CVE-2026-24267

Vulnerability Analysis

The vulnerability resides in the speech data explorer component of NVIDIA NeMo Speech. The component processes input data in a way that permits code execution when a crafted payload is loaded. The weakness maps to CWE-502, indicating that untrusted serialized data is deserialized without adequate validation.

Exploitation requires local access and user interaction, meaning a victim must open or process the malicious data using the affected component. The scope remains unchanged, but the impact on confidentiality, integrity, and availability is high because the payload executes with the privileges of the invoking user.

Machine learning environments frequently share datasets, checkpoints, and preprocessing artifacts between researchers. This distribution pattern gives attackers a realistic delivery channel for weaponized files. The EPSS score is currently low, but the impact profile matches known deserialization abuse patterns in Python-based ML tooling.

Root Cause

The root cause is unsafe deserialization of attacker-influenced input inside the speech data explorer. When the component reconstructs objects from serialized data, it does not restrict the types or callables that may be instantiated. This allows an attacker to embed gadgets that execute arbitrary code at load time.

Attack Vector

An attacker crafts a malicious file that the speech data explorer accepts as valid input. The attacker then convinces a victim to open the file — for example, by publishing it as a dataset, sample, or shared artifact. When the victim loads the file, the embedded payload executes with the victim's privileges, enabling code execution, credential theft, or manipulation of local NeMo assets.

No verified public proof-of-concept is available at this time. Refer to the NVIDIA Security Advisory for authoritative technical details.

Detection Methods for CVE-2026-24267

Indicators of Compromise

• Unexpected child processes spawned by Python interpreters running NeMo Speech tooling, particularly shells, curl, wget, or scripting engines.
• Outbound network connections initiated shortly after a user opens a .pkl, .pt, or dataset manifest file in the speech data explorer.
• New or modified files in NeMo cache and configuration directories following interaction with third-party datasets.

Detection Strategies

• Monitor process lineage where the parent process is a NeMo Speech utility and the child process performs system reconnaissance or network activity.
• Alert on deserialization calls (pickle.load, torch.load) executed against files sourced from untrusted or user-download locations.
• Correlate file open events on speech dataset formats with subsequent script execution or credential access attempts.

Monitoring Recommendations

• Enable command-line auditing on workstations and GPU nodes that run NeMo Speech workloads.
• Ingest endpoint telemetry into a centralized data lake to build baselines for legitimate NeMo process behavior.
• Track integrity of datasets and model artifacts using cryptographic hashes recorded at ingest time.

How to Mitigate CVE-2026-24267

Immediate Actions Required

• Restrict use of the speech data explorer to trusted datasets and vetted internal sources until a patched version is deployed.
• Inventory workstations and pipelines that run NVIDIA NeMo Speech and prioritize those handling external data.
• Revoke and rotate credentials that were accessible from any host that processed untrusted speech datasets.

Patch Information

Consult the NVIDIA Product Security Advisory for bulletin 5885 for the fixed release and upgrade guidance. Apply the vendor-supplied update to all NeMo Speech installations, including containers and derivative images. Additional record details are available at the NVD entry for CVE-2026-24267 and the CVE.org record.

Workarounds

• Only load datasets, checkpoints, and manifest files obtained from verified, first-party sources.
• Run the speech data explorer inside an isolated container or sandbox with no access to sensitive credentials or shared storage.
• Disable or remove the speech data explorer component from production pipelines that do not require interactive exploration.
• Enforce least-privilege on user accounts that execute NeMo tooling to limit blast radius of a successful exploit.

# Configuration example: run NeMo Speech tooling in an isolated container
docker run --rm -it \
  --network=none \
  --read-only \
  --tmpfs /tmp \
  --user 1000:1000 \
  -v /path/to/trusted/data:/data:ro \
  nvcr.io/nvidia/nemo:<patched-version> \
  python -m nemo.tools.speech_data_explorer /data/manifest.json