CVE-2026-24267: NVIDIA NeMo Speech Speech Data Explorer Remote Code Execution Vulnerability

Summary

CVE-2026-24267 is a high-severity vulnerability affecting NVIDIA NeMo Speech across all platforms. The flaw resides in the speech data explorer component, where malicious data crafted by an attacker could trigger remote code execution. With a CVSS 3.1 base score of 7.8, this vulnerability poses a significant risk to organizations using NVIDIA NeMo Speech for speech and audio processing workflows. Successful exploitation could result in code execution, escalation of privileges, information disclosure, and data tampering, making it a critical concern for security teams managing AI and machine learning infrastructure.

Technical Analysis

The vulnerability is classified under CWE-502, which refers to deserialization of untrusted data. CWE-502 vulnerabilities occur when an application deserializes data from an untrusted source without sufficient verification, allowing an attacker to manipulate the serialized object and inject malicious payloads. In the context of NVIDIA NeMo Speech, the speech data explorer component is responsible for loading, visualizing, and interacting with speech and audio datasets used during model training, evaluation, and exploration.

Speech data explorers in machine learning frameworks commonly process complex data structures, including metadata files, feature representations, annotations, and serialized model artifacts. When these components deserialize objects from user-supplied or externally sourced data files, they may use unsafe deserialization mechanisms that can be abused to execute arbitrary code. The attack surface is particularly concerning in AI and ML environments, where researchers and engineers routinely share datasets, preprocessed features, and configuration files across teams and organizations.

The CVSS 3.1 vector string for this vulnerability is CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H. Breaking this down, the attack vector is local (AV:L), meaning the attacker needs either local access or the ability to deliver a malicious file to the target system. The attack complexity is low (AC:L), indicating that exploitation does not require special conditions. No privileges are required (PR:N), which lowers the barrier for exploitation. User interaction is required (UI:R), meaning the victim must perform some action, such as opening a malicious dataset file in the speech data explorer. The scope is unchanged (S:U), and the impact on confidentiality, integrity, and availability is high across all three dimensions (C:H/I:H/A:H).

The combination of local attack vector and required user interaction suggests that the most likely exploitation path involves an attacker crafting a malicious speech dataset or metadata file and delivering it to a victim who then loads it into the NeMo Speech data explorer. This could occur through shared repositories, email attachments, collaborative data platforms, or supply chain compromises where legitimate datasets are tampered with before reaching the end user.

Because the vulnerability leads to remote code execution despite the local attack vector classification, it is important to understand that the code execution occurs within the context of the process running the speech data explorer. If that process runs with elevated privileges or has access to sensitive model artifacts, training data, API keys, or cloud credentials, the attacker gains access to those resources as well. This explains why the vulnerability description explicitly mentions escalation of privileges, information disclosure, and data tampering as potential outcomes.

Affected Software and Versions

Based on the NVD data, the affected product is NVIDIA NeMo Speech. The vulnerability description states that it affects all platforms. The NVD record does not specify particular version ranges that are affected or patched. Organizations using NVIDIA NeMo Speech should consult the official NVIDIA product security advisory referenced in this article for the most current information regarding affected versions and available updates.

Vendor: NVIDIA
Product: NeMo Speech
Platforms: All platforms
Component: Speech data explorer
Vulnerability type: CWE-502 (Deserialization of Untrusted Data)
Severity: High (CVSS 7.8)

Impact

The impact of CVE-2026-24267 is severe and multifaceted. The vulnerability description from NVIDIA and the NVD explicitly identifies four categories of impact that can result from successful exploitation:

Remote code execution: The primary impact is arbitrary code execution on the target system. An attacker who successfully exploits the deserialization flaw can run malicious code within the context of the NeMo Speech process, potentially establishing persistence, deploying additional malware, or pivoting to other systems on the network.
Escalation of privileges: Depending on the privileges of the process running the speech data explorer, an attacker may be able to escalate privileges. In enterprise AI environments, speech processing workloads may run on dedicated servers with elevated access to GPU resources, shared storage, or orchestration platforms. Compromising such a process can provide a stepping stone to broader system or cloud infrastructure compromise.
Information disclosure: The attacker can access sensitive information stored on the compromised system. In the context of NVIDIA NeMo Speech, this could include proprietary training datasets, model weights, preprocessing pipelines, configuration files containing API credentials, and personally identifiable information embedded in speech recordings or transcriptions.
Data tampering: The attacker can modify or corrupt data on the target system. This is particularly dangerous in machine learning environments, where subtle modifications to training data or model parameters can introduce backdoors, bias, or adversarial vulnerabilities that are extremely difficult to detect. An attacker could tamper with speech datasets to degrade model performance, inject triggers for adversarial attacks, or corrupt evaluation metrics.

The high ratings for confidentiality, integrity, and availability impact in the CVSS vector confirm that all three security properties are at risk. Availability impact suggests that an attacker could also disrupt speech processing services, delete critical data, or crash systems, causing denial of service conditions for dependent applications and workflows.

Exploitation Scenario

A realistic exploitation scenario for CVE-2026-24267 involves the following chain of events:

An attacker identifies a target organization or individual using NVIDIA NeMo Speech for speech recognition, text-to-speech, or audio processing tasks.
The attacker crafts a malicious serialized data file that, when deserialized by the speech data explorer component, triggers arbitrary code execution. This file could be disguised as a legitimate speech dataset, metadata file, or configuration artifact commonly exchanged in ML workflows.
The malicious file is delivered to the target. Delivery mechanisms could include posting the file to a public dataset repository, sending it via email or messaging platforms to a researcher, compromising a shared data storage location, or injecting it into a CI/CD pipeline that pulls external datasets.
A victim within the target organization opens or loads the malicious file using the NeMo Speech data explorer as part of their normal workflow, such as inspecting a new dataset before training or evaluating model outputs.
The speech data explorer deserializes the malicious payload, executing attacker-controlled code on the victim’s system.
The attacker gains a foothold on the system, from which they can escalate privileges, exfiltrate sensitive data, tamper with models and datasets, or move laterally within the network.

This scenario highlights the importance of treating all externally sourced data files as untrusted, even in research and development environments where data sharing is common practice.

Detection and Mitigation

Organizations should implement a layered approach to detect and mitigate CVE-2026-24267. The following recommendations are based on general security best practices for deserialization vulnerabilities and the specific characteristics of this flaw:

Detection

Monitor for unusual process activity originating from the NeMo Speech data explorer, including unexpected child processes, network connections to unknown destinations, or file system access patterns inconsistent with normal data exploration workflows.
Deploy endpoint detection and response (EDR) solutions that can identify suspicious deserialization activity, including the loading of unexpected classes or modules during dataset processing.
Log and review all file loading events in the speech data explorer, particularly files originating from external or untrusted sources.
Watch for signs of post-exploitation activity, such as privilege escalation attempts, credential access, data exfiltration, or modifications to model artifacts and training datasets.

Mitigation

Apply vendor patches: Consult the NVIDIA product security advisory for CVE-2026-24267 and apply any available patches or updates to NeMo Speech as soon as possible. The advisory is referenced in the References section below.
Restrict data sources: Only load datasets and metadata files from trusted, verified sources. Implement integrity verification, such as cryptographic hashes or digital signatures, for all externally sourced data files before loading them into the speech data explorer.
Sandbox the data explorer: Run the NeMo Speech data explorer in an isolated environment, such as a container or virtual machine, with minimal privileges and restricted network access. This limits the impact of successful exploitation.
Disable unsafe deserialization: If the speech data explorer allows configuration of deserialization behavior, disable or restrict unsafe deserialization mechanisms. Prefer safe serialization formats such as JSON over pickle or other executable formats where possible.
Implement least privilege: Ensure that the user account and process running the speech data explorer have the minimum privileges necessary to perform their tasks. Avoid running ML workloads as root or with unnecessary access to sensitive resources.
Train personnel: Educate researchers, data scientists, and engineers about the risks of loading untrusted data files. Establish clear policies for handling external datasets and reporting suspicious files.
Monitor supply chains: Verify the integrity of datasets and model artifacts obtained from public repositories, collaborative platforms, or third-party vendors before integrating them into NeMo Speech workflows.

Bug Bounty Angle

CVE-2026-24267 presents several interesting angles for bug bounty researchers and security analysts interested in AI and ML security:

Deserialization in ML tooling: Machine learning frameworks and data exploration tools are a rich target for deserialization vulnerabilities. Researchers should audit components that load, parse, or visualize datasets for unsafe use of pickle, YAML, or other executable serialization formats. The speech data explorer in NeMo Speech is one example, but similar components exist across many ML ecosystems.
Attack surface mapping: Identifying all entry points where external data enters the speech data explorer is critical. This includes file upload interfaces, command-line arguments, API endpoints, and integration with data versioning tools. Each entry point represents a potential vector for delivering malicious serialized payloads.
Impact demonstration: When reporting deserialization vulnerabilities, demonstrating full impact is key. For this CVE, showing code execution, privilege escalation, information disclosure, and data tampering in a proof-of-concept can help vendors understand the severity and prioritize remediation.
Safe deserialization alternatives: Researchers can contribute by identifying and recommending safe deserialization patterns. For example, replacing pickle with JSON, using allowlists for deserializable classes, or implementing integrity checks before deserialization can significantly reduce risk.
AI supply chain security: This vulnerability underscores the broader issue of supply chain security in AI. Datasets, pre-trained models, and preprocessing pipelines are frequently shared across organizations, and each represents a potential delivery mechanism for malicious payloads. Bug bounty programs that include ML infrastructure in scope can help uncover these risks before they are exploited in the wild.

Frequently Asked Questions

What is CVE-2026-24267?

CVE-2026-24267 is a high-severity vulnerability in NVIDIA NeMo Speech’s speech data explorer component. It is a deserialization of untrusted data flaw (CWE-502) that allows attackers to achieve remote code execution by crafting malicious data, potentially leading to privilege escalation, information disclosure, and data tampering.

Which versions of NVIDIA NeMo Speech are affected?

The NVD data indicates that the vulnerability affects NVIDIA NeMo Speech on all platforms. Specific version ranges are not detailed in the NVD record. Organizations should consult the official NVIDIA product security advisory for precise information on affected versions and available patches.

How can organizations protect against CVE-2026-24267?

Organizations should apply any patches released by NVIDIA, restrict the speech data explorer to processing files from trusted sources only, run the component in a sandboxed environment with least privileges, and implement integrity verification for all externally sourced datasets. Monitoring for unusual process activity and training personnel on the risks of untrusted data files are also important defensive measures.