CVE-2026-24245: NVIDIA Megatron Bridge Deserialization Code Execution Vulnerability

Technical summary

CVE-2026-24245 is classified as CWE-502: Deserialization of Untrusted Data. The vulnerability exists in NVIDIA Nemo Megatron Bridge running on Linux systems. During deserialization of input objects, the application fails to validate or sanitize the source data properly, allowing an attacker to supply crafted serialized objects that execute arbitrary code in the application's context. The CVSS 3.1 vector (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) indicates local attack vector, low attack complexity, no privilege requirement, but requires user interaction. The high confidentiality, integrity, and availability impacts reflect full system compromise potential.

Business impact

Organizations deploying NVIDIA Megatron Bridge in machine learning or AI pipelines face the risk of code injection attacks that could compromise model integrity, expose training data, or disrupt pipeline operations. Since the vulnerability requires user interaction, the practical risk depends on who has access to the system and whether untrusted serialized data could be introduced through model files, checkpoint data, or inter-process communication. In shared ML environments or where data scientists handle external datasets, this could be a significant insider-risk or supply-chain vector. Successful exploitation could result in data exfiltration, unauthorized model modifications, or lateral movement within the infrastructure.

Affected systems

The vulnerability affects NVIDIA Nemo Megatron Bridge on Linux systems. The vulnerability also lists Linux Kernel as an affected vendor_product; however, without explicit version ranges in the source data, affected versions should be verified directly from NVIDIA's official advisory. Organizations running Megatron Bridge for distributed training, particularly in multi-tenant or research environments where serialized objects are exchanged, should inventory their deployments and check version status.

Exploitability

The vulnerability requires local system access and user interaction, which meaningfully reduces its exploitability compared to a network-based attack. An attacker would need to either already possess shell access to the target machine or convince a user to deserialize a malicious object (e.g., by opening a crafted checkpoint file or model artifact). Once a user interacts with untrusted data, exploitation is likely to succeed because attack complexity is low. The threat is most acute in environments with high user trust in data sources or where automated deserialization of third-party artifacts occurs without validation. This vulnerability is not in the CISA KEV catalog as of the current data snapshot.

Remediation

Remediation requires applying a patched version of NVIDIA Megatron Bridge from NVIDIA's official release channels. Consult NVIDIA's security advisory for specific patched versions and upgrade timelines. In parallel, implement input validation and sandboxing: avoid deserializing untrusted checkpoint files, use secure serialization formats where possible, and isolate Megatron Bridge processes from sensitive data or high-privilege contexts. For organizations unable to patch immediately, restrict who can supply serialized objects to the system and implement file integrity monitoring on model checkpoints.

Patch guidance

Contact NVIDIA directly or visit their security advisory page for the specific patched version of Nemo Megatron Bridge that remediates CVE-2026-24245. Apply patches to all systems running vulnerable versions in development, staging, and production environments. Test patches in a non-production environment first, especially in ML pipelines, to ensure compatibility with existing models and training jobs. Prioritize production systems handling sensitive data or high-value models. Given the user-interaction requirement, patching can often be scheduled during maintenance windows without emergency urgency, but should not be indefinitely delayed.

Detection guidance

Monitor for unexpected deserialization activity or exceptions in Megatron Bridge logs. Look for unusual checkpoint or model file access, particularly files with unexpected modifications or from untrusted sources. Inspect process execution from Megatron Bridge instances for indicators of code injection or spawned child processes. File integrity monitoring on model artifacts and serialized objects can alert to tampering. Network-based detection is less applicable due to the local attack vector, but endpoint detection and response (EDR) tools should be configured to flag privilege escalation attempts originating from Megatron Bridge processes. Correlate file access logs with deserialization errors or crashes.

Why prioritize this

Despite the HIGH CVSS score (7.8), the local attack vector and user interaction requirement reduce the immediate urgency compared to a remotely exploitable vulnerability. However, this should not be deferred: in ML-focused organizations, developers routinely exchange model files and checkpoints, creating natural opportunities for exploitation. The impact scope—code execution, privilege escalation, and data theft—is severe. Prioritize patching systems in research environments, shared clusters, and any context where deserialization of external artifacts is common. Organizations processing sensitive training data should treat this as moderate-to-high priority; those in air-gapped or tightly controlled environments may schedule patches more flexibly.

Risk score, explained

The CVSS 3.1 score of 7.8 (HIGH) reflects the severe impact (confidentiality, integrity, availability all high) balanced against the local-only attack surface and requirement for user interaction. The score appropriately flags this as a serious vulnerability requiring patching, but the attack complexity and user-interaction barrier prevent it from reaching CRITICAL (9.0+). In risk-assessment frameworks, adjust this score upward for organizations with high model/data sensitivity or frequent external data ingestion; adjust downward for isolated, air-gapped clusters with strong access controls.

Frequently asked questions