Raw vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-24151 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Nvidia Megatron-Lm. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 11th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-24151 is a vulnerability in NVIDIA Megatron-LM, specifically within its inferencing component. An attacker can cause remote code execution (RCE) by convincing a user to load a maliciously crafted input. The issue stems from CWE-502 (Deserialization of Untrusted Data) and carries a CVSS v3.1 base score of 7.8 (AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high impact with local access, low complexity, and low privileges required.
A local attacker with low privileges can exploit this vulnerability by socially engineering a user to process the malicious input during Megatron-LM inferencing. Successful exploitation enables arbitrary code execution, privilege escalation, information disclosure, and data tampering on the affected system.
Mitigation details are available in official advisories, including the NVIDIA security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5769, the NVD entry at https://nvd.nist.gov/vuln/detail/CVE-2026-24151, and the CVE record at https://www.cve.org/CVERecord?id=CVE-2026-24151.
This vulnerability affects an AI/ML framework used for large language model inferencing, underscoring risks associated with untrusted inputs in such pipelines. No information on real-world exploitation is available in the provided details.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-15007
Vulnerability Data
NVIDIA Megatron-LM contains a vulnerability in inferencing where an Attacker may cause an RCE by convincing a user to load a maliciously crafted input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure,…
more
and data tampering.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.