CVE-2026-24146
Nvidia Triton Inference Server ≤ 26.02
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-24146 is a high-severity Memory Allocation with Excessive Size Value (CWE-789) vulnerability in Nvidia Triton Inference Server. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 42th 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 SI-10 (Information Input Validation) and SC-6 (Resource Availability) — 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-2026-24146 is a vulnerability in NVIDIA Triton Inference Server stemming from insufficient input validation combined with a large number of outputs, which can trigger a server crash. This issue, classified under CWE-789 (Uncontrolled Memory Allocation), carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H), indicating high severity primarily due to its impact on availability.
A remote, unauthenticated attacker can exploit this vulnerability over the network with low complexity and no user interaction required. Successful exploitation results in denial-of-service, causing the server to crash and potentially disrupting inference services until restart.
Official advisories, including NVIDIA's security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5816 and NVD details at https://nvd.nist.gov/vuln/detail/CVE-2026-24146, provide guidance on mitigation; security practitioners should consult these for patching instructions and workarounds specific to affected Triton Inference Server versions.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19749
Vulnerability Data
NVIDIA Triton Inference Server contains a vulnerability where insufficient input validation and a large number of outputs could cause a server crash. A successful exploit of this vulnerability might lead to denial of service.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly rejects or bounds untrusted size values before any allocation occurs.
Resource quotas and priority allocation limit the system-wide impact of an oversized request.
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.
Secure SDLC practices directly prevent coding flaws that trust unvalidated size values for allocations.
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 in development can detect and block excessive allocation flaws before deployment.
Secure development lifecycle includes input validation and size checks that prevent unbounded allocations.
Application security requirements mandate bounds checking on size parameters to avoid excessive memory allocation.
Secure architecture principles require resource-limit enforcement that mitigates uncontrolled memory requests.
Secure coding standards directly prohibit allocating memory from untrusted size values without validation.
Capacity management monitors overall resource use but does not prevent individual allocation bugs.