EUVD-2026-44451
GHSA-33qc-w569-hr38
Severity by source
AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
Deserializing an attacker-supplied tensor needs no auth (PR:N) or interaction, but adjacent reachability (AV:A) and difficult heap grooming (AC:H) apply, with full C/I/A impact from memory corruption.
Primary rating from Vendor (nvidia).
CVSS VectorVendor: nvidia
CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
Lifecycle Timeline
2DescriptionCVE.org
NVIDIA TensorRT-LLM for any platform contains a vulnerability in tensor deserialization, where an attacker could cause a heap based buffer overflow. A successful exploit of this vulnerability might lead to information disclosure, data tampering, or denial of service.
Articles & Coverage 1
AnalysisAI
Heap-based buffer overflow in NVIDIA TensorRT-LLM's tensor deserialization path lets an adjacent, unauthenticated attacker corrupt heap memory by supplying a crafted serialized tensor, potentially causing information disclosure, data tampering, or denial of service. All platforms running affected TensorRT-LLM versions are impacted. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | Exploitation requires the attacker to reach the tensor deserialization code path with a maliciously crafted serialized tensor, meaning the target must accept and deserialize attacker-supplied tensor data. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | The CVSS 3.1 base score is 7.5 (High) with vector AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H, meaning no authentication or user interaction is required but the attacker must be on an adjacent network and overcome high attack complexity - consistent with the difficulty of reliably grooming a heap overflow. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | An attacker positioned on the same adjacent network as a TensorRT-LLM inference service sends a crafted serialized tensor whose size/shape metadata is malformed to the deserialization routine. The oversized fields trigger a heap-based buffer overflow, and after careful heap grooming (reflecting the high attack complexity) the attacker leaks adjacent memory, tampers with in-memory data, or crashes the service. … |
| Remediation | No vendor-released patch identified at time of analysis, so consult NVIDIA's official security bulletin for TensorRT-LLM (linked via the NVD entry at https://nvd.nist.gov/vuln/detail/CVE-2026-47471 and https://www.cve.org/CVERecord?id=CVE-2026-47471) and upgrade to the fixed release once published. … Detailed patch versions, workarounds, and compensating controls in full report. |
Recommended ActionAI
Within 24 hours: create complete inventory of all NVIDIA TensorRT-LLM deployments and current versions; implement firewall rules restricting network access to affected systems from untrusted networks (exploit requires adjacent network position), and disable TensorRT-LLM if not operationally critical. …
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External POC / Exploit Code
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