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NVIDIA AIStore CVE-2026-65162

| EUVDEUVD-2026-98555 CRITICAL
Execution with Unnecessary Privileges (CWE-250)
2026-10-13 psirt@nvidia.com GHSA-958w-8f9q-43j8
10.0
CVSS 3.1 · Vendor: nvidia
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Reach AIStore ETL API over network
  2. Delivery
    Submit crafted PodSpec with host-namespace flags
  3. Exploit
    Scheduler launches privileged container
  4. Execution
    Escape container to Kubernetes node
  5. Impact
    Execute code and escalate on host

Vulnerability Assessment

Weakness
CWE
ASVS 5.0
V8 - Authorization Unnecessary privilege execution
Exploitation Conditions

Exploitation requires network reachability to the LEGACY ETL Kubernetes PodSpec initialization API in NVIDIA AIStore on Linux, and depends on that legacy ETL/transformation feature being enabled and exposed. The specific lever is attacker-supplied Kubernetes host-namespace settings (e.g. hostPID/hostNetwork/hostIPC) and container-security-context fields in the PodSpec. The vector is AV:N/AC:L/PR:N/UI:N, so no authentication or user interaction is needed against a default-reachable endpoint; the practical limiting factors are whether the ETL API is network-exposed, whether the legacy code path is in use, and the Kubernetes RBAC/PodSecurity constraints governing whether AIStore can actually schedule host-namespaced privileged pods - hardened PodSecurity admission policies would blunt the escape step.

Impact

This is a genuine top-priority vulnerability, not a high-CVSS-but-low-risk artifact. The CVSS 3.1 vector (AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H = 10.0) describes a network-reachable, low-complexity, unauthenticated flaw with scope change and total compromise of confidentiality, integrity, and availability - the worst possible profile. CWE-250 (execution with unnecessary privileges) is consistent with the description: the legacy ETL PodSpec API lets a remote caller dictate Kubernetes host-namespace and container-security settings, so an attacker can request a privileged/host-namespaced pod and break out to the node. The scope change (S:C) is well justified because compromise moves from the AIStore service to the underlying Kubernetes host. Signals are mutually reinforcing rather than conflicting: the CVSS vector and the CWE both point to privileged container escape. However, exploitation-likelihood telemetry is MISSING - no EPSS score, no CISA KEV listing, and no public POC were provided, so there is no public exploit identified at time of analysis. The absence of EPSS/KEV data does not lower the intrinsic severity; it only means real-world exploitation volume is currently unmeasured. Treat as critical and patch urgently despite the lack of exploitation telemetry.

Exploit ScenarioDerived

A remote attacker with network access to the AIStore ETL endpoint submits a crafted PodSpec initialization request that sets host-namespace flags (e.g. hostPID/hostNetwork) and permissive container-security context. AIStore schedules the attacker-controlled privileged pod, which escapes to the Kubernetes node and executes code with elevated privileges. No public exploit identified at time of analysis, so the scenario is inferred from the description and CVSS vector rather than a confirmed POC.

Act Now Prioritize within 24-48h. Why: CRITICAL severity.
No patch listed yet 0.7% exploitation probability (EPSS)

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AnalysisAIv1 – Oct 13, 2026

Unauthenticated remote attackers can achieve code execution and privilege escalation against NVIDIA AIStore for Linux by injecting arbitrary Kubernetes host-namespace and container security settings into the legacy ETL Kubernetes PodSpec initialization API, rated CVSS 10.0 (AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H) because the impact escapes the AIStore workload and extends to the underlying Kubernetes node and cluster. The vector confirms the attack is network-reachable with no privileges or user interaction required, though practical exploitation depends on the legacy ETL/transformation feature being enabled and the ETL API being exposed, and on Kubernetes RBAC and PodSecurity admission being permissive enough for AIStore to actually schedule host-namespaced or privileged pods. At time of analysis there is no confirmed active exploitation (CISA KEV) and no public exploit code identified, but the vendor-reported severity, the scope-changed impact, and CWE-250 (execution with unnecessary privileges) place this in top-priority territory for any cluster running an exposed AIStore ETL endpoint.

RemediationAI

The primary fix is to upgrade NVIDIA AIStore for Linux to the vendor-patched release identified in the NVIDIA product-security advisory for 2026/5889 (https://github.com/NVIDIA/product-security/tree/main/2026/5889); the input data does not contain an exact fixed version number, so obtain it from that advisory rather than assuming a build, and consult NVD (https://nvd.nist.gov/vuln/detail/CVE-2026-65162) for updated patch references. Until upgrading, disable the legacy ETL Kubernetes PodSpec initialization API or the ETL transformation feature entirely if transformation workloads are not business-critical - the trade-off is that ETL pipelines stop functioning. Immediately remove network exposure of the ETL API by restricting it to trusted management networks with Kubernetes NetworkPolicy and ingress allowlists, accepting that this breaks legitimate remote ETL clients. Reduce blast radius by hardening the AIStore service account with RBAC that forbids creation of pods using hostPID, hostNetwork, hostIPC, privileged containers, or hostPath mounts, and by enforcing PodSecurity Admission at the restricted or baseline level on the AIStore namespace; note that stricter admission profiles will reject misconfigured or legacy ETL jobs, so validate pipeline compatibility first. As defense in depth, deploy an admission controller (OPA Gatekeeper or Kyverno) with explicit deny rules for host-namespace and privileged securityContext fields, and alert on ETL API calls that include them.

Affected VersionsENISA

AIStore framework 0 to 4.9

Affected Packages

euvd aistore_framework_0_to no fix

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 1 package in euvd

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

NVIDIA AIStore is a distributed object-storage and data-transformation service for AI/ML pipelines; its ETL (extract-transform-load) feature lets clients define transformation workloads that AIStore instantiates as Kubernetes pods. CVE-2026-65162 resides in the legacy ETL Kubernetes PodSpec initialization API, which accepts PodSpec content from a remote caller without adequately constraining which Kubernetes fields the caller may set. Because PodSpec is a privileged configuration artifact, an attacker who controls it can request hostPID, hostNetwork, and hostIPC host-namespace sharing, privileged securityContext, dangerous Linux capabilities, permissive runAsUser/runAsGroup settings, and hostPath volume mounts. The root-cause class is CWE-250 (Execution with Unnecessary Privileges): the legacy code path executes attacker-influenced workloads with privileges and node-level namespace access that the ETL context does not require. This yields container escape and node compromise, which is why CVSS marks Scope as Changed - the security authority of the AIStore component is violated and the node, its pods, and its secrets fall inside the impact boundary, giving full confidentiality, integrity, and availability loss. The CPE data provided does not enumerate a version string, so affected builds must be confirmed against the vendor's own advisory mapping.

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Severity by source

Vendor (nvidia) PRIMARY 10.0 CRITICAL
AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
vuln.today AI 10.0 CRITICAL

Remote unauthenticated low-complexity API accepts host-namespace PodSpec fields; privileged container escape crosses from service to Kubernetes node (S:C) with full C/I/A loss.

3.1 AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
4.0 AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

Primary rating from Vendor (nvidia).

CVSS VectorVendor: nvidia

Attack Vector
Network
Attack Complexity
Low
Privileges Required
None
User Interaction
None
Scope
Changed
Confidentiality
High
Integrity
High
Availability
High

DescriptionCVE.org

NVIDIA AIStore for Linux contains a vulnerability in the legacy ETL Kubernetes PodSpec initialization API where a remote attacker may supply Kubernetes host-namespace and container-security settings. A successful exploit may lead to code execution, privilege escalation, information disclosure, data tampering, and denial of service.

Executive ViewAI

Why It Matters

NVIDIA AIStore for Linux, the vendor's Kubernetes-native object storage platform for AI workloads, carries a maximum-severity flaw that allows an unauthenticated network attacker to execute code and elevate privileges and - critically - to break out of the AIStore workload into the underlying Kubernetes node and cluster. Because the impact is scoped beyond the application itself, any organization running AIStore with the legacy ETL/transformation PodSpec API enabled is exposed at the cluster level, not just the workload level, which raises the prospect of broad compromise of shared AI training data, model artifacts, and adjacent production services. The condition is unauthenticated and requires no user interaction, so exposure is a function of network reachability and permissive scheduling controls rather than attacker sophistication.

Business Impact: Severe
Compensating Controls

No vendor-released patch identified at time of analysis. Interim mitigations: disable the legacy ETL/transformation Kubernetes PodSpec initialization API where not required; block external network access to the ETL API; tighten AIStore Kubernetes RBAC to least privilege; enforce PodSecurity admission (restricted) or equivalent policy to reject host-namespace, hostNetwork, hostPath, and privileged pod specs; isolate the AIStore namespace via network policy and dedicated node pools; monitor for anomalous pod scheduling and container escape indicators.

Threat Intelligence Overview

EPSS Score
0.7%
Top 50% exploitable
Prevalence
Low
specialized AI/ML object store, deployed only in GPU/ML clusters
Data Quality
5 sources
Confidence: Medium

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity CRITICAL
CISA KEV No
Public POC No
EPSS 0.7%
Patch available No
Active threat No
Deciding rule
CRITICAL severity
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Out-of-Cycle
Exploitation
None
System Exposure· derived
Open
Automatable
Yes
Human Impact
Very High
Technical Impact
Total

Priority Score

51
Low Medium High Critical
KEV: 0
EPSS: +1
CVSS: +50
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Privilege escalation occurs when an attacker leverages flaws in access control mechanisms to gain permissions beyond what they were originally granted.

Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.

Remote Code Execution represents the critical moment when an attacker successfully runs arbitrary code on a target system without physical access.

Denial of Service attacks render applications or systems unavailable by overwhelming resources or triggering failure conditions.

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