CVE-2022-24031: n/a SMM corruption

CISO Take

This is a memory corruption flaw in NvmExpressDxe, a UEFI driver inside Insyde's InsydeH2O firmware, that lets an already-privileged local attacker write fixed or predictable data into SMRAM and escalate from the OS into System Management Mode (SMM) — effectively ring -2, below the hypervisor and OS. It is not an AI/ML software vulnerability despite the ml_library tag on this feed entry; it matters to AI teams only because InsydeH2O ships on a wide range of OEM servers and workstations, including many of the GPU boxes and edge appliances that host training and inference workloads. EPSS places likely exploitation in the top 79th percentile of all scored CVEs, but the raw probability is still low (0.28%), there is no CISA KEV listing, and no public exploit or Nuclei template exists — so this is a low-likelihood, high-impact firmware risk rather than an active threat. Because SMM-level implants survive OS reinstalls and are invisible to EDR/antivirus, treat this as an infrastructure-hardening item: confirm with your server OEM (not Insyde directly) whether a patched UEFI/BIOS build exists for the affected InsydeH2O kernel range (5.1-5.5) and apply it during your next maintenance window, and use firmware integrity tooling to detect SMRAM tampering on AI infrastructure hosts in the meantime.

Sources:NVDEPSSinsyde.comsecurity.netapp.comcert-portal.siemens.com

What is the risk?

Overall risk is MEDIUM and highly exposure-dependent. Exploitability is non-trivial — SMM vulnerabilities of this class typically require existing privileged (kernel-level or physical) access to trigger, so this is a privilege-escalation/persistence primitive rather than a remote initial-access vector. Impact if exploited is severe: full compromise of the firmware layer beneath the OS and hypervisor, enabling persistent, EDR-invisible implants. Exposure for AI/ML organizations depends entirely on hardware inventory — those running GPU training clusters, on-prem inference/model-serving boxes, or edge AI appliances on OEM hardware built on InsydeH2O firmware (kernel 5.1-5.5) are exposed; cloud-only shops with no bare-metal/edge footprint are not.

How does the attack unfold?

Initial Access

Attacker already holds kernel-level or physical access to an AI training/inference host or edge appliance, typically via a prior credential or OS-level compromise.

Exploitation

Attacker triggers the NvmExpressDxe flaw to write fixed or predictable data into SMRAM during SMI handling.

Privilege Escalation / Persistence

The corrupted SMRAM state is leveraged to gain code execution in SMM (ring -2), installing a firmware-resident implant invisible to the OS and EDR.

Impact

Attacker uses the persistent SMM foothold to tamper with or exfiltrate training data, model weights, or credentials on the AI infrastructure host, surviving OS reinstalls and redeployments.

Initial Access

Attacker already holds kernel-level or physical access to an AI training/inference host or edge appliance, typically via a prior credential or OS-level compromise.

Exploitation

Attacker triggers the NvmExpressDxe flaw to write fixed or predictable data into SMRAM during SMI handling.

Privilege Escalation / Persistence

The corrupted SMRAM state is leveraged to gain code execution in SMM (ring -2), installing a firmware-resident implant invisible to the OS and EDR.

Impact

Attacker uses the persistent SMM foothold to tamper with or exfiltrate training data, model weights, or credentials on the AI infrastructure host, surviving OS reinstalls and redeployments.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
n/a	—	—	No patch

Do you use n/a? You're affected.

How severe is it?

CVSS 3.1

N/A

EPSS

0.3%

chance of exploitation in 30 days

Higher than 21% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Advanced

What should I do?

1 step

1) Inventory AI/ML servers and edge devices whose OEM firmware is built on Insyde InsydeH2O with kernel 5.1-5.5 (check via dmidecode -t bios or the vendor's firmware utility). 2) Patch by contacting the hardware OEM — Insyde supplies firmware to OEMs, who release the actual updates (see insyde.com/security-pledge/SA-2022015); NetApp and Siemens have already published advisories (ntap-20220216-0009, ssa-306654) confirming affected product lines, so check equivalent advisories from your own server/appliance vendor. 3) Compensating controls until patched: restrict physical and privileged local access to AI infrastructure hosts, and enable Secure Boot / any available SMM protection or lock features in firmware settings. 4) Detection: run firmware integrity tooling (e.g., CHIPSEC, TPM-based remote attestation) periodically on AI training/inference hosts to catch SMRAM tampering that OS-level EDR cannot see.

How is it classified?

Code ExecutionSupply ChainInference

Which compliance frameworks are affected?

Compliance analysis pending. Sign in for full compliance mapping when available.

Frequently Asked Questions

What is CVE-2022-24031?

This is a memory corruption flaw in NvmExpressDxe, a UEFI driver inside Insyde's InsydeH2O firmware, that lets an already-privileged local attacker write fixed or predictable data into SMRAM and escalate from the OS into System Management Mode (SMM) — effectively ring -2, below the hypervisor and OS. It is not an AI/ML software vulnerability despite the ml_library tag on this feed entry; it matters to AI teams only because InsydeH2O ships on a wide range of OEM servers and workstations, including many of the GPU boxes and edge appliances that host training and inference workloads. EPSS places likely exploitation in the top 79th percentile of all scored CVEs, but the raw probability is still low (0.28%), there is no CISA KEV listing, and no public exploit or Nuclei template exists — so this is a low-likelihood, high-impact firmware risk rather than an active threat. Because SMM-level implants survive OS reinstalls and are invisible to EDR/antivirus, treat this as an infrastructure-hardening item: confirm with your server OEM (not Insyde directly) whether a patched UEFI/BIOS build exists for the affected InsydeH2O kernel range (5.1-5.5) and apply it during your next maintenance window, and use firmware integrity tooling to detect SMRAM tampering on AI infrastructure hosts in the meantime.

Is CVE-2022-24031 actively exploited?

No confirmed active exploitation of CVE-2022-24031 has been reported, but organizations should still patch proactively.

How to fix CVE-2022-24031?

1) Inventory AI/ML servers and edge devices whose OEM firmware is built on Insyde InsydeH2O with kernel 5.1-5.5 (check via `dmidecode -t bios` or the vendor's firmware utility). 2) Patch by contacting the hardware OEM — Insyde supplies firmware to OEMs, who release the actual updates (see insyde.com/security-pledge/SA-2022015); NetApp and Siemens have already published advisories (ntap-20220216-0009, ssa-306654) confirming affected product lines, so check equivalent advisories from your own server/appliance vendor. 3) Compensating controls until patched: restrict physical and privileged local access to AI infrastructure hosts, and enable Secure Boot / any available SMM protection or lock features in firmware settings. 4) Detection: run firmware integrity tooling (e.g., CHIPSEC, TPM-based remote attestation) periodically on AI training/inference hosts to catch SMRAM tampering that OS-level EDR cannot see.

What systems are affected by CVE-2022-24031?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, edge AI deployments.

What is the CVSS score for CVE-2022-24031?

No CVSS score has been assigned yet.

What is the AI security impact?

Affected AI Architectures

training pipelinesmodel servingedge AI deployments

What are the technical details?

Original Advisory

An issue was discovered in NvmExpressDxe in Insyde InsydeH2O with kernel 5.1 through 5.5. An SMM memory corruption vulnerability allows an attacker to write fixed or predictable data to SMRAM. Exploiting this issue could lead to escalating privileges to SMM.

Exploitation Scenario

An attacker who has already gained kernel-level or physical access to an on-prem AI training server or edge-AI appliance — for example via a prior credential compromise of an ML engineer's admin account — leverages the NvmExpressDxe flaw to write predictable data into SMRAM during an NVMe-related SMI handler call. This escalates their foothold from OS-level privileges into SMM (ring -2), letting them install a firmware-resident implant invisible to the OS, hypervisor, and any EDR running above it. From that position they can silently monitor or exfiltrate training data and model artifacts passing through the host, tamper with model files before they are loaded for serving, or maintain long-term persistence on the AI infrastructure that survives OS reinstalls, container redeployments, or even full server rebuilds — undermining incident-response assumptions of a clean recovery.