CVE-2022-24030: n/a SMM memory corruption enables

CISO Take

This is an SMM (System Management Mode) memory corruption flaw in Insyde InsydeH2O firmware's AhciBusDxe driver, allowing an attacker with sufficient local access to write fixed or predictable data into SMRAM and escalate privileges to the most trusted execution ring on the machine, below the OS and hypervisor. It is not an AI-specific vulnerability — it is a general UEFI firmware bug affecting any server or workstation running affected InsydeH2O builds (kernel 5.1-5.5), including GPU training clusters, inference servers, and edge AI devices that happen to run this firmware. There is no CISA KEV listing, no public exploit or Nuclei template, and EPSS sits at 0.3% (top 77th percentile, meaning the vast majority of CVEs are still more likely to be exploited) — practical risk is low but the ceiling of impact is severe since SMM compromise survives OS reinstalls and most endpoint defenses. Action: identify hardware/appliances in your AI infrastructure fleet running InsydeH2O firmware (check with your hardware/BMC vendor — NetApp and Siemens have issued advisories per the references), apply the vendor firmware patch, and treat this as a standard infrastructure hardening item rather than an AI-pipeline-specific alert.

Sources:NVDEPSS

What is the risk?

Low near-term exploitation likelihood: EPSS 0.3% (top 77th percentile of scored CVEs), no CISA KEV listing, no public PoC/exploit code, and no Nuclei detection template. However, impact ceiling is high because SMM is the most privileged x86 execution mode — a successful exploit can persist below OS/hypervisor visibility, defeating secure boot assumptions and most runtime detection. Exploitation almost certainly requires local/administrative access to the target system (typical for SMM/firmware-class bugs), which significantly narrows the realistic attack surface compared to remote, unauthenticated flaws.

How does the attack unfold?

Local Access Foothold

Attacker obtains local/administrative access to a host running vulnerable InsydeH2O firmware, such as an AI training or inference server.

SMM Memory Corruption

Attacker exploits the AhciBusDxe flaw to write fixed or predictable data into SMRAM, corrupting SMM memory.

Privilege Escalation to SMM

The corruption is leveraged to execute code in System Management Mode, the highest-privilege x86 execution ring, below OS and hypervisor visibility.

AML.T0010.000

Persistent Firmware-Level Compromise

Attacker establishes a firmware-level implant that survives OS reinstalls and can undermine secure boot/attestation guarantees relied upon by systems hosted on the compromised hardware, including AI infrastructure.

Local Access Foothold

Attacker obtains local/administrative access to a host running vulnerable InsydeH2O firmware, such as an AI training or inference server.

SMM Memory Corruption

Attacker exploits the AhciBusDxe flaw to write fixed or predictable data into SMRAM, corrupting SMM memory.

Privilege Escalation to SMM

The corruption is leveraged to execute code in System Management Mode, the highest-privilege x86 execution ring, below OS and hypervisor visibility.

AML.T0010.000

Persistent Firmware-Level Compromise

Attacker establishes a firmware-level implant that survives OS reinstalls and can undermine secure boot/attestation guarantees relied upon by systems hosted on the compromised hardware, including AI infrastructure.

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 23% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Advanced

What should I do?

1 step

1) Inventory hardware and appliances (including AI/ML compute nodes, storage backends, and network/security appliances) for Insyde InsydeH2O firmware in the affected kernel range (5.1-5.5). 2) Apply vendor-supplied firmware updates — check with your OEM/appliance vendor (NetApp and Siemens have published advisories, see references) since Insyde itself doesn't ship firmware directly to end users. 3) Restrict local administrative and physical access to systems hosting sensitive AI workloads, since SMM exploitation of this class typically requires elevated local privileges. 4) There is no software-level detection signature available (no Nuclei template); rely on firmware version inventory and vendor patch management rather than runtime detection.

How is it classified?

Code ExecutionInferenceAML.T0010.000- Hardware

Which compliance frameworks are affected?

This CVE is relevant to:

ISO 42001

A.6.2.6-AI system infrastructure security

Frequently Asked Questions

What is CVE-2022-24030?

This is an SMM (System Management Mode) memory corruption flaw in Insyde InsydeH2O firmware's AhciBusDxe driver, allowing an attacker with sufficient local access to write fixed or predictable data into SMRAM and escalate privileges to the most trusted execution ring on the machine, below the OS and hypervisor. It is not an AI-specific vulnerability — it is a general UEFI firmware bug affecting any server or workstation running affected InsydeH2O builds (kernel 5.1-5.5), including GPU training clusters, inference servers, and edge AI devices that happen to run this firmware. There is no CISA KEV listing, no public exploit or Nuclei template, and EPSS sits at 0.3% (top 77th percentile, meaning the vast majority of CVEs are still more likely to be exploited) — practical risk is low but the ceiling of impact is severe since SMM compromise survives OS reinstalls and most endpoint defenses. Action: identify hardware/appliances in your AI infrastructure fleet running InsydeH2O firmware (check with your hardware/BMC vendor — NetApp and Siemens have issued advisories per the references), apply the vendor firmware patch, and treat this as a standard infrastructure hardening item rather than an AI-pipeline-specific alert.

Is CVE-2022-24030 actively exploited?

No confirmed active exploitation of CVE-2022-24030 has been reported, but organizations should still patch proactively.

How to fix CVE-2022-24030?

1) Inventory hardware and appliances (including AI/ML compute nodes, storage backends, and network/security appliances) for Insyde InsydeH2O firmware in the affected kernel range (5.1-5.5). 2) Apply vendor-supplied firmware updates — check with your OEM/appliance vendor (NetApp and Siemens have published advisories, see references) since Insyde itself doesn't ship firmware directly to end users. 3) Restrict local administrative and physical access to systems hosting sensitive AI workloads, since SMM exploitation of this class typically requires elevated local privileges. 4) There is no software-level detection signature available (no Nuclei template); rely on firmware version inventory and vendor patch management rather than runtime detection.

What systems are affected by CVE-2022-24030?

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

What is the CVSS score for CVE-2022-24030?

No CVSS score has been assigned yet.

What is the AI security impact?

Affected AI Architectures

model servingtraining pipelines

MITRE ATLAS Techniques

AML.T0010.000Hardware

Compliance Controls Affected

ISO 42001: A.6.2.6

What are the technical details?

Original Advisory

An issue was discovered in AhciBusDxe 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 local administrative access to an AI training or inference host (e.g., via a prior compromise, insider access, or a co-tenant in an under-isolated multi-tenant GPU environment) leverages the AhciBusDxe SMM flaw to write attacker-controlled or predictable data into SMRAM. This escalates their foothold from OS-level access to SMM, the highest-privilege x86 ring, allowing them to persist firmware-level implants that survive OS reinstalls, disable security controls, or pivot to tamper with the integrity of the AI training/inference environment itself (e.g., undermining secure boot or attestation the AI platform relies on for supply-chain trust).