CVSS 10.0 OneView RCE: Detect Faster With AI

A CVSS 10.0 vulnerability isn’t “another patch Tuesday item.” It’s the kind of flaw that can turn an infrastructure management plane into an attacker’s launchpad—fast. This week’s disclosure of CVE-2025-37164 in HPE OneView is exactly that: an unauthenticated remote code execution (RCE) risk affecting versions prior to 11.00, with hotfixes available for 5.20 through 10.20.

Here’s why this matters beyond the vendor advisory: OneView isn’t a user laptop app. It’s the software that can touch a lot of what you run—compute, profiles, templates, orchestration, and the operational workflows your admins rely on. When a tool sits close to the “keys to the kingdom,” even a short exposure window can be expensive.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: manual security operations are structurally too slow for CVSS-10 infrastructure flaws. AI-driven threat detection and automation don’t replace patching—but they can shrink the time between “risk exists” and “we’re safe,” and they can catch exploitation attempts while teams are still coordinating upgrades.

What happened: the OneView flaw in plain language

Answer first: HPE patched a maximum-severity OneView vulnerability that could let a remote, unauthenticated attacker execute code on the OneView system.

HPE OneView provides centralized infrastructure management, which makes it operationally valuable—and security sensitive. The vulnerability (tracked as CVE-2025-37164) is rated CVSS 10.0, meaning it’s treated as the highest severity class in common risk scoring.

What we know from the advisory details:

• Impact: Remote code execution
• Access: Remote, unauthenticated
• Affected: OneView prior to 11.00
• Fix options: Upgrade to 11.00 or apply hotfixes for 5.20–10.20
• Operational gotcha: Some upgrade paths and reimaging actions require reapplying the hotfix (a real-world footgun during incident response)

Even if there’s “no evidence of exploitation,” this category of bug is routinely targeted because it’s high payoff and often externally reachable in messy enterprise networks.

Why CVSS 10.0 in management planes is a different kind of emergency

Answer first: An RCE in an infrastructure management platform can become a “control-plane compromise,” which often beats endpoint security and accelerates lateral movement.

Security teams tend to triage with a familiar mental model: patch the vulnerable server, monitor endpoints, add WAF rules, move on. That model breaks down when the vulnerable thing is the system that manages other systems.

The blast radius is bigger than the OneView host

A compromised management plane can enable attackers to:

• Create or modify server profiles and templates
• Push configuration changes that look “legitimate” because they came from the admin platform
• Harvest secrets, tokens, or cached credentials used for orchestration
• Use the management host as a pivot point into segmented networks

The scary part isn’t just initial code execution. It’s what follows: trusted automation becomes attacker automation.

The timing problem: patching isn’t instantaneous

In December, many organizations are running with:

• Reduced staffing and slower approvals (holiday schedules)
• Change freezes (especially in regulated industries)
• Backlogged vulnerability queues from year-end audits

That’s exactly when high-severity flaws become most dangerous—because defenders are slower even when they know what to do.

Where AI-driven threat detection changes the math

Answer first: AI helps most when it reduces two delays: the delay to notice you’re being targeted, and the delay to act safely at scale.

Patching still matters. But AI-based cybersecurity tools can cover the gap between disclosure and full remediation, and they can help you avoid the classic failure mode: “We planned the upgrade, but we missed one appliance.”

1) AI for vulnerability prioritization that matches reality

Most companies get this wrong: they prioritize vulnerabilities by raw CVSS score and generic asset tags. That’s how you end up patching a noisy endpoint issue while a management plane sits exposed.

AI-assisted prioritization can combine signals that humans rarely unify quickly:

• Exploitability patterns (unauthenticated RCE + network reachable = high urgency)
• Asset criticality inferred from traffic and identity relationships (what systems authenticate to it? what does it control?)
• Exposure evidence (is it reachable from internet-facing segments, VPN ranges, partner networks?)
• Change risk modeling (upgrade + hotfix + reimage interactions that create regressions)

A practical target metric I like is this: reduce “time-to-first-action” to under 4 hours for CVSS 10 management plane vulnerabilities. First action might be patching, isolating, compensating controls, or emergency monitoring—just don’t let it sit.

2) AI for anomaly detection during exploitation attempts

If an attacker tries to exploit OneView, you often won’t see a neat “malware alert.” You’ll see behaviors.

AI-driven anomaly detection is good at spotting patterns like:

• New processes spawned by the OneView service account that don’t match baseline
• Unexpected outbound connections from the management appliance to new IPs/domains
• Privilege escalations or shell execution chains atypical for OneView workflows
• Sudden API usage spikes (bulk read of inventory, templates, credentials, or config objects)

This matters because exploitation often looks like “normal admin automation,” except it happens at odd times, at odd volumes, or from odd sources.

3) AI as a guardrail for patch operations, not just alerts

Teams hesitate on upgrades because they fear downtime or breaking integrations. That’s reasonable—especially in infrastructure tooling. AI can help here in a less flashy way: change safety.

Examples that work well:

• Summarizing the environment impact: “These 12 server groups depend on this OneView instance.”
• Simulating policy and configuration drift: “These templates will change under version 11.00.”
• Detecting missed steps: “Hotfix applied pre-upgrade, but not re-applied after reimage.”

The point isn’t magic. It’s fewer human misses under pressure.

A practical response plan for CVE-2025-37164 (and similar flaws)

Answer first: Treat this as a control-plane incident until proven otherwise: patch fast, isolate early, and monitor the management appliance like it’s a domain controller.

Use the checklist below as a starting point. It’s intentionally opinionated.

Step 1: Confirm exposure and inventory (same day)

• Identify all OneView instances (virtual appliance, Synergy Composer variants)
• Record versions and whether they’re < 11.00
• Map who can reach them (admin subnets, VPN, jump hosts, partner networks)
• Verify external reachability isn’t accidentally enabled

If you can’t quickly answer “how many instances exist,” that’s already a risk indicator. AI-driven asset discovery helps because it learns infrastructure patterns (certs, banners, traffic flows) rather than relying on CMDB perfection.

Step 2: Apply the fix path that matches your operational reality

• If you can upgrade cleanly, move to OneView 11.00
• If you can’t upgrade immediately, apply the vendor hotfix for 5.20–10.20
• Document the reapply conditions (upgrades and reimaging) and create a hard verification step

One of the most common enterprise failures is “we patched, then we reimaged, and the patch disappeared.” Treat reimage events like patch rollbacks.

Step 3: Add temporary compensating controls (within 24 hours)

Even after patching, add speed bumps:

• Restrict access to OneView management interfaces to dedicated admin networks
• Require jump hosts and strong identity controls for administrative entry paths
• Reduce inbound exposure with firewall rules (default deny, explicit allow)

Segmentation is still the cheapest risk reduction you can buy.

Step 4: Monitor for exploitation-style behaviors (for 14–30 days)

Your detection goal isn’t “detect the CVE.” Your goal is “detect the attacker workflow.”

High-signal monitoring ideas:

• Unexpected process trees originating from OneView services
• Newly created scheduled tasks/cron jobs
• Outbound connections to unfamiliar destinations
• Bursts of API calls or configuration export actions
• Authentication anomalies: admin logins at unusual times or from unusual sources

AI-based detection helps because baselines are messy in enterprises. Static rules tend to either miss the weird stuff or flood you with noise.

The bigger lesson: patching is necessary, but it’s not a strategy

Answer first: The OneView case shows the limit of “patch-and-pray” when the vulnerable asset is mission-critical and complex to change.

A single CVSS 10.0 advisory can create a predictable chain of events:

1. Security flags it as critical.
2. Ops says upgrades are risky.
3. The org negotiates timelines.
4. Attackers scan faster than the org changes.

AI in cybersecurity is most valuable when it breaks that chain. Not by replacing people, but by:

• Reducing triage ambiguity (“this is the one to patch first”)
• Finding the forgotten instances (the test appliance nobody owns)
• Detecting exploitation attempts early (while patching is in progress)
• Preventing regression mistakes (hotfix not re-applied after upgrade/reimage)

If your security program still depends on perfect CMDB data, perfect change windows, and humans reading every advisory on time, you’re running a strategy that only works on paper.

What to do next (if you own enterprise infrastructure)

If your organization runs HPE OneView, treat CVE-2025-37164 as urgent until every instance is verified upgraded or hotfixed—and verified again after any reimage or upgrade action.

If you don’t run OneView, don’t ignore this. The pattern repeats across enterprise management tools: when control planes get hit, attackers move faster than manual operations. That’s why our AI in Cybersecurity series keeps coming back to the same theme: continuous detection + automated response beats “periodic review + best effort.”

If you had to answer one uncomfortable question before the year ends, make it this: Do we have enough visibility and automation to spot control-plane exploitation within minutes, not days?