AI-Driven CVE Prioritization: September 2025 Playbook

Most vulnerability programs don’t fail because teams don’t patch. They fail because teams patch the wrong things first.

September 2025 is a clean example of why. Recorded Future’s Insikt Group flagged 16 high-impact vulnerabilities to prioritize—down from 18 in August—and 11 were rated “Very Critical.” The month wasn’t “more CVEs than we can handle.” It was a smaller set of flaws with a familiar pattern: internet-facing edge devices, common weakness types, and real malware campaigns riding exploits into production environments.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: AI isn’t optional anymore for vulnerability prioritization. Not because AI is trendy—because attackers already operate with automation, and defenders who still triage by CVSS, spreadsheets, and weekly meetings are fighting with one hand tied.

What September 2025 really says about risk

September’s “CVE landscape” isn’t just a list of scary IDs. It tells you where the next incident report is likely to start.

Here are the signals worth acting on.

The vendor concentration is the story

Cisco and TP-Link accounted for 6 of the 16 prioritized vulnerabilities. That concentration matters because it points to two high-leverage attack surfaces:

• Network edge / VPN / firewall exposure (Cisco IOS, IOS XE, Secure Firewall/ASA)
• Commodity router exposure (TP-Link models with auth bypass, command injection, and missing auth)

If you’re defending an enterprise, you already know the ugly truth: edge devices are the new “server fleet.” They sit in front of everything, they’re hard to monitor deeply, and compromise often means silent persistence.

Weakness patterns repeat (and that’s an advantage)

Insikt Group observed common weakness types in September:

• CWE-502: Deserialization of Untrusted Data (most common)
• CWE-77 / CWE-78: Command injection
• CWE-862 / CWE-863 / CWE-290 / CWE-306: Auth and authorization failures
• Buffer overflows: CWE-120 / CWE-121

That repetition is good news for defenders—if you use it. AI-based security analytics can learn patterns across:

• exploit preconditions (internet-facing, auth required vs not)
• affected product families (VPN stacks, admin panels, CMS platforms)
• exploit “shape” (deserialization chains, command injection primitives)
• attacker behaviors that follow (credential dumping, tunneling, lateral movement)

Humans notice patterns too, but humans don’t notice them fast enough across thousands of CVEs, PoCs, asset inventories, and threat reports.

Public PoCs change the clock speed

Insikt Group identified public proof-of-concept exploits for 6 of the 16 vulnerabilities and created 6 Nuclei templates (including for Sitecore CVE-2025-53690 and Adminer CVE-2021-21311).

When PoCs drop, the timeline compresses:

1. Researchers publish technical details
2. PoCs appear and get copied
3. Commodity scanning starts
4. Your internet-facing instances get tested

AI helps here in a very practical way: monitor PoC emergence and exploit chatter, then auto-adjust your remediation queue the same day, not after the next change advisory board meeting.

Two exploitation stories that should change your patch order

September included multiple “patch-this-first” moments. Two stand out because they show how exploitation actually plays out.

Cisco ASA: when a CVE becomes a persistence platform

Threat actors exploited CVE-2025-20333 and CVE-2025-20362 in legacy Cisco ASA 5500-X devices (notably systems without secure boot and with VPN web services enabled) to deploy:

• RayInitiator (multi-stage bootkit)
• LINE VIPER (modular shellcode)

The operational lesson isn’t “apply the patch” (though yes, apply it). The lesson is:

If attackers get code execution on the VPN edge, they’ll invest in persistence that outlives reboots and upgrades.

This campaign included techniques like boot-path hooking and kernel/user-space handoffs. That’s not smash-and-grab. That’s “we plan to be here for a while.”

Where AI fits in:

• Asset intelligence + exposure mapping: identify every ASA/FTD instance with WebVPN enabled and match firmware/build metadata.
• Anomaly detection: baseline outbound traffic from edge devices and alert on unusual destinations, protocols, or bursts.
• Behavioral correlation: connect “odd WebVPN patterns + log suppression + new outbound channels” into a single incident hypothesis instead of three unconnected alerts.

If you’ve ever tried to do that correlation manually across VPN logs, firewall telemetry, and endpoint data… you know why automation matters.

Sitecore ViewState deserialization: old secrets, modern intrusions

CVE-2025-53690 (Sitecore XM/XP/XC) is a deserialization issue that became exploitable in real environments because of one very human failure mode: static secrets reused from old deployment guidance.

Attackers targeted a legitimate endpoint (/sitecore/blocked.aspx) that accepts ViewState. The exploitation signal included application log artifacts consistent with forged ViewState validation failures. The payload delivered a .NET assembly (tracked as WEEPSTEEL) for host discovery, then moved quickly into tunneling and AD reconnaissance.

The post-exploitation toolkit described included:

• reverse tunneling (EARTHWORM)
• persistent remote access tooling (DWAgent)
• AD mapping (SharpHound)
• credential and hive dumping attempts

Here’s the security program lesson:

A “critical CVE” isn’t always what breaks you. Sometimes it’s a critical CVE plus a stale configuration decision from 2017.

Where AI fits in:

• Secret-scanning + config drift detection: flag reused machine keys, plaintext secrets, and risky legacy patterns before an attacker does.
• Log analytics: detect patterns like repeated ViewState failures tied to suspicious POST bursts.
• Attack-path reasoning: connect web app exploitation to “new admin accounts + tunneling + SharpHound execution” as a coherent kill chain.

AI doesn’t replace hardening. It makes hardening measurable, continuous, and prioritized.

The September pattern: RCE + SSRF + auth failures = fast compromise

Seven of the sixteen vulnerabilities enabled remote code execution or command injection, affecting products from Cisco, TP-Link, Sitecore, Fortra, Libraesva, and Dassault Systèmes.

That mix matters because it covers three “incident accelerants”:

1) RCE/command injection: the shortest route to hands-on-keyboard

When attackers can execute arbitrary code, they skip a lot of steps. AI-driven prioritization should treat reachable RCE as a top-tier class—especially when it’s:

• internet-facing
• on edge infrastructure (VPN, firewall, routers)
• paired with public PoCs
• mentioned in government/industry exploitation advisories

2) SSRF: the quiet way into cloud credentials and internal services

CVE-2021-21311 in Adminer is “only” SSRF, but SSRF is one of those bugs that becomes devastating depending on where it lives.

If a vulnerable Adminer instance can reach internal HTTP services, SSRF becomes:

• a cloud metadata credential theft path
• an internal service enumeration tool
• sometimes an admin action proxy against internal APIs

Insikt Group noted 9,651 exposed Adminer instances observed by internet scanning at the time of writing. Even if a fraction are vulnerable, the scanning volume will be relentless.

AI-driven detection angle: watch for Adminer endpoints making unusual outbound requests or redirect-follow chains, then auto-escalate that signal when it lines up with known SSRF behaviors.

3) Auth/authorization flaws: the “this shouldn’t be reachable” problem

Missing auth and incomplete authorization checks show up repeatedly (Cisco and TP-Link prominently). These issues are perfect for attacker automation because they often:

• don’t require complex payloads
• can be tested quickly at scale
• lead to privileged functionality exposure

This is exactly the type of pattern a mature AI security posture should treat as “likely-to-be-exploited,” even before full exploitation reporting catches up.

A practical AI workflow to prioritize CVEs (without hand-waving)

AI-based vulnerability management works when it produces a daily, defensible patch queue tied to your environment—not a prettier dashboard.

Here’s a workflow I’ve found actually holds up in real operations.

Step 1: Build a “reachable exposure” graph

Start with one non-negotiable: a CVE on an unreachable asset is not the same risk as the same CVE on an internet-facing asset.

Have your system continuously map:

• internet-facing services (VPN, admin portals, management interfaces)
• product fingerprinting (versions, banners, firmware where possible)
• identity context (is auth required? what kind?)
• segmentation context (what can this system reach?)

AI helps by reconciling messy asset inventories and spotting “unknown unknowns” exposed to the internet.

Step 2: Score exploitation likelihood, not just severity

September’s report highlights why this matters: many “Very Critical” calls are driven by exploitation signals such as:

• KEV catalog inclusion
• observed malware campaigns
• public PoCs or templates
• technical writeups that enable weaponization

A good AI model weights these signals and updates them continuously.

CVSS tells you how bad it could be. Exploitation likelihood tells you how soon it’ll matter.

Step 3: Auto-generate “patch-first” cohorts

Instead of one long list, create cohorts your team can execute:

• Cohort A (24–72 hours): edge RCE/auth bypass with exploitation evidence
• Cohort B (7 days): internet-facing app RCE/SSRF with PoCs and exposure
• Cohort C (30 days): internal-only vulnerabilities with mitigations available

AI can place assets into cohorts automatically based on exposure + exploit intel + business criticality.

Step 4: Close the loop with detection engineering

When patching takes time, detection buys you time.

Use AI-assisted security analytics to propose:

• log queries for known exploit patterns (for example, suspicious ViewState failures)
• anomaly baselines for VPN appliances and routers
• correlations for post-exploitation behaviors (new admin accounts, tunneling tools, AD discovery)

Then measure outcomes: did alerts fire, did they produce noise, did patching reduce attack surface?

What to do next (December 2025 reality check)

By December, many September flaws are no longer “new.” That’s exactly the danger. Attackers love the window where defenders assume, “Surely everyone patched by now.” Plenty didn’t—especially for legacy edge gear and third-party platforms.

If you want a concrete next step, run this three-part check this week:

1. Inventory your internet-facing edge and admin surfaces (VPN, firewall portals, router management, CMS admin paths).
2. Prioritize anything that matches September’s pattern: edge exposure + RCE/auth bypass + public exploit signals.
3. Harden the “secret layer”: rotate static keys, remove sample configs, encrypt sensitive config values, and alert on config drift.

AI in cybersecurity earns its keep when it turns a monthly CVE landscape into a daily decision system: what to patch first, what to monitor right now, and what legacy assets need to be retired instead of “managed.”

If your vulnerability queue still looks like a flat list sorted by severity, the real question isn’t whether you can patch faster—it’s whether you can decide faster when the next Cisco- or CMS-shaped story breaks.