Fortinet Vulnerabilities: AI Defense for Active Attacks

Most companies still treat security appliances as “the thing that protects us,” not “the thing that can break and get used against us.” That mindset is exactly why critical Fortinet flaws under active attack is such a nasty headline: when the perimeter tool becomes the entry point, detection and response have to happen fast—and often from the inside.

The source article wasn’t accessible due to a verification wall, but the core signal is clear: attackers are actively exploiting critical Fortinet vulnerabilities in the wild. When exploitation is already happening, the window for comfortable patching is gone. You’re in triage mode.

This post is part of our AI in Cybersecurity series, and I’ll take a stance: AI is most valuable here not as “magic prevention,” but as a way to compress the time between exploitation and containment. If you run FortiGate, FortiOS, FortiProxy, FortiManager, or you simply have Fortinet in your network path, the playbook below is built for speed.

What “active attack” means for Fortinet environments

Active attack means someone is exploiting these flaws right now, not merely scanning. That changes priorities: you don’t start with perfect remediation plans; you start with what reduces risk in hours.

Fortinet vulnerabilities typically become high-impact fast because devices are:

• Internet-facing by design (VPN, firewall, web portal, admin interfaces)
• Widely deployed and standardized, so one exploit chain scales
• High-privilege footholds once compromised (routing, policy, credentials, traffic inspection)

Why perimeter products are a favorite target

Attackers love perimeter gear for one simple reason: one successful exploit can bypass months of endpoint hardening. If a threat actor can land on a firewall/VPN device, they can often:

• Harvest credentials or session tokens
• Create stealthy tunnels and pivot internally
• Tamper with logs or disable security controls
• Blend into “normal” network flows (because the device is expected to talk to everything)

If you’ve ever looked at a SOC queue during an appliance compromise, you know the pain: the logs are noisy, the device is “trusted,” and the blast radius is hard to bound.

The uncomfortable reality: patching alone won’t save you

Patching is mandatory, but it’s not a detection strategy. When exploitation is already underway, patching stops new exploitation—yet it doesn’t tell you whether you were already hit.

There are three common failure modes I see when teams rely on patching as the whole answer:

1. Patch lag: change windows, approvals, remote sites, HA pairs—real life stretches “48 hours” into “3 weeks.”
2. Unknown exposure: you may not know which devices are internet-reachable (shadow IT, forgotten VIPs, legacy NAT rules).
3. Post-exploit persistence: attackers can drop accounts, SSH keys, cron jobs, or modify configs before you patch.

So treat this as two parallel workstreams:

• Stop the bleeding (reduce attack surface and patch fast)
• Prove you’re clean (hunt for exploitation and persistence)

Where AI helps most: detection and response at network speed

AI in cybersecurity shines when you need to detect weak signals across messy network data—fast. Fortinet exploitation often creates patterns that are visible if you correlate enough telemetry, but easy to miss if you look at one log source at a time.

Here’s the practical way to think about it: traditional rules are good at “known bad.” AI is good at “this is weird for your environment.” You want both.

AI-driven anomaly detection that actually maps to Fortinet attacks

AI/ML approaches can flag behaviors that frequently accompany exploitation of perimeter devices:

• Rare admin actions: configuration exports, firmware actions, new admin users, policy edits outside change windows
• Impossible travel for admins: management access from new geos/ASNs, especially to admin portals
• VPN oddities: sudden spikes in failed logins, new user agents, unusual session durations, or logins at unusual hours
• East-west pivot signals: a firewall suddenly initiating connections to internal servers it never talks to
• Data staging: bursts of outbound traffic from the appliance or from internal systems soon after a device event

A solid model doesn’t just say “anomaly.” It should answer:

• What changed?
• How rare is it in our baseline?
• What other events co-occurred?

If your tool can’t show the reasoning chain, it’s not helpful during an active incident.

LLMs in the SOC: faster triage, fewer dropped threads

Used carefully, LLMs can reduce response time in three high-friction areas:

1. Log normalization and summarization: turning thousands of FortiGate/FortiOS events into a human-readable timeline.
2. Alert clustering: grouping related signals (VPN login anomalies + admin changes + new outbound connections) into one incident.
3. Playbook guidance: generating step-by-step response checklists based on your environment’s tooling and past incidents.

The win isn’t that the model “finds the exploit.” The win is fewer missed connections when everything is happening at once.

A perimeter-device incident is a time-compression problem. AI is how you buy back minutes and hours.

A 24-hour response plan for suspected Fortinet exploitation

The goal in the first 24 hours is containment and clarity. You’re trying to answer two questions: (1) Are we exposed? (2) Are we already compromised?

Hour 0–4: reduce exposure immediately

Start with actions that are low-risk and high-impact:

• Restrict management access to allowlisted IPs/VPN only (no open admin interfaces).
• Disable unused services on the device (especially anything internet-facing you don’t need).
• Review external exposure: confirm what VIPs/NAT rules/port forwards exist for Fortinet services.
• Enable or increase logging (to SIEM if possible) for admin events, VPN events, and configuration changes.

AI assist: Use anomaly detection to produce a “last 7 days of abnormal admin activity” report, prioritized by rarity and privilege level.

Hour 4–12: patch, but don’t destroy evidence

Patch quickly, but do it with discipline:

• Confirm the vulnerable versions and affected components across all sites.
• Patch HA pairs carefully (avoid creating a window where failover behavior hides the attacker’s activity).
• Snapshot configs and logs first if you have the ability—especially if you suspect compromise.

AI assist: Use LLM-based runbooks to ensure responders don’t skip the “pre-patch evidence capture” steps when the pressure is high.

Hour 12–24: hunt for compromise and persistence

This is where teams often stop too early. Don’t.

Hunting checklist (adapt to your Fortinet stack):

• New admin accounts, changed admin passwords, or modified auth settings (RADIUS/TACACS/LDAP)
• Unexpected policy changes (new allow rules, NAT changes, new address objects)
• New VPN users/groups, altered portal settings, suspicious split-tunnel changes
• Unusual DNS requests or outbound connections from the appliance itself
• Log gaps or logging config changes
• Internal lateral movement shortly after suspicious appliance events

AI assist: Correlate firewall/VPN logs with:

• Identity logs (IdP, AD) for credential misuse
• EDR events for new admin tools, remote exec, or credential dumping
• Network telemetry (NetFlow/NDR) for unusual east-west scans and new outbound beacons

The highest-signal story is usually a sequence: external access → admin/config change → internal pivot → data access. AI is good at spotting those sequences across systems.

Preventing the next Fortinet scramble: build “exploit readiness”

If exploitation headlines keep happening, your plan can’t start on patch day. You need repeatable controls that assume perimeter devices will eventually have a bad week.

Control 1: continuous asset and exposure inventory

You can’t protect what you can’t name. The minimum standard:

• A current list of Fortinet devices, versions, and enabled services
• A map of which devices are internet-reachable and through what ports
• Owners for each device (so patching doesn’t stall in inbox purgatory)

AI helps by continuously reconciling inventories from CMDB + network scans + cloud configs, then flagging drift.

Control 2: “golden” baselines for configs and behavior

Most orgs baseline configs (maybe). Fewer baseline behavior. Do both:

• Config baselines: alert on changes to admin users, auth methods, logging, VPN portals, and policies
• Behavioral baselines: what management IPs normally administer devices, typical change windows, typical outbound destinations

An ML baseline paired with strict rules is a strong combo: rules catch known bad; ML catches “new weird.”

Control 3: automated containment you can trust

If you can’t contain quickly, detection is just anxiety with graphs.

Good automation targets:

• Temporarily lock down management access on anomaly (allowlist-only)
• Disable risky services when exploit indicators spike
• Force re-auth / rotate credentials for impacted admin identities
• Quarantine VPN sessions that match impossible-travel + unusual device fingerprints

I’m opinionated here: start with reversible automations (time-limited blocks, approval gates) so you don’t brick your network during a false positive.

People also ask: practical Fortinet + AI questions

Can AI prevent a zero-day on Fortinet devices?

AI won’t “prevent” a true zero-day by itself. What it can do reliably is detect exploitation behavior early (odd admin changes, new outbound connections, unusual VPN behavior) and trigger containment before the attacker spreads.

What logs matter most during active exploitation?

Prioritize:

• Admin login and admin action logs
• Configuration change logs
• VPN authentication and session logs
• Traffic logs for outbound connections from the appliance and unusual internal pivots

If you only collect generic traffic logs, you’ll miss the “who changed what” story.

What’s the fastest way to reduce risk if we can’t patch today?

Restrict management interfaces, remove unnecessary internet exposure, and increase monitoring around admin/config actions. Then create a hard patch deadline measured in hours, not weeks.

Next steps: turn this incident into a repeatable playbook

Critical Fortinet flaws under active attack are a reminder that perimeter security is now an uptime-and-detection problem, not just a “strong firewall” problem. The teams that do well are the ones that can answer, quickly and confidently: Are we exposed, and are we already compromised?

If you want AI in cybersecurity to generate leads and value inside your org, start here: pick one high-risk perimeter product (Fortinet is an obvious candidate), wire its telemetry into your detection stack, baseline normal behavior, and automate a small set of safe containment actions. You’ll feel the difference the next time an “active exploitation” alert hits your inbox.

Where would AI-driven anomaly detection help you most right now—internet-facing VPN access, admin/config changes, or lateral movement after initial access?