AI Detection for DNS Hijacking: Stop Silent Reroutes

DNS infrastructure hijacking is one of the cleanest ways to compromise an organization without “breaking in” the way defenders expect. Change a few DNS records, and suddenly your customers, employees, and partners are talking to the attacker’s servers—often over valid HTTPS with a real certificate. No scary browser warning. No obvious outage. Just quiet interception.

CISA flagged this pattern back in 2019: attackers used compromised credentials to alter DNS records (A, MX, NS), redirected web and email traffic, and then obtained legitimate TLS certificates for the victim domain to enable man-in-the-middle (MitM) access. That advisory is archived now, but the technique is evergreen—and it’s showing up in modern campaigns because it scales, it’s profitable, and it bypasses a lot of perimeter thinking.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: DNS hijacking is a detection problem first, not a firewall problem. The fastest way to catch it is to treat DNS changes, certificate issuance, and traffic shifts as one correlated story—something AI is well suited to do.

DNS infrastructure hijacking: what actually happens

At its core, DNS infrastructure hijacking is simple: an attacker gets the ability to change records that control where your services live. The impact is anything but simple.

CISA’s advisory describes a common flow:

1. Credential compromise: the attacker obtains credentials for an account that can modify DNS records (often at a registrar, DNS hosting provider, or internal DNS management platform).
2. Record manipulation: they change records like:
   • A / AAAA (web apps, APIs)
   • MX (email routing)
   • NS (delegation—often the most damaging)
3. Certificate acquisition: because they control DNS answers, they can satisfy domain validation checks and request valid TLS certificates for your domain.
4. Interception and persistence: they can proxy traffic, harvest credentials, inspect mail, or keep access even after you revert records by exploiting cached DNS, residual certs, or lingering trust.

Here’s the uncomfortable truth: a “successful” DNS hijack can look like normal operations—users still reach a website, email still “works,” and monitoring may not alert if it’s only checking availability.

Why this attack is hard to spot with traditional monitoring

Most organizations monitor DNS like it’s plumbing: as long as it isn’t clogged, it’s fine. That leaves three big blind spots:

• Configuration drift: DNS record changes may be “expected” and get lost among routine updates.
• Legitimate encryption: valid certificates remove the classic red flag of TLS errors.
• Distributed evidence: the registrar sees the login, IT sees “some DNS change,” SOC sees a weird auth event, and no one connects the dots fast enough.

AI’s advantage is correlation: it can stitch together small “not-quite-an-incident” signals into one high-confidence narrative.

The risks CISA called out still matter in 2025

The original advisory focused on global campaigns affecting multiple sectors, including government-related infrastructure. That’s still relevant, but the environment has changed in ways that make DNS hijacking even more attractive:

• Cloud and SaaS sprawl: more DNS records, more providers, more change events.
• Remote administration: registrar/DNS management is often done from anywhere, increasing exposure to credential theft.
• Short-lived certificates and automation: certificate issuance is faster and more automated than it used to be, which is great for defenders—until attackers use the same machinery.
• Holiday season pressure: it’s mid-December. Teams are understaffed, change freezes are partial, and attackers know response times slow down.

A single hijacked MX record can siphon password resets and invoices. A swapped A record can harvest SSO credentials at scale. An altered NS record can hand over the keys to the kingdom.

Snippet-worthy reality check: If an attacker can change your DNS, they can make your users trust the wrong system while everything “looks encrypted.”

Where AI helps: detecting DNS hijacking before it becomes a breach

AI doesn’t “solve” DNS hijacking. It gives you earlier detection, better prioritization, and faster containment by focusing on patterns humans miss.

1) AI for DNS change anomaly detection

The best early warning is often the DNS change event itself.

AI models (or even well-designed ML-assisted rules) can score DNS changes based on features like:

• Who made the change (account, role, historical behavior)
• Where the change came from (geo, ASN, device fingerprint, time-of-day)
• What changed (record type, TTL, target IP ranges)
• How risky the new destination is (new hosting provider, newly seen IP, mismatch with known asset inventory)
• Whether it’s consistent with deployment patterns (e.g., you usually update api records during CI/CD windows)

Practical example: If your organization typically updates A records from a small set of corporate IPs during business hours, and suddenly a registrar admin logs in from a new location at 2:13 a.m. and changes MX to an unfamiliar network, that’s not “a change.” That’s an incident until proven otherwise.

2) AI that correlates DNS changes with certificate issuance

CISA highlighted a key move: attackers obtain valid encryption certificates for the victim domain. That step is detection gold—if you’re watching.

AI is useful here because certificate signals are noisy at scale. A good approach is to correlate:

• DNS record modifications (especially A, CNAME, NS)
• New certificates observed for your domains (including subdomains)
• First-seen hosting destinations for those domains

What you want to catch is the “triangle”:

• DNS changed → new cert appears → traffic shifts

Even if each event alone doesn’t trip a high-severity alert, the combination should.

3) AI for traffic behavior shifts (the “silent reroute”)

Once traffic is redirected, you’ll often see subtle changes:

• Increased authentication failures (because the attacker’s proxy mishandles sessions)
• New TLS fingerprints or reverse proxy behaviors
• Unexpected HTTP headers or content length patterns
• Email delivery anomalies (SPF/DKIM alignment changes, new relay patterns)

AI models that baseline “normal” for web and mail flows can identify these deltas quickly—especially when paired with the DNS and cert signals above.

4) AI for identity-based detection at the registrar and DNS admin layer

DNS hijacking usually starts as account takeover. That’s identity territory.

If you’re using AI-driven user and entity behavior analytics (UEBA), focus on registrar/DNS admin accounts:

• Rarely used accounts suddenly active
• Admin logins from new devices
• Password resets followed by privileged actions
• “First time ever” actions like changing NS records

Treat DNS admin identities like you treat domain admin: high signal, low tolerance.

A practical defense plan (with and without AI)

You don’t need a huge AI budget to materially reduce risk. You need tight controls and smart monitoring, then use AI to scale what works.

Hardening steps CISA recommended—still the right baseline

CISA’s mitigation list holds up because it targets the real weakness: control of DNS changes.

• Reset passwords for all accounts that can change DNS records
• Enforce multifactor authentication (MFA) on registrar and DNS management accounts
• Audit public DNS to ensure records resolve where you intend
• Find and revoke fraudulent certificates issued for your domains

If you do nothing else, do MFA and eliminate shared admin accounts. Most companies get this wrong.

Add the controls that make DNS hijacking harder to pull off

To move from “baseline” to “resilient,” add:

1. Registry lock / registrar lock where feasible for high-value domains
2. Role-based access control: separate “can edit DNS” from “can change NS” from “can transfer domain”
3. Change approval workflow for NS and MX updates (two-person rule)
4. Short, intentional TTL strategy for critical records (so you can recover faster) paired with alerting when TTL is changed unusually
5. Central inventory of authorized DNS targets (known IP ranges, known SaaS endpoints)

AI becomes far more effective when you’ve defined what “authorized” means.

An AI-assisted detection checklist you can implement in weeks

Here’s what works in the real world when teams are busy:

• Stream DNS change logs (registrar + DNS provider + internal DNS management) into your SIEM
• Build an anomaly score for DNS changes using:
  • new user/device/location
  • record type sensitivity (NS > MX > A/CNAME)
  • destination reputation / first-seen
  • abnormal TTL change
• Monitor certificate appearance for your apex domain and critical subdomains and alert on:
  • first-seen issuer for that domain
  • unexpected certificate volume spikes
  • certs for “never-used” subdomains
• Correlate with web/email telemetry:
  • spikes in login failures
  • changes in email routing patterns
  • new proxy/TLS fingerprints

The goal isn’t perfect prediction. It’s fast certainty when an attacker starts turning knobs.

“People also ask” style answers your team will need

How do attackers get a valid certificate for my domain?

They control DNS answers long enough to pass domain validation (for example, by serving the expected response during validation). Once issued, the certificate looks legitimate to browsers and many monitoring tools.

Which DNS records are most dangerous to change?

NS is the most dangerous because it can delegate your entire domain to attacker-controlled name servers. MX is high impact for email interception. A/CNAME are common for web and API redirection.

What’s the fastest sign you’re being hijacked?

A DNS change you didn’t schedule from an identity that doesn’t usually make DNS changes, especially when it targets NS or MX. The second fastest sign is a new certificate for a domain that rarely changes.

What to do next if you suspect DNS hijacking

Speed matters more than elegance.

1. Lock down registrar access: reset passwords, revoke sessions, enforce MFA, restrict IP access if possible.
2. Revert DNS changes and review the full change history (don’t stop at the last edit).
3. Hunt for fraudulent certificates and revoke them; update certificate transparency monitoring and internal allowlists.
4. Assume credential theft: rotate admin credentials and investigate initial access (phishing, infostealer logs, reused passwords).
5. Validate downstream impact: check email forwarding rules, SSO logs, OAuth app consents, and helpdesk password reset activity.

If you’re running AI-assisted monitoring, feed the incident back into your models and rules. The whole point is getting better every time.

DNS hijacking is a perfect use case for AI in cybersecurity

DNS infrastructure hijacking sits at the intersection of identity, configuration, and network behavior. Humans can investigate it well, but humans are slow at correlating five separate “small anomalies” across three tools.

AI is strong where teams struggle: spotting abnormal patterns in DNS configuration changes, correlating them with certificate activity, and confirming impact through traffic behavior. That’s how you catch the attack before customers start entering passwords into the wrong site.

If you want a single north-star metric for 2026 planning, use this: time-to-detect DNS changes that affect user trust (NS/MX/A) and time-to-confirm certificate misuse. Shrink those two numbers, and DNS hijacking becomes a containable incident instead of a headline.

What would your team see first if your MX records were changed tonight—an alert, or a helpdesk ticket in the morning?