Stop Authentication Coercion With AI-Driven Detection

A single Windows box can be “told” to authenticate to an attacker—no phishing email, no fake login page, no malware required on the target server. That’s the uncomfortable reality behind authentication coercion, and it’s showing up again in 2025 in a more slippery form: attackers are shifting away from the famous coercion tricks defenders already watch (like old print spooler favorites) and into rare RPC functions that almost nobody monitors.

Most companies get this wrong. They treat coercion as a list of one-off exploits to patch and signatures to match. Attackers treat it as a pattern: force a machine (often a Tier 0 asset like a Domain Controller) to authenticate outward, capture the material needed for NTLM relay or credential abuse, then pivot into AD CS and domain takeover.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: AI-driven behavioral detection is the most practical way to keep up with evolving coercion paths, because you’re not going to win a whack-a-mole contest across hundreds of RPC interfaces and opnums.

Authentication coercion: the “your PC betrays you” attack

Authentication coercion is when an attacker forces a Windows machine to initiate an authentication attempt to an attacker-controlled system. The attacker doesn’t need your password; they need your machine to “helpfully” authenticate the way Windows often does when it believes it’s accessing a legitimate remote resource.

Here’s the chain defenders keep seeing:

• An internal machine gets compromised (phishing, stolen creds, exposed service—pick your poison).
• The attacker sends RPC requests that include a UNC path (like a file or remote resource reference).
• The target machine tries to access that path and auto-authenticates, often via NTLM.
• The attacker captures what they need (hashes / challenge-response) and attempts relay, privilege escalation, or lateral movement.

The scary part is the permission model: many RPC calls are reachable by standard domain users. So the “cost” to attempt coercion can be low, while the upside can be “full domain compromise.”

Why this keeps working in mature environments

The teams getting hit aren’t necessarily sloppy. Coercion thrives because:

• RPC is everywhere in Windows and Active Directory environments.
• Many environments still have NTLM in play for legacy reasons.
• Security controls often focus on known tools and known RPC methods.
• Coercion can look like “normal Windows admin activity”… until you add context.

That last point—context—is where AI and analytics earn their keep.

RPC is the real attack surface (not just PrintNightmare)

RPC (Remote Procedure Call) is the backbone for lots of Windows services, and it exposes a huge number of callable methods. Each method is identified by an interface plus an operation number (opnum). Some of those methods accept parameters like UNC paths, which is where coercion attackers love to play.

Defenders remember the hits:

• Print spooler-related coercion methods (commonly associated with PrintNightmare-era techniques)
• EFSRPC coercion (commonly associated with PetitPotam)
• DFS namespace coercion (often called DFSCoerce)

But attackers have adapted. Public research has cataloged 16 working coercion-capable functions across five protocols, and researchers have also noted hundreds more RPC functions remain untested. That’s the point: the “coercion vulnerability” isn’t always a single CVE—it’s often abuse of legitimate behavior.

The new trend: “rare opnums” that bypass tool-focused detections

Many security programs tuned detections around popular coercion tools and their usual RPC calls. That worked… for a while.

Now we’re seeing attackers choose rarely used interfaces because:

• EDR rules and detections may be tuned to the “popular” opnums.
• SOC analysts are less likely to investigate a protocol nobody uses day-to-day.
• Traditional detections don’t baseline “normal RPC behavior” per asset.

A practical takeaway: you can’t defend coercion by memorizing a few RPC method names. You need a strategy that detects coercion as a behavior, even when the exact opnum is new.

Case study: coercion via remote event log (MS-EVEN)

A real intrusion pattern from 2025 used an obscure RPC interface to coerce authentication—specifically the EventLog Remoting protocol (MS-EVEN), using an operation associated with opening a backup event log on a remote system.

What made it stand out wasn’t just the function call. It was the surrounding behavior:

• The protocol/opnum combination was rare in that environment (effectively “nobody uses this”).
• The coerced target was asked to reach an external IP address.
• A compromised internal host attempted authentication broadly against critical servers in a short window, then pivoted into coercion.

The attack trajectory followed an all-too-familiar escalation path:

1. Compromise an internal machine.
2. Trigger coerced authentications to many high-value systems (Domain Controllers, RODCs, RADIUS, Citrix).
3. Capture NTLM material and attempt NTLM relay.
4. Target certificate services (AD CS) using relayed machine account authentication.
5. Attempt domain-level actions (including DCSync-style objectives).

This is why security teams feel like they’re constantly late: by the time a human recognizes the pattern, the attacker is already chaining steps.

Where AI fits: catching coercion as an anomaly, not a signature

AI-driven threat detection helps with authentication coercion because coercion is easier to spot as “weird behavior” than as a static indicator. The attacker can change opnums. They can switch protocols. They can rotate tools. But they still need your environment to do unusual things.

Here’s what works in practice: treat RPC and authentication coercion like a behavioral anomaly problem.

What an AI model should be watching (high signal features)

Security teams often ask, “What do we feed the model?” Start with these features because they create strong context with relatively low ambiguity:

• RPC calls with UNC path parameters
  • Especially when the UNC resolves to an unusual host, a new subnet, or an external IP.
• Rare interface GUID + opnum combinations
  • “Rare” is environment-specific. Your baseline matters more than a generic list.
• Source-to-destination weirdness
  • A user workstation initiating remote RPC calls to Tier 0 assets is almost always suspicious.
• Burst patterns
  • One host making coercion-like RPC calls to 10+ systems in minutes is not “admin work.”
• Authentication outcomes and protocol choice
  • Spikes in NTLM attempts (especially failures) followed by coerced outbound authentication attempts.

A useful one-liner for your SOC runbook:

Coercion is usually a burst of rare RPC plus an authentication path that doesn’t match the asset’s job.

Why “baseline first” beats “block everything”

It’s tempting to say, “Just disable NTLM,” or “Just block RPC.” In real enterprises and government networks, that’s rarely possible quickly—especially in December, when change windows are tight and downtime tolerance is low.

AI gives you a workable middle path:

• Build baselines per asset role (Domain Controller vs Citrix vs file server vs workstation).
• Alert on deviations, not just known-bad calls.
• Prioritize incidents where multiple signals stack (rare opnum + external UNC + Tier 0 target + burst behavior).

This approach scales because it doesn’t assume you already know tomorrow’s coercion opnum.

A practical defense plan (detection + hardening)

The best defense against authentication coercion combines hardening that reduces relay value and detection that spots coercion attempts early. Here’s a plan I’ve seen succeed without turning the network into a change-management war zone.

Step 1: Reduce relay payoff

Coercion often exists to enable NTLM relay. So make relay harder:

• Enforce SMB signing where feasible across the domain.
• Deploy Extended Protection for Authentication (EPA) where it protects the right services.
• Review where NTLM is still required and start shrinking it intentionally (don’t do it blindly).

If you run AD CS, treat it as a high-risk relay target. Many real-world coercion chains aim straight at certificate abuse.

Step 2: Shrink the RPC attack surface (selectively)

You don’t need every RPC-based service everywhere:

• Disable the Print Spooler on Domain Controllers (still a classic move).
• Disable remote event log access where it’s not required—especially on Tier 0 assets.
• Disable other unused RPC services aligned to asset purpose (file server VSS agent, DFS features, etc.).

This is less about a perfect checklist and more about an opinionated posture: Tier 0 should be boring.

Step 3: Detect coercion generically (what to log and alert on)

Monitoring RPC is noisy, so the trick is filtering toward meaningful anomalies:

• Collect RPC metadata: interface GUID, opnum, named pipe, source/dest, and parameters where possible.
• Flag RPC calls that include UNC paths—then enrich those with reputation and “seen before” context.
• Alert when:
  • a host uses a rare RPC interface/opnum for the first time in 30 days
  • a workstation targets Tier 0 assets via remote RPC
  • the UNC points to an unusual or external destination
  • the same source repeats across many targets (spray behavior)

Step 4: Automate the response (where AI turns into leads)

This is where automated security operations matter. When coercion is suspected, speed wins:

• Isolate the initiating host (or at minimum restrict outbound SMB/RPC temporarily).
• Reset or rotate exposed credentials and assess machine account risk.
• Hunt for follow-on activity: NTLM relay attempts, AD CS certificate requests, privilege escalation artifacts.
• Snapshot volatile data for incident response before you reimage.

If your SOC needs three human approvals to isolate a box, attackers will finish the chain first.

Quick Q&A your team will ask anyway

“If we patch known coercion CVEs, are we safe?”

No. Patching helps, but many coercion paths are abuse of intended behavior through rarely monitored RPC functions.

“Can we just block all outbound authentication?”

You can reduce it, but Windows networks need legitimate outbound auth. The realistic goal is least privilege for network paths plus detection on abnormal auth destinations.

“Why does AI matter here specifically?”

Because coercion evolves faster than rule-writing. AI helps by flagging rare RPC usage, unusual auth paths, and attack chaining across endpoints, identity, and network telemetry.

What to do next if you want to get ahead of coercion

Authentication coercion is evolving because defenders made the obvious paths expensive. Attackers responded by picking weirder RPC methods and aiming them at the systems that matter most—Domain Controllers, RODCs, and certificate services.

If you’re responsible for AD security in 2025, here’s the standard you should hold your program to: can you detect and contain a coercion attempt in minutes, not hours, even if the opnum is unfamiliar? That’s the bar. And it’s why AI in cybersecurity isn’t hype here—it’s how you keep pace.

If you want a practical starting point, pick one high-value segment (Tier 0), baseline its normal RPC and authentication behavior, then automate response actions for the top coercion indicators. Once you see what “normal” looks like, coercion stands out fast.

What would your environment show if a single workstation suddenly started making rare RPC calls to ten servers, each with a UNC path pointing somewhere it’s never pointed before?