SamSam Ransomware: How AI Spots RDP Attacks Fast

Most companies still talk about ransomware like it starts with a bad email click. SamSam is the reminder that some of the costliest ransomware campaigns don’t need your users at all—they need your servers.

SamSam (also tracked as MSIL/Samas) became notorious for breaking in through exposed or poorly secured remote access, then encrypting entire networks. That “network-wide first” approach is exactly why it produced big ransoms: if operations stop everywhere at once, the pressure to pay spikes.

This post is part of our AI in Cybersecurity series, and SamSam is a useful case study because its playbook leaves patterns behind—login bursts, unusual admin activity, lateral movement—that AI-driven cybersecurity tools can detect faster than humans can triage them.

Why SamSam still matters in 2025

SamSam’s original wave peaked years ago, but the method is alive and thriving: compromise remote access, escalate privileges, encrypt at scale. If you’re defending a modern environment with hybrid identity, cloud VMs, and contractors logging in from everywhere, this style of attack is arguably easier to hide than the old “phishing attachment” model.

Two things make SamSam’s approach particularly relevant right now:

1. Remote access is everywhere. Even organizations that “don’t use RDP” often discover legacy servers, emergency jump boxes, vendor-supported systems, or cloud instances that quietly expose port 3389.
2. Credential theft has an economy. SamSam operators were observed buying stolen RDP credentials and moving fast—sometimes compromising networks within hours. That same marketplace dynamic now applies to VPN creds, SSO tokens, and session cookies.

Here’s the stance I’ll take: if your ransomware prevention strategy is mostly backups and user training, you’re under-defending the most common path into servers—stolen credentials and remote access abuse.

How SamSam gets in: the short version defenders need

SamSam’s success came from a simple sequence: get remote foothold, become admin, deploy broadly.

Initial access: RDP and exposed server services

Early reporting tied SamSam activity to exploitation of vulnerable server applications (notably JBoss). Later, many intrusions shifted to Remote Desktop Protocol (RDP) access—typically via:

• Brute-force password guessing against exposed RDP
• Stolen credentials purchased from illicit marketplaces

The frustrating part: RDP is an “approved access point.” That means the attacker’s entry doesn’t always look like malware. It looks like a login.

Privilege escalation and ransomware deployment

Once inside, the operators commonly:

• Escalate privileges to administrator
• Drop and execute ransomware payloads on servers
• Enumerate and encrypt reachable hosts (the “big payout” move)

This is where defenders often lose time. A traditional security stack may alert on a ransomware binary too late—after the attacker has already spread tooling, staged payloads, and positioned themselves to detonate across the domain.

Ransomware at scale isn’t a file problem. It’s an access problem that turns into a permissions problem.

Where AI helps: the hidden patterns SamSam can’t avoid

AI won’t magically stop ransomware. What it can do—when deployed sensibly—is reduce the window between “attacker logged in” and “attacker is encrypting everything.” That window is where SamSam lives.

AI for brute-force and credential-stuffing detection

The easiest SamSam entry path to spot is brute force against RDP. Even basic rules can catch it, but AI models tend to outperform simple thresholds because they can incorporate context:

• Baseline normal login frequency per account and host
• Learn typical source geographies/ASNs for admins and vendors
• Detect “low-and-slow” guessing that avoids fixed-rate thresholds

Practical example: A rule might alert on 20 failures in 5 minutes. An AI model might alert on a new source attempting 6 accounts across 3 servers with timing that matches human typing—something many static rules miss.

AI for anomaly detection in remote logins

SamSam-style intrusions often use valid credentials. That’s where anomaly detection earns its keep.

Signals that frequently show up in real incidents:

• First-ever RDP login for a user who normally uses VPN-only access
• RDP logins outside established work hours (especially during holidays)
• A vendor account logging into systems outside its normal scope
• Logins from a new device fingerprint or impossible travel patterns

Seasonal reality check (December): organizations run on skeleton crews, change freezes, and heavy contractor coverage. Attackers know this. AI-based monitoring that understands “normal for December weekends” is far more useful than blanket after-hours alerts that everyone ignores.

AI for privilege escalation and lateral movement

SamSam operators don’t stop at one server. They go hunting for admin.

AI-driven behavior analytics can flag sequences like:

• Remote login → rapid enumeration of domain assets
• Multiple authentication attempts across hosts (spray behavior)
• Sudden use of admin tools (psexec-like patterns, remote service creation, scheduled tasks)
• A standard user account initiating actions typical of IT automation

A helpful way to think about it: AI is good at detecting “workflow violations.” Attackers can mimic single events (a login), but it’s hard to mimic the full shape of legitimate IT work over time.

Automated containment: stopping spread before encryption

Detection without response is just expensive logging.

When the signals stack up, automated playbooks can:

• Temporarily isolate the host exhibiting suspicious remote admin activity
• Disable or step-up-auth the account (force re-auth, require MFA, rotate secrets)
• Block the offending source IP at the edge or firewall
• Kill suspicious remote execution chains and preserve forensic artifacts

The goal isn’t to auto-nuke accounts at the first anomaly. The goal is to buy time—minutes matter when ransomware is staging.

Hardening RDP and remote access (what actually works)

The CISA guidance around SamSam focuses heavily on reducing RDP exposure and improving operational hygiene. That advice has aged well.

Reduce the attack surface first

If RDP isn’t required, turn it off. If it is required, make it boringly hard to abuse:

• No public RDP exposure unless there’s a documented business need
• Put remote access behind a VPN or a zero-trust access proxy
• Restrict inbound access with firewall allowlists
• Segment: jump hosts should not have broad access to everything

If you’re running cloud virtual machines, treat this as non-negotiable: no open 3389 on public IPs except in tightly controlled scenarios.

Make stolen credentials less useful

SamSam proved how far a single purchased password can go.

Controls that reduce blast radius:

• Enforce strong passwords and account lockout policies (carefully tuned to avoid DoS)
• Require multi-factor authentication for remote access—especially for admins and vendors
• Use least privilege: admin rights should be time-bound and task-bound
• Rotate privileged credentials and remove shared accounts where possible

Logging that supports fast investigations

If you can’t reconstruct the story, you can’t respond well.

Minimum logging posture for SamSam-style threats:

• Capture successful and failed RDP logins
• Centralize logs and retain them for at least 90 days
• Alert on admin logins to servers that rarely receive interactive sessions
• Monitor creation of new services, scheduled tasks, and remote execution events

AI analytics gets dramatically better when the logs are complete and consistent. Garbage in still means garbage out.

A practical “AI + controls” blueprint for ransomware prevention

Security teams often ask what to implement first without boiling the ocean. Here’s a realistic blueprint that pairs classic controls with AI-driven detection.

Step 1: Inventory remote access and close the obvious gaps

Start with an uncomfortable scan:

• Which systems accept RDP?
• Which are reachable from the internet?
• Which accounts can log into them?

Fix the easy stuff immediately: close public exposure, remove unused access, and enforce MFA where it matters.

Step 2: Teach AI what “normal” looks like for your admins

Behavior models are only helpful if they’re anchored in reality.

Feed detections with context:

• Admin schedules and on-call rotations
• Known vendor IP ranges and maintenance windows
• Asset criticality (domain controllers and file servers get stricter thresholds)

This reduces alert fatigue and makes the high-confidence alerts stand out.

Step 3: Automate “safe” responses

Not every response should be fully automatic. But some actions are low-risk and high-value:

• Auto-block repeated brute-force sources
• Require step-up authentication on anomalous logins
• Quarantine endpoints showing encryption precursors (mass file renames, shadow copy deletion behavior, suspicious process trees)

A good rule: automate what you’d do at 2 a.m. when you’re half-asleep—the boring, repeatable actions.

Step 4: Measure time-to-detect and time-to-contain

If you want fewer ransomware incidents, track two numbers:

• MTTD (mean time to detect) suspicious remote access and privilege escalation
• MTTC (mean time to contain) the host/account before lateral spread

AI can help, but only if you’re operationalizing it with playbooks and ownership.

Quick Q&A (what security leaders usually ask)

“If we have good backups, are we fine?”

Backups are necessary, not sufficient. SamSam’s model pressures organizations by disrupting operations across many systems at once. If restoration takes days, the ransom demand becomes tempting.

“Is MFA enough to stop SamSam-style attacks?”

MFA dramatically helps, but it’s not a force field. You still need monitoring for compromised sessions, misconfigured bypasses, and privileged misuse. Pair MFA with anomaly detection and strong admin hygiene.

“What’s the single highest-impact fix?”

Closing public RDP exposure is the cleanest win. After that: MFA for remote access, and detection for abnormal admin behavior.

Next steps: build ransomware resistance where SamSam wins

SamSam ransomware is a case study in what attackers prefer: valid access, fast escalation, and network-wide impact. If you defend only against malicious attachments, you’ll miss the warning signs that show up hours earlier in authentication logs.

If you’re evaluating AI in cybersecurity for ransomware prevention, focus on use cases that map directly to SamSam’s playbook: detecting brute force, spotting anomalous remote logins, flagging privilege escalation chains, and triggering automated containment.

A good question to bring to your next security review is simple: If an attacker buys one set of credentials tonight, how quickly would we know—and how quickly could we stop them from reaching domain admin?