AI-Powered EDR: Detecting Storm-0249 Style Evasion

Most security teams still talk about EDR like it’s the final boss. Storm-0249 treats it like a prop.

ReliaQuest recently highlighted a pattern that should make every SOC pause: an initial access broker (IAB) using legitimate EDR components, signed binaries, and everyday Windows utilities to stay quiet after compromise. Instead of trying to “beat” endpoint tools head-on, the actor blends into the processes defenders trust the most.

This is exactly where AI in cybersecurity earns its budget. Not by “replacing analysts,” but by spotting the subtle, cross-signal weirdness that human eyes and signature-based controls miss—especially when attackers never drop a clean, scannable file.

Why Storm-0249 is a case study in modern EDR bypass

Storm-0249’s playbook matters because it reflects a bigger shift: attackers are optimizing for precision and invisibility, not volume. If your defenses are tuned to catch noisy phishing and commodity malware, you’ll detect the old version of this actor—and miss the new one.

Two characteristics make this actor a useful case study for defenders:

1. They’re abusing what’s already allowed. EDR platforms, Windows Installer (msiexec), PowerShell, and curl.exe are all common in enterprise environments. Blocking them outright is usually unrealistic.
2. They’re creating “trust collisions.” A malicious DLL sitting next to a legitimate executable is not a weird concept anymore. What’s different is the selection of targets—including components that look like part of your security stack.

Here’s the uncomfortable truth: many environments still rely on a security model that assumes “trusted tools do trusted things.” Threat actors have moved on.

The IAB angle: why this evolves fast

Storm-0249 isn’t just breaking in—it’s brokering access to other criminals, including ransomware groups. That changes the economics.

An IAB’s job is to get reliable footholds with minimal friction. The more stealthy and repeatable the access method, the more valuable it becomes to downstream operators. That’s why ReliaQuest’s observation about “newcomer energy” is important: new actors can innovate quickly, and those techniques spread fast across the ecosystem.

How the attack works (and why it’s hard to see)

The defensive lesson isn’t “watch for Storm-0249.” It’s “watch for the patterns Storm-0249 is proving will work.”

Below are the core tactics described in the report, translated into what your detections should care about.

ClickFix social engineering: the user becomes the execution pipeline

A tactic known as ClickFix convinces a user to paste a “helpful” command into the Windows Run box. The command looks harmless. The user believes they’re fixing a Microsoft issue.

But the command’s real job is to pull down a spoofed support installer from a phishing site impersonating a legitimate support portal.

Defender takeaway: if your control strategy assumes “we can stop malware downloads,” ClickFix sidesteps it by turning the user into a trusted launcher.

MSI + SYSTEM privilege: a quiet escalation path hiding in plain sight

Once the installer launches, it abuses Windows Installer behavior to run with SYSTEM-level privileges. That matters because SYSTEM can:

• Write into protected locations
• Execute payloads with maximum local privileges
• Install persistence mechanisms that survive reboots and user logouts

Defender takeaway: many teams log process creation but don’t treat MSI execution chains as high-risk unless a known-bad hash appears. That’s a gap.

Trojanized DLL sideloading: “trusted binary” becomes the loader

Inside the MSI, Storm-0249 reportedly uses a Trojanized DLL that masquerades as a legitimate component of an EDR product (notably, a SentinelOne-like DLL naming pattern).

The trick is classic DLL sideloading:

• A legitimate executable (brought by the attacker) is placed beside a malicious DLL
• When the executable runs, Windows DLL search order leads it to load the attacker’s DLL
• The malicious code runs under the cover of a legitimate, trusted process

Defender takeaway: this isn’t about one vendor. The technique is adaptable because it targets how Windows loads libraries, not a specific EDR weakness.

“Living off the land” with curl + fileless PowerShell

In observed attacks, the actor uses built-in Windows tools such as curl.exe to fetch PowerShell scripts from URLs that look like they originate from Microsoft.

Then it pipes the script directly into PowerShell memory—fileless execution—so there’s no obvious artifact on disk.

Defender takeaway: plenty of enterprises still treat PowerShell logging as optional or “nice to have.” It’s not. It’s the difference between seeing the crime and guessing.

Where traditional EDR falls short (and what AI detection changes)

EDR is still essential—but the common operational model around EDR is the real problem.

Traditional detection stacks tend to overweight:

• Known-bad hashes n- Known-bad domains
• Known malware families
• Static rules for “high confidence” behaviors

Storm-0249 leans into the blind spots:

• In-memory execution means fewer file indicators
• Signed binaries and trusted processes reduce suspicion
• Normal admin tooling makes malicious activity look like IT work
• AppData and registry hives can hide persistence in places many teams don’t monitor deeply

AI-driven threat detection helps when it’s used for what it’s good at: behavioral context at scale.

What AI can actually detect here

A practical AI model (or a strong ML-assisted analytics layer) can:

• Baseline “normal” DLL load paths per host group, then flag anomalies
• Detect rare parent/child process chains (e.g., explorer.exe → msiexec.exe → unusual unsigned DLL loads)
• Correlate endpoint + DNS + identity events into one storyline
• Spot low-and-slow patterns that don’t trip single-rule thresholds

A sentence I use internally when evaluating tools: If the only thing it’s good at is known IOCs, it’s a threat intel feed—not detection.

The behavioral signals that matter most

If you’re building detections (or evaluating AI-powered EDR/XDR), prioritize signals that map to this attack style:

• DLL sideloading indicators

  • DLL load from AppData, Downloads, Temp, or user-writable paths
  • A signed binary loading an unsigned DLL from a non-standard directory
  • New DLL appearing adjacent to an executable that doesn’t normally live there

• Installer abuse signals

  • MSI execution from user-writable directories
  • Unusual msiexec.exe command-line arguments
  • MSI spawning PowerShell or script engines

• Fileless execution signals

  • PowerShell with -EncodedCommand, IEX, or pipeline execution patterns
  • Script block logging events that include remote retrieval and immediate execution

• Suspicious “normal tool” usage

  • curl.exe or bitsadmin downloading to unusual locations
  • Command lines that fetch content and pipe directly to interpreters

• DNS/infra signals

  • New domains (commonly flagged as “< 90 days old”)
  • Domains that mimic trusted brands but resolve to unusual hosting patterns

AI makes these signals usable because it can score them together. Individually, they’re often “maybe.” Collectively, they’re a near-guarantee.

Defensive playbook: controls that work without breaking IT

Blocking PowerShell everywhere is the kind of advice that sounds good until you try to run a business. The goal is constraint + visibility, not fantasy.

Here’s a realistic set of moves that reduces exposure to Storm-0249-style tradecraft.

1) Put PowerShell on a leash

If you do one thing this quarter, do this.

• Enable deep PowerShell logging (module + script block) in high-risk tiers
• Adopt PowerShell Constrained Language Mode where possible
• Prefer signed scripts and controlled execution policies for admin endpoints
• Alert on memory-only execution patterns and suspicious command-line primitives

2) Treat AppData as a high-risk zone

AppData is where attackers hide because defenders accept it as “user stuff.” Change that assumption.

• Monitor execution from user-writable directories (AppData, Temp, Downloads)
• Alert on new binaries/DLLs appearing next to executables that are not installed via standard software distribution
• Hunt for persistence in registry run keys and scheduled tasks pointing to user paths

3) Build detections for DLL load anomalies, not just malware

DLL sideloading is common enough that signature-only detection loses.

• Baseline expected DLL load paths per application category
• Alert on signed processes loading unsigned DLLs from user-writable paths
• Use allowlists carefully: allow where and how a binary runs, not just that it runs

4) Harden installer execution

Windows Installer isn’t the villain, but it’s frequently the vehicle.

• Alert on MSI execution initiated by user interaction in unexpected contexts
• Restrict MSI installs to approved software channels for standard users
• Monitor msiexec.exe spawning scripting engines or network activity

5) Add DNS monitoring that’s designed for response, not dashboards

Teams often collect DNS and never operationalize it.

• Alert on newly registered domains contacting endpoints with no browsing history
• Correlate DNS anomalies with endpoint process telemetry (what process made the call?)
• Quarantine endpoints automatically when high-confidence correlations occur

6) Use AI for triage and storyline building (where it actually helps)

The best use of AI in the SOC is speeding up the path from “weird event” to “confirmed incident.”

Look for capabilities like:

• Automated event clustering into incidents
• Natural-language investigation summaries grounded in telemetry
• Confidence scoring that accounts for environment-specific baselines
• Recommended response playbooks tied to observed behaviors

If your AI feature can’t show its work (the events and reasoning), it becomes noise fast.

“People also ask” questions SOC teams raise about this tactic

Does abusing EDR processes mean EDR is failing?

No. It means attackers are exploiting how defenders operationalize EDR. EDR still provides crucial telemetry; the miss usually happens in analytics, correlation, and response speed.

Should we swap EDR vendors to stop this?

Switching vendors won’t fix a Windows-native technique like DLL sideloading or fileless PowerShell. Better wins come from behavioral detections, hardening, and baselining—ideally assisted by ML analytics.

What’s the fastest way to reduce risk in 30 days?

Turn on (or improve) PowerShell logging, monitor execution from user-writable directories, and implement DNS age-based alerting with endpoint correlation. Those three moves catch a lot of stealthy activity quickly.

Where this fits in the AI in Cybersecurity series

This Storm-0249 case is a clean reminder of what modern attackers optimize for: they don’t need zero-days if they can hide inside your “normal.” That’s why AI-powered EDR and XDR strategies keep showing up in mature security programs—they’re designed to detect behavior that looks legitimate in isolation.

If your current posture depends on signatures and a handful of static rules, you’re going to keep losing time to threats that are already inside the house.

A useful next step is to pressure-test your environment with a simple question: If an attacker ran PowerShell filelessly and hid a malicious DLL in AppData next to a signed executable, would we see it in minutes—or in next month’s incident report?