AI Detection for DLL Sideloading Backdoors Like AshTag

Most organizations still treat DLL sideloading as a “malware analyst problem.” It isn’t. It’s a day-to-day detection problem for SOCs, and the WIRTE (aka Ashen Lepus) campaign is a clean example of why.

The operation, tracked targeting government and diplomatic entities across parts of the Middle East, uses a familiar pattern: believable lures, legitimate-looking binaries, staged loaders, and in-memory execution to keep disk artifacts thin. That combination is exactly what makes traditional “block known bad hash” defenses feel confident… right up until they miss the one variant that matters.

This post is part of our AI in Cybersecurity series, and I’m going to use the AshTag campaign as a practical case study: what the chain looks like, what defenders usually miss, and where AI-driven threat detection (behavioral analytics + anomaly detection + automated triage) can catch the attack early—often before the backdoor ever becomes “real” in your environment.

What the AshTag chain teaches defenders (answer first)

The lesson is simple: the attacker’s advantage is “normal-looking” execution paths, not exotic exploits. When a benign executable loads a malicious DLL from the same directory, your tooling needs to judge behavior and context, not just file reputation.

Based on reported observations of the campaign, the chain commonly looks like this:

1. Phishing lure tied to regional geopolitical themes.
2. A PDF decoy convinces the user to download a RAR archive from a file-sharing service.
3. The archive contains a renamed legitimate binary used for DLL sideloading.
4. That binary loads a malicious DLL (reported as AshenLoader), which keeps the user distracted by opening a decoy document.
5. The loader reaches out to infrastructure to fetch additional components, including:
   • a legitimate executable
   • a second DLL (reported as AshenStager) that’s sideloaded again
6. The final payload (reported as AshTag) runs in memory and behaves like a modular espionage backdoor.

If you only look for known malware signatures, this is the kind of chain that slides by—because each step borrows trust from something legitimate.

Why DLL sideloading works so often

DLL sideloading succeeds because Windows will happily load DLLs from certain locations based on search order rules. Attackers take advantage of this by:

• Shipping a signed or legitimate executable that your allowlists trust
• Dropping a malicious DLL with the expected name beside it
• Letting the OS do the rest

The defender’s mistake is assuming that “signed binary executed” implies “safe activity.” In reality, it can mean “trusted loader executed.”

AshTag as a real-world espionage playbook (answer first)

AshTag, as described by researchers, is built for persistence, remote command execution, and modular post-compromise actions—the standard toolkit for espionage. The important bit for defenders isn’t the exact module list; it’s the operational intent: quiet access over time.

Reported modules/capabilities align with what most SOCs see during long-dwell intrusions:

• Persistence and process management
• Update and removal functions (attackers maintain and prune)
• Screen capture
• File browsing and collection
• System fingerprinting

One observed hands-on tactic—staging documents in C:\Users\Public—is a classic “low drama” move. It avoids user profiles and looks like generic clutter. Then exfiltration reportedly occurred using Rclone, a legitimate tool that blends in because plenty of admins and developers use it.

A useful mental model: modern espionage is less about exotic malware and more about abusing normal tools in abnormal sequences.

Where AI-driven threat detection catches what rules miss (answer first)

AI in cybersecurity is most valuable when attackers stay inside the lines of “normal software.” DLL sideloading, staged loaders, and Rclone exfiltration are perfect examples—because the individual events are often not “malicious enough” to trip a rule.

Here’s what tends to work in practice: behavioral detection that scores sequences, not isolated alerts.

1) Detect the sideloading pattern, not the malware family

A strong analytic doesn’t need to know “AshenLoader.” It needs to spot:

• A legitimate binary executing from a user-writable directory (Downloads, Temp, AppData)
• The same process loading a DLL from its local folder (especially with an unusual compile time, no signature, or low prevalence)
• Immediate network beacons to rare domains/IPs

AI models (or well-designed ML-assisted analytics) can assign higher risk when these features co-occur. This matters because attackers routinely change:

• filenames
• archive structure
• C2 domains
• minor loader code

…but they often keep the same execution logic.

2) Use anomaly detection on “legitimate” tools (Rclone is the poster child)

Rclone is legitimate. So is powershell.exe. So are many signed vendor binaries.

AI-powered anomaly detection shines when it can answer: “Is this normal for us?”

Examples of high-signal anomalies:

• Rclone appearing on endpoints that never had it before
• Rclone making outbound connections to new destinations right after document staging
• Large outbound transfers from a diplomatic user workstation at unusual hours
• A new binary executed from a RAR extraction path followed by credential access attempts

This is where static allow/deny lists fail: the tool isn’t the problem; the context is.

3) Prioritize alerts with sequence-based scoring

Most SOC pain comes from alert floods, not lack of alerts.

A practical AI workflow is:

• Score events individually (low, medium, high)
• Then score chains (low events become urgent when connected)

For a sideloading campaign, the “chain score” spikes when you see:

• rar.exe or archive extraction → execution from the extracted directory
• signed binary loads unexpected DLL → network beacon → second stage execution
• memory-only .NET payload behavior (reflection, dynamic assembly load)

Even if each step is “medium,” the chain is “high.” This is the part many teams don’t have time to do manually.

Practical detections and controls you can deploy this quarter (answer first)

You don’t need a massive platform overhaul to get meaningfully better at stopping this type of attack. You need tighter telemetry, a few strong controls, and automation that reduces investigation time.

Minimum telemetry that makes sideloading visible

If you’re missing these, you’re largely blind:

• Process creation with command-line and parent/child relationships
• Module/DLL load events (at least for user-writable paths)
• Network connection telemetry (destination, port, timing, process attribution)
• File creation in user profile paths (Downloads/AppData/Temp)

If you can’t collect DLL loads everywhere, start with high-risk segments (executives, diplomats, finance, IT admins) and servers that host sensitive documents.

Controls that reduce the attacker’s room to maneuver

These are boring controls. They also work.

1. Block execution from common user-writable locations where feasible (or require higher trust). Focus on:
   • Downloads
   • AppData\Local\Temp
   • OneDrive sync folders used for file exchange
2. Constrain DLL search order abuse where possible (application control policies, packaged apps, hardened deployment paths).
3. Harden archive handling:
   • restrict RAR execution utilities where not needed
   • add warnings/quarantine for archives downloaded from the internet
4. Treat “dual-use” exfil tools as high-risk outside approved admin boxes:
   • Rclone
   • winscp.exe
   • curl.exe used in unusual contexts

A simple investigation playbook for suspected sideloading

When an alert fires, your analyst needs a fast checklist:

• What is the executable? Where did it run from?
• What DLLs did it load from the local directory?
• Was the DLL signed? What’s its prevalence in the org?
• What network destinations were contacted within 1–5 minutes of launch?
• Did the host show signs of staging (C:\Users\Public, temp folders, new archives)?
• Any unusual use of Rclone or other transfer tools after staging?

AI-assisted copilots can help here by summarizing the chain and highlighting “why this is weird,” but you still need the underlying data.

“People also ask” questions SOC teams raise (and straight answers)

Is DLL sideloading detectable without malware signatures?

Yes. Detect the mismatch: a trusted binary loading an untrusted DLL from a suspicious location, followed by network activity or staging behavior.

Why does in-memory malware matter for detection?

In-memory execution reduces disk artifacts and can evade file-based scanning. The tradeoff for attackers is they still leave process, module load, and network evidence—signals AI models can correlate quickly.

What’s the fastest win against campaigns like AshTag?

Stop treating “signed” as “safe.” Prioritize detections for:

• signed binaries executing from user-writable folders
• unexpected DLL loads
• low-prevalence network destinations right after launch

What to do next if you want earlier warning (answer first)

Campaigns like AshTag aren’t rare; they’re representative. The specific names will change. The tactics—sideloading, staged loaders, and living-off-the-land exfiltration—won’t.

If you’re building an AI in cybersecurity program for 2026 planning, I’d push for two concrete outcomes:

1. Behavior-first detection coverage for loader patterns (especially DLL sideloading and multi-stage execution)
2. AI-assisted triage that turns “ten medium alerts” into “one high-confidence incident” with a clear timeline

The question worth asking your team heading into the new year isn’t “Can we detect AshTag?” It’s: How quickly can we spot the next sideloaded backdoor before it starts collecting documents?