AI Detects Watering Hole Attacks Like ScanBox Fast

Most security teams still treat web browsing as “low risk” compared to email or endpoints. That’s exactly why watering hole attacks keep working—especially the kind that don’t need a traditional malware install.

In 2022, researchers reported a campaign attributed with moderate confidence to the China-based APT TA423 (also known as Red Ladon) that used targeted emails to lure victims to a fake news site. The site served ScanBox, a JavaScript-based reconnaissance framework that can fingerprint browsers and capture keystrokes without dropping a file to disk. The technique is old, but it remains effective because many defenses are built to stop executables, not hostile JavaScript running inside “normal” web sessions.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: watering hole detection is a perfect job for AI—not because AI is magic, but because these attacks generate patterns across DNS, HTTP behavior, JavaScript execution, and user flows that humans and static rules miss.

Watering hole attacks: why they’re still winning

A watering hole attack succeeds because it targets trust, not just technology. Instead of blasting malware at thousands of inboxes, the attacker compromises (or imitates) a site the victim is likely to visit and then waits.

In the TA423-style playbook described in research, the “trust anchor” was simple: a link that looked like it belonged to an Australian news outlet. Victims who clicked were redirected to a page that copied content from legitimate sources—while quietly loading ScanBox in the background.

Here’s the uncomfortable truth: even mature organizations often lack strong visibility into what executes inside the browser. Endpoint controls may never see a suspicious binary. Email security might only see a “news link.” And network security may log the session but not understand the intent.

Why fileless JavaScript reconnaissance is so effective

ScanBox highlights a broader class of threats: browser-native collection.

Because the code runs as JavaScript, it can:

• Collect browser fingerprinting data (OS, language, plugins, extensions)
• Capture user input (keylogging on targeted pages)
• Probe for communications paths (including WebRTC behaviors)
• Stage victims for follow-on intrusion (credential theft, MFA bypass attempts, tailored phishing)

The attack doesn’t need persistence on disk to be useful. It just needs a short window of execution to learn enough to sharpen the next stage.

ScanBox in plain English: what it does once the page loads

ScanBox is best understood as a modular reconnaissance toolkit delivered through a compromised (or attacker-controlled) web page. When a victim visits, the script executes and collects data that’s valuable for espionage—especially when the attacker is choosing targets carefully.

Stage 1: fingerprint the environment

First, the script inventories the browser and system.

Typical collection includes:

• OS and browser details
• Language and locale n- Installed plugins/extensions (historically even Flash version checks showed up in similar tooling)
• WebRTC availability and configuration hints

This matters because it tells an attacker how to tailor the next step. If they know your environment, they can choose exploits, lures, and payload formats that are more likely to work.

Stage 2: keylogging—without an “installed keylogger”

The scary part is also the simplest: if JavaScript can run on the page, it can capture keystrokes on that page.

That can include:

• Credentials typed into web forms
• Search terms and internal portal navigation
• Names, emails, and operational details typed into fields

Calling this “a keylogger” is accurate, but it misleads people into thinking it behaves like classic endpoint malware. It doesn’t. It behaves like an instrumented webpage.

Stage 3: reachability tricks (WebRTC + STUN)

Some ScanBox modules also explore reachability using WebRTC and STUN. At a high level, these technologies help real-time communications traverse NAT devices.

Why an attacker cares: it can help them learn about network positioning and connectivity and may enable communications strategies that bypass naive assumptions about “internal vs external.” Even when the victim is behind NAT, the attacker may still infer usable network details.

Where traditional defenses struggle (and why AI helps)

Rule-based security still has a place, but watering hole attacks stress it in predictable ways.

Problem #1: the initial page looks normal. A cloned news page with copied content often passes superficial checks. If you’re only blocking known-bad domains, you’re already behind.

Problem #2: JavaScript changes constantly. Attackers can mutate script structure, variable names, load paths, and timing to defeat static signatures.

Problem #3: the “signal” is spread across layers. The useful indicators appear across DNS, proxy logs, browser telemetry, and endpoint behavior—rarely in one place.

AI-based detection works well here because it’s designed to find behavioral patterns and correlations:

• A new domain that looks like a news site but has unusual hosting patterns
• A browsing flow with abnormal redirect chains
• Script execution that behaves like instrumentation rather than content
• Network beacons or exfil patterns that don’t match normal site analytics

Snippet-worthy truth: Watering hole defense fails when you hunt for “malware.” It succeeds when you detect malicious browsing behavior.

AI-driven detections that actually catch ScanBox-style campaigns

You don’t need a sci-fi SOC to get value from AI. You need a few practical detection surfaces and models that reduce noise.

1) Network anomaly detection for watering hole traffic

The most reliable early clue is often not the JavaScript itself—it’s the traffic shape.

AI can model “normal” for:

• DNS queries and new domain discovery rates
• Redirect depth and cross-domain hops
• Session timing (short dwell + immediate secondary calls)
• Unusual outbound POSTs after form interactions

Actionable detections to implement:

• Alert on first-seen domains that rapidly receive multiple visits from a specific department (e.g., maritime operations, legal, executive)
• Flag redirect chains that end in script-heavy pages with minimal user interaction
• Detect form-input-to-exfil timing, where a user types and the browser immediately sends structured data to a different host

2) Machine learning classification of malicious JavaScript behavior

Static signatures break fast. Behavior-based JavaScript analysis holds up longer.

A practical ML approach is to classify scripts based on features like:

• DOM event hooks consistent with keylogging (keydown, keypress, input listeners attached broadly)
• High-entropy string patterns consistent with obfuscation
• Unusual access to browser capability APIs (enumerating plugins/extensions, probing media devices, WebRTC objects)
• Network call patterns inconsistent with the site’s stated purpose (a “news site” calling endpoints that look like collection APIs)

You don’t have to fully deobfuscate everything. In my experience, flagging the intent (instrumentation + collection) is enough to trigger deeper inspection.

3) Automated reconnaissance detection before data theft

ScanBox is reconnaissance first. That’s good news: reconnaissance creates detectable “setup” behavior.

AI systems can surface:

• Browser fingerprinting bursts (a page that queries dozens of attributes rapidly)
• Extension enumeration patterns
• WebRTC/STUN behaviors that are unexpected for the site category

Then automation can do the boring-but-critical work fast:

1. Isolate the session (short-term browser containment)
2. Block the domain and related infrastructure indicators in proxy/DNS
3. Trigger credential hygiene workflows (rotate passwords, review SSO logs)
4. Queue the JavaScript sample for sandboxing and retro-hunting

That last step—retro-hunting—is where AI pays off again. Once you classify a script family, you can search for similar behaviors across weeks of logs.

A practical playbook: how to defend against ScanBox-style watering holes

Most teams don’t need more tools. They need a tighter loop between web telemetry and response.

Step 1: Treat the browser as an endpoint (because it is)

If you can’t see what executes in the browser, you’re blind to fileless threats.

Minimum viable improvements:

• Centralized proxy or secure web gateway logs with full URL paths
• Browser telemetry (enterprise browser management or endpoint telemetry that captures web script behaviors)
• DNS visibility that includes first-seen domain tracking

Step 2: Reduce exposure with a few blunt controls

These are unglamorous but effective:

• Block newly registered domains by default for high-risk teams (exec, finance, OT support, M&A)
• Enforce isolation for uncategorized “news/media” sites when accessed from sensitive networks
• Disable or tightly govern browser extensions (extensions are a huge fingerprinting and persistence surface)

Step 3: Use AI to prioritize what humans should look at

Analysts burn out when every weird redirect becomes a ticket. AI should shrink the pile.

Prioritization signals that work:

• Targeting patterns (who received the lure? which teams visited?)
• Repeated infrastructure reuse (shared TLS traits, hosting overlap, recurring script behaviors)
• High-confidence behavior matches (keylogging hooks + obfuscation + cross-domain posts)

Step 4: Assume reconnaissance leads to follow-on intrusion

Once you confirm a watering hole event, respond like it’s the first chapter, not the whole story.

Do this within 24–48 hours:

• Review SSO, email, and VPN logs for abnormal access from new geos/devices
• Look for targeted spear-phishing waves tied to the same users
• Check for suspicious OAuth consent grants and mailbox rules
• Hunt for repeated visits to the same domain from other devices

People also ask: quick answers for leaders

Is ScanBox “malware” if nothing is installed?

Operationally, yes—it’s malicious code. Technically, it’s often fileless and runs inside the browser, which helps it evade controls that focus on executables.

Why would an APT use a fake news site?

Because it lowers suspicion and improves click-through rates in targeted communities. It also gives attackers a believable reason to request “cooperation” or “user research.”

Can AI stop watering hole attacks automatically?

AI can detect and triage them earlier than manual methods, then automation can block domains, isolate sessions, and kick off incident workflows. Full “auto-stop” depends on your web controls and response maturity.

Where this fits in the AI in Cybersecurity series (and what to do next)

The TA423/ScanBox case is a clean example of why AI threat detection matters: modern espionage often starts with subtle web-based reconnaissance that looks harmless in isolation. AI is strong at connecting the dots across browsing, DNS, and script behavior—fast enough to interrupt the sequence before credential theft or deeper compromise.

If you’re building an AI-assisted SOC program for 2026, put watering hole attack detection on your shortlist. It’s a high-leverage area: the attacker’s cost is low, your exposure is broad, and the signals are measurable.

Want a practical next step? Pick one high-risk group (executives, OT-adjacent engineers, or anyone working with sensitive partners) and pilot AI-driven web anomaly detection with clear outcomes: fewer first-seen domain incidents, faster time-to-block, and tighter correlation from web events to identity risk. What would your incident metrics look like if reconnaissance attempts were contained in minutes instead of days?