NanoRemote Shows Why AI Must Monitor Cloud APIs

Most defenders still treat cloud apps as “safe” traffic. NANOREMOTE is a good reminder that attackers love hiding inside the tools your business already trusts.

Elastic Security Labs recently described NANOREMOTE, a Windows backdoor that uses the Google Drive API for command-and-control (C2)—not just for exfiltration, but as an ongoing control channel for tasking, file staging, and data theft. If your security visibility is built around blocking suspicious domains and scanning obvious malware callbacks, this is the kind of threat that slips through.

This post is part of our AI in Cybersecurity series, and I’m going to take a clear stance: API-based C2 is now common enough that “cloud allowlists” are a liability. You need AI-driven detection that understands behavior—who is calling what API, from where, how often, and with what data patterns—because the network indicators defenders relied on for a decade don’t show up the same way.

What makes NANOREMOTE different (and why it works)

NANOREMOTE’s advantage is simple: it blends into legitimate enterprise traffic.

A lot of security programs still assume C2 looks like:

• A sketchy domain registered yesterday
• A weird TLS fingerprint
• A suspicious IP range
• A beaconing pattern to infrastructure you can block at the firewall

NANOREMOTE flips that model by pushing core operator activity into Google Drive API calls. In practical terms, that means:

• Commands and results can be exchanged through a mainstream SaaS platform
• Payloads can be staged in cloud storage that’s already approved
• Data theft can look like normal file sync behavior unless you’re measuring it carefully

Elastic’s analysis also notes code similarity to FINALDRAFT, another implant that used Microsoft Graph API for C2. Different cloud, same tactic: use “normal” business APIs as the control plane.

The “trusted channel” problem in enterprise security

The more your organization depends on Google Workspace, Microsoft 365, Slack, Atlassian, or similar services, the bigger your trusted-channel exposure becomes.

Traditional controls often do this:

• Allow outbound traffic to major cloud services by default
• Inspect less (or not at all) because TLS decryption is limited
• Log the activity, but without context to decide what’s risky

Attackers aren’t guessing this will work. They’re designing malware around it.

How Google Drive API C2 changes detection

Using cloud APIs for C2 changes what “good detection” looks like. The win for defenders is that cloud APIs are structured and logged. The loss is that many teams don’t have the analytics maturity to spot misuse.

Elastic reports NANOREMOTE includes a task management system for:

• Queuing uploads/downloads
• Pausing/resuming transfers
• Canceling transfers
• Generating refresh tokens

That set of features matters because it signals operator intent: this isn’t a smash-and-grab infostealer. It’s a fully featured backdoor built for sustained access.

Behavioral signals that matter more than domains

When malware uses Google Drive as a control channel, the question isn’t “Is Drive allowed?” It’s:

• Which identity is calling the API?
• From which device and network location?
• At what frequency, and does it match the user’s normal work pattern?
• What is being uploaded/downloaded (sizes, types, timing)?
• Is token behavior suspicious (new refresh token generation, unusual OAuth scope usage)?

Those are hard questions to answer with static rules alone.

Where AI-driven cybersecurity fits (and why rules won’t keep up)

AI helps here for the same reason it helps with fraud: the meaningful signals are scattered and context-dependent.

A good AI-based threat detection approach can connect dots across:

• Endpoint behavior (processes, memory injection, persistence)
• Identity events (OAuth consent, token refresh, sign-ins)
• SaaS telemetry (Drive API actions, sharing events, file activity)
• Network metadata (beacon timing, unusual egress patterns)

The goal isn’t “detect Google Drive usage.” The goal is “detect malicious Google Drive usage.”

What AI can detect earlier in an API-based attack chain

For a threat like NANOREMOTE, earlier detection often comes from small inconsistencies that humans don’t have time to triage:

1. A loader pretending to be a security product component (the report describes a loader mimicking a Bitdefender crash handler name)
2. A new or rare process tree spawning network-aware activity
3. Drive API activity from a device that never uses Drive APIs programmatically
4. Automation-like cadence (regular intervals, off-hours bursts)
5. Large outbound transfers that don’t match typical document collaboration

AI shines when it’s trained (or tuned) to your environment’s baseline. In most companies I’ve worked with, “normal” is surprisingly consistent once you measure it.

Practical stance: don’t bet your security on allowlists

If your policy is effectively:

• Google Drive = trusted
• Microsoft 365 = trusted
• Everything else = suspicious

…then you’ve created an attacker roadmap.

The reality? Your most trusted services are now prime hiding spots.

A defender’s checklist: detecting and stopping cloud API abuse

Here’s what works when you want to catch malware that abuses cloud APIs—without turning your SOC into a false-positive factory.

1) Instrument the three planes: endpoint, identity, SaaS

If you only look at one layer, you’ll miss the story.

• Endpoint: capture process creation, command lines, module loads, and suspicious encryption/compression behavior
• Identity: monitor OAuth grants, token refresh patterns, anomalous sign-ins, and conditional access outcomes
• SaaS (Google Workspace / Drive): log Drive API calls, file operations, sharing changes, and unusual client IDs

A key point: API C2 is still C2. It leaves rhythms, retries, and operational fingerprints.

2) Build “normal” for file transfer behavior

Create baselines for:

• Typical upload/download volume per user/device
• Typical file size ranges
• Typical hours of activity
• Typical destinations (shared drives, personal drives, external shares)

Then alert on clear outliers, such as:

• A workstation that suddenly uploads hundreds of MB at 3 a.m.
• A user who has never used Drive APIs programmatically suddenly generating tokens
• Rapid upload/download sequences that resemble staging more than collaboration

3) Treat refresh tokens and OAuth scopes as high-value signals

NANOREMOTE’s ability to generate refresh tokens is not an implementation detail—it’s a persistence strategy.

Strong controls include:

• Restricting OAuth app consent (especially in enterprises)
• Reviewing high-risk scopes regularly
• Detecting new, rare, or suspicious OAuth client IDs
• Expiring tokens aggressively when compromise is suspected

4) Correlate compression/encryption + outbound API traffic

Elastic notes NANOREMOTE uses JSON over HTTP, with Zlib compression and AES-CBC encryption for its internal request/response data.

Even when payloads are encrypted, endpoints may expose patterns like:

• Repeated compression calls before network activity
• Unusual crypto library usage in processes that shouldn’t need it
• Consistent request sizes or timing patterns

AI-powered endpoint analytics can score these combinations far faster than manual threat hunting.

5) Prepare an “API C2” incident playbook

When cloud APIs are involved, incident response needs to be ready to move beyond isolating a PC.

Include steps for:

• Revoking tokens (user tokens and suspicious OAuth app tokens)
• Rotating credentials and enforcing MFA re-registration if needed
• Searching SaaS logs for staged payloads (files created, renamed, shared)
• Identifying other endpoints that used the same client IDs or patterns

If you don’t have this playbook, you’ll contain the host and leave the attacker’s SaaS foothold intact.

What this says about threat evolution heading into 2026

NANOREMOTE is part of a broader pattern: attackers are building operations around legitimate platforms—not because it’s clever, but because it’s efficient.

Cloud services provide:

• High availability
• Global infrastructure
• Built-in encryption
• “Normal-looking” traffic
• Easy storage and staging

Defenders aren’t helpless here. The advantage defenders have is visibility—if you set it up.

A useful rule: when the attacker’s infrastructure disappears into SaaS, your detection must shift from indicators to intent.

That’s also why this story belongs in an AI in Cybersecurity series. The telemetry volume is too high for humans to connect reliably, but it’s perfect for machine learning models that specialize in anomaly detection, sequence analysis, and entity behavior analytics.

What to do next if you’re worried about malware using Google Drive API

If you want an immediate, practical next step, do this exercise internally:

1. List all sanctioned cloud apps where outbound traffic is broadly allowed (Drive, Graph, Slack, etc.)
2. Identify what logs you actually collect for them (not what you could collect)
3. Measure how many endpoints ever use those APIs programmatically
4. Add detections for “rare API usage from a device/user” and “abnormal transfer volume”

Then pressure-test it: would your current monitoring spot a workstation that starts staging files to Drive every 15 minutes?

If the honest answer is “probably not,” you’re not alone. Most companies are in that camp.

NANOREMOTE’s lesson is straightforward: API-based attacks are now a mainstream technique, and AI-driven threat detection is the most practical way to keep up. The next wave won’t announce itself with a suspicious domain—so your defenses shouldn’t depend on one.