Detect Malware C2 Hiding in Google Drive APIs

Most companies still treat Google Drive traffic as “safe by default.” That assumption is exactly what makes API-based command-and-control so effective.

Researchers recently disclosed NANOREMOTE, a fully featured Windows backdoor that uses the Google Drive API for command-and-control (C2). The idea is blunt: if defenders allow cloud collaboration tools everywhere, attackers can blend in by using the same trusted rails. Elastic Security Labs also noted code similarities to another implant, FINALDRAFT (aka Squidoor), which uses the Microsoft Graph API for C2. Different cloud. Same playbook.

This post is part of our “AI in Cybersecurity” series, and I’m going to take a stance: traditional detection alone won’t keep up with “living-off-trusted-APIs” malware. You need AI-driven detection that understands behavior, not just indicators, plus automated response that can move at machine speed.

Why malware is moving C2 into “trusted” cloud APIs

Answer first: Attackers use Google Drive (and similar APIs) for C2 because it looks like normal business traffic, survives network controls, and reduces attacker infrastructure.

“Trust” becomes camouflage

Security teams often allow Google Drive and Microsoft 365 broadly because the business demands it. That creates three advantages for an attacker:

1. Egress is already permitted. Many orgs tightly restrict unknown outbound traffic but allow drive.googleapis.com and related endpoints.
2. Traffic patterns look routine. Sync clients, browser access, third-party apps—Drive traffic is noisy, which gives malware cover.
3. Attacker infrastructure shrinks. If your C2 lives in a commodity cloud platform, defenders can’t just block an IP and move on.

If you’re relying mostly on domain blocklists, proxy category filters, or “known bad” IPs, you’re playing defense in the wrong decade.

This isn’t new—what’s new is how operational it’s become

Cloud-based C2 has been discussed for years, but the practical maturity is what’s changing. Tools like NANOREMOTE and FINALDRAFT suggest adversaries are standardizing a pattern:

• Use a legitimate API as the transport layer
• Store commands in objects (files, metadata, comments, descriptions)
• Poll on an interval that mimics human or app behavior
• Exfiltrate in small chunks that resemble document sync

The implication: detection has to shift from “where it connects” to “what it does once connected.”

How a Google Drive API C2 channel typically works (and what to watch)

Answer first: Drive-based C2 usually boils down to polling, retrieving a command blob from Drive, executing locally, then writing results back to Drive.

Because the RSS summary doesn’t include full technical detail, here’s the most common architecture defenders should expect when malware weaponizes Google Drive:

1) Initial setup: OAuth and API access

To talk to the Google Drive API, software typically needs an OAuth token (or another authorized mechanism). Malware can obtain access in a few ways:

• Abuse an embedded client ID/secret controlled by the attacker
• Steal browser/session tokens from the endpoint
• Trick a user into granting consent (less common for pure backdoors, more common for phishing apps)

Defender note: OAuth consent and token handling is where identity meets endpoint security. If your cloud team and endpoint team don’t share telemetry, this will slip through.

2) Command staging inside Drive

Attackers need a place to put instructions. Common staging options:

• A file in a specific folder the malware knows to check
• A file with a predictable name pattern (e.g., host ID + timestamp)
• Data hidden in file metadata (description fields, properties)

What to watch:

• Unusual Drive file operations from endpoints that don’t normally automate Drive
• Repeated access to a single small file that changes frequently
• File creation patterns that correlate tightly with endpoint execution events

3) Polling behavior that blends in

Polling is the heartbeat of many backdoors. In Drive-based C2, malware may:

• Poll every N minutes (often jittered)
• Use Drive “list” queries to check for new commands
• Fetch content only when something changes

What to watch:

• A workstation that makes high-frequency Drive API calls without corresponding user Drive activity (no UI/browser actions)
• Activity during unusual times (e.g., midnight local time) that repeats day after day
• Consistent, machine-like periodicity across multiple hosts

4) Output and exfiltration via uploads

Once a command runs (system info, file listing, credential dumping attempts, screenshot capture, lateral movement checks), results can be written back as:

• New files uploaded to Drive
• Updates to an existing “results” file
• Chunks split across multiple small uploads to avoid size thresholds

What to watch:

• A non-Drive-heavy endpoint uploading many small files
• Uploads that coincide with suspicious child processes (PowerShell, cmd.exe, rundll32, scripting engines)

Why signature-based tools struggle against API-based C2

Answer first: When malware uses legitimate APIs, there’s no “obviously malicious” domain, certificate, or protocol to match—so signatures go blind.

Here’s the painful truth: a lot of mature security stacks still assume that C2 is detectable via known bad infrastructure. That assumption breaks when C2 is:

• A legitimate cloud provider
• TLS-encrypted with standard cipher suites
• Using official SDKs and normal HTTP methods

Even endpoint-only detection can struggle if it’s overly dependent on static indicators (hashes, known strings, simple YARA hits). Backdoors like NANOREMOTE are built to be operational, meaning:

• They can rotate artifacts
• They can adjust sleep intervals
• They can update themselves
• They can avoid loud tooling

This is where AI-driven threat detection earns its keep: not by “detecting Google Drive,” but by detecting malicious intent expressed through behavior.

Where AI-driven detection catches what humans miss

Answer first: AI is strong at correlating weak signals—endpoint process behavior, identity context, and cloud API patterns—into a high-confidence alert.

Security teams don’t lose to one missed indicator. They lose to a thousand low-grade events that never get stitched together.

Behavioral analytics across endpoint + cloud

A good AI detection approach treats this as a cross-domain story:

• Endpoint: suspicious process tree, persistence attempts, privilege escalation, credential access
• Network: repeated Drive API calls with unusual cadence
• Identity: tokens created/refreshed in odd contexts, unusual app IDs, anomalous consent
• Cloud: atypical Drive operations (list/get/upload) for that user/device

On their own, each signal can look harmless. Together, they form a clear narrative: “This endpoint is using Google Drive like a bot, not a person.”

Anomaly detection that’s actually useful

Anomaly detection gets a bad reputation because teams deploy it without guardrails. What works in practice is scoped baselining:

• Baseline Drive API behavior per role (finance laptop vs. developer workstation)
• Baseline per device (managed endpoint vs. BYOD)
• Baseline per app (known sync clients vs. unknown automation)

Then alert on anomalies that matter:

• New Drive API client usage on a host with no prior automation footprint
• Polling loops consistent with beaconing
• Upload bursts right after suspicious local execution

Automated response: speed matters more than perfect certainty

Once a Drive-based backdoor has an interactive channel, time is the enemy. I’ve found teams get the best outcomes by automating “contain first, investigate fast” actions such as:

• Isolate the endpoint from the network (or restrict egress)
• Revoke tokens and force re-authentication for the user
• Disable suspicious OAuth apps and block untrusted client IDs
• Snapshot volatile data for forensics (process list, network connections)
• Quarantine suspicious binaries and persistence artifacts

You’re not trying to win an academic debate about whether it’s malware. You’re trying to stop the operator.

Practical defenses you can implement this quarter

Answer first: Combine cloud API visibility, endpoint controls, and AI-driven correlation—then rehearse the response.

Here’s a realistic checklist for teams that want to reduce exposure to Google Drive API C2 without slowing the business.

1) Put Drive API activity on your detection map

If your SOC can’t answer “Which endpoints use Drive APIs programmatically?”, you’re operating blind.

Minimum viable telemetry:

• Drive API call logs (where available)
• OAuth app inventory and consent events
• Endpoint network telemetry (destinations + frequency)
• Endpoint process telemetry (parent/child relationships)

2) Restrict OAuth and third-party app access

Cloud collaboration platforms are permission ecosystems. Tighten them.

Controls that help immediately:

• Allow-list approved OAuth apps for Drive access
• Restrict high-risk scopes (write access, full Drive access) to specific groups
• Require admin approval for new app consents

3) Add endpoint rules that match the behavior, not the hash

Some high-signal endpoint behaviors commonly accompany backdoors:

• Persistence creation (scheduled tasks, registry run keys, services)
• Suspicious scripting engines launching with encoded commands
• Unusual child processes spawned by user-facing apps

The winning pattern is: endpoint behavior triggers suspicion; cloud API activity corroborates it.

4) Build a playbook for “cloud API C2 suspected”

When you suspect Google Drive C2, your playbook should include both endpoint and cloud steps:

1. Contain the endpoint (isolation or egress restriction)
2. Revoke sessions/tokens for the user and device
3. Investigate Drive artifacts (folders/files used for staging)
4. Hunt for similar patterns across the fleet
5. Confirm persistence and remove it

If you don’t rehearse this, it’ll take hours the first time. Attackers don’t need hours.

5) Run a focused hunt: “Drive beaconing”

A simple hunt idea that often produces results:

• Identify endpoints with regular periodic Drive API calls
• Filter out known sync clients and approved automation
• Correlate the remainder with:
  • recent new binaries
  • suspicious process trees
  • unusual logon patterns

This is exactly the kind of hunt that AI-assisted triage can accelerate—ranking the riskiest candidates first.

People also ask: quick answers for your team

Answer first: Yes, you can detect malware hiding in Google Drive—but you need the right signals.

Can we just block Google Drive to stop this? You can, but most businesses won’t. A better approach is conditional access, app allow-listing, and monitoring for abnormal API behavior.

Is Google Drive the problem? No. The problem is that defenders treat trusted cloud services as implicitly benign. Attackers exploit that trust.

Do I need AI to catch this? If you have a small environment and deep manual expertise, you can catch some cases. At enterprise scale, AI-driven threat detection is the practical way to correlate endpoint + identity + cloud signals fast enough.

What’s the single highest-value control? Tightening OAuth app governance (allow-lists and consent restrictions) plus endpoint containment automation will stop a lot of API-based C2 campaigns early.

What NANOREMOTE should change about your 2026 security plan

NANOREMOTE is a reminder that C2 no longer needs shady servers. It can live inside the tools your employees use every day. If your detection strategy is still anchored to “block known bad,” you’ll miss the quiet attacks—the ones that last weeks.

For the AI in Cybersecurity series, this is the north star: use AI to connect the dots across endpoints, identities, and cloud APIs, then automate containment before an operator turns initial access into a full breach.

If you wanted one internal question to kick off a productive conversation after reading this, make it this: “Could we prove—within 15 minutes—that a compromised Windows endpoint is using Google Drive as C2?”