Your AI Coding Assistant Might Be Uploading Your Code to the Cloud — Here’s How to Check

You sit down, fire up your terminal, and run an AI-powered code build command. Within seconds, every file you’ve ever committed — every API key, every database password tucked inside .env, every proprietary algorithm your team spent months building — is quietly uploaded to a cloud server halfway across the world. You didn’t click “agree.” You didn’t read a privacy notice. The tool just did it.

That’s not a hypothetical. That’s what happened this week with SpaceXAI’s Grok Build CLI.

An independent security researcher discovered that Grok Build had been silently uploading entire Git repositories — complete with unredacted secrets, private codebases, and full commit history — to a Google Cloud storage bucket. SpaceXAI only acknowledged the issue after the story broke and has since issued a server-side fix, but the damage to trust is done.

And here’s the uncomfortable truth: Grok Build probably isn’t the only tool doing this.

If you’re using AI coding assistants to write, review, or build your code, you’re trusting them with your most valuable asset — your source code. The question is not whether you trust the company behind the tool. The question is: do you have a way to check what the tool is actually doing with your code?

Today, I’m walking through a practical, step-by-step framework for auditing any AI coding tool’s data privacy behavior. By the end of this guide, you’ll know exactly what to look for, what tools to use, and how to protect your codebase from unauthorized exfiltration.

Step 1: Monitor Network Traffic from AI Coding Tools

The fastest way to catch unauthorized data transmission is to watch what your AI tool sends over the network. Most developers never do this — and that’s exactly why Grok Build’s behavior went unnoticed until a security researcher published the findings.

Using mitmproxy for HTTPS Traffic Inspection

mitmproxy lets you intercept and inspect HTTPS traffic. Here’s the quick setup:

# Install mitmproxy
brew install mitmproxy
# or on Ubuntu/Debian
pip install mitmproxy

# Start the proxy
mitmproxy --mode transparent --listen-port 8080

# Route your AI tool through the proxy
# For most CLI tools, set environment variables:
export HTTP_PROXY=http://localhost:8080
export HTTPS_PROXY=http://localhost:8080

Then run your AI tool’s command. Every HTTP request appears in mitmproxy’s interface. Look for POST requests to unfamiliar endpoints that contain code snippets, file paths, or repository metadata. For Grok Build, the payloads were going to storage.googleapis.com buckets — which wouldn’t immediately look suspicious unless you knew to check.

Using Wireshark for System-Level Monitoring

For a broader view, Wireshark captures all network traffic on your machine. Filter by the AI tool’s process name:

# Install Wireshark
sudo apt install wireshark

# Run and filter by DNS queries or IP ranges
# Filter: dns.qry.name contains "googleapis"
# Filter: http.request or tls.handshake

The key insight: run the audit before you start using a new AI tool, not after. A five-minute network trace when you first install a tool can save you weeks of regret down the line.

Step 2: Audit File System Access with strace and lsof

Network monitoring catches outgoing data, but what if the tool reads files without sending them immediately? Linux’s strace and lsof show you every file the tool touches.

Using strace to Log File Operations

# Run the AI tool with strace, logging all file opens
strace -o /tmp/ai-tool-trace.log \
  -e trace=file,open,openat,read \
  -f \
  grok build "refactor this module"

# Filter for read operations on sensitive files
grep -E '(\.env|config\.|\.git/|credentials|key\.json|secret)' \
  /tmp/ai-tool-trace.log | head -50

This tells you exactly which files the tool accessed. If it’s reading ~/.ssh/id_rsa, database.yml, or secrets.json when all you asked it to do was refactor a component, something is wrong.

Using lsof for Real-Time Monitoring

# Get the PID of your AI tool
pgrep -f "grok" | head -1

# List all open files for that PID
lsof -p <PID>

# Watch for new file opens in real-time
watch -n 2 "lsof -p <PID> | grep -E '(\.env|\.git|secret)'"

I wrote about this in more technical depth in my guide on how AI coding assistants can trigger security alerts — the same tools that guard against attackers can also catch your own AI tools accessing unexpected files.

Step 3: Scan Your Codebase for Secrets Before Using AI Tools

Even if you trust the tool not to exfiltrate anything, human error happens. A developer on your team might paste a sensitive API key into a code snippet sent to an AI assistant. A CI/CD configuration might accidentally include --allow-secrets in a prompt.

Run a secrets scanner before any AI-assisted session:

# Install Gitleaks
brew install gitleaks
# or
apt install gitleaks

# Scan the entire repository
gitleaks detect --source . --verbose

# Scan staged changes before they hit the AI tool
gitleaks protect --staged --verbose

TruffleHog is another excellent option — it specializes in finding high-entropy strings and credentials:

# Install and run TruffleHog
pip install trufflehog
trufflehog filesystem --directory . --since-commit HEAD~5

Make this a habit. Before you open a file with an AI assistant, run the secrets scanner. The five seconds it takes could prevent a leak of production database credentials.

Step 4: Run AI Tools in Isolated Environments

The nuclear option — and honestly, the one I recommend for any team dealing with sensitive code — is to run AI coding tools inside containers with no network access to the outside world.

Docker with No Network Access

# Run the AI tool in a container with network disabled
docker run --rm -it \
  --network none \
  -v $(pwd):/workspace \
  -w /workspace \
  ubuntu:latest \
  bash -c "./your-ai-tool"

# For tools that need local-only networking for model loading
docker run --rm -it \
  --network host \
  --cap-drop ALL \
  --security-opt no-new-privileges \
  -v $(pwd):/workspace:ro \
  -w /workspace \
  your-ai-tool-image

Notice the :ro mount flag — that makes your workspace read-only inside the container. The AI tool can read your code to analyze it, but it can’t modify or exfiltrate it.

Using Firejail for Lightweight Sandboxing

Firejail is lighter than Docker and perfect for running individual commands safely:

# Install Firejail
sudo apt install firejail

# Run a command with no network access
firejail --net=none your-ai-tool

# With stricter permissions
firejail \
  --net=none \
  --noprofile \
  --read-only=/home/user/project \
  --private=/tmp/ai-sandbox \
  your-ai-tool

If you’re looking for a more comprehensive solution, my Destructive Command Guard (DCG) guide covers another essential layer — preventing AI agents from running destructive commands even when they’re already inside your environment.

Step 5: Choose Local-First or Self-Hosted Alternatives

This is the most effective long-term strategy. If the AI model runs entirely on your machine, there’s nothing to exfiltrate.

Local Models with Ollama

Ollama runs open-source models directly on your hardware. No cloud API calls, no data leaving your machine:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a coding-capable model
ollama pull codellama:7b
# or for better performance
ollama pull deepseek-coder:6.7b

# Verify no network traffic (Step 1) while using it
ollama run codellama:7b "explain this code"

Pair this with Continue.dev — an open-source AI code assistant that integrates with VS Code and JetBrains, using your local Ollama models instead of sending code to a third-party API.

Self-Hosted vs API: The Cost Argument

Besides privacy, there’s a compelling economic case for self-hosting. As I explored in my analysis of the self-hosted AI shift, Fortune 500 companies are quietly moving away from third-party AI APIs for exactly these reasons — control, cost, and data sovereignty.

For individual developers and small teams, the math is even clearer. A decent local model running on an M-series Mac or an RTX GPU can handle 80% of your daily coding tasks without ever touching the internet.

Step 6: Create a Privacy Review Checklist for Every AI Tool

Before adding any AI coding tool to your workflow, run it through this checklist. I use this myself at my day job as an ICT manager, and it’s saved us from onboarding tools that would have been compliance nightmares.

Document the results. If a tool fails the network check and you still need to use it, at least you’re making an informed decision rather than discovering the problem on Hacker News.

Why This Matters More Than Ever

The Grok Build incident isn’t an isolated case. It’s a symptom of a larger pattern. As I wrote in my breakdown of the jscrambler supply chain attack, the developer ecosystem is under constant pressure from tools and libraries that overreach their permissions. AI coding tools amplify this risk because they operate with an implicit trust — nobody audits them because nobody expects an AI tool to be a spy.

The stakes are higher now because these tools don’t just read a line of code at a time. They read your entire repository. Your commit history. Your branch structure. Your CI/CD configuration. Your .env files with production database credentials. Everything.

The difference between a safe AI coding tool and an unsafe one is not the quality of the code it generates. It’s whether you can verify what it does with your code after generating it.

Bottom Line

You shouldn’t have to trust AI companies with your entire codebase. You should have tools and processes that let you verify what their software does — just like you would with any other dependency.

The six-step framework I laid out here takes about an hour to set up the first time, and about five minutes per tool every time you evaluate a new one. That’s a small price to pay for protecting code that might represent months or years of work.

The next time a new AI coding tool hits the front page of Hacker News, before you npm install or pip install or curl | sh, spend twenty minutes running it through this audit. Your future self — and your team — will thank you.

If you found this useful and want to go deeper, I also explored how the data you feed into AI tools can come back to compete with you — a broader look at the economics of AI training data and intellectual property in the age of frontier models.