A 'feature' of AI can become a 'nightmare' for an organisation - Here's how.

The developers are living in an exciting time. A whole new generation of AI-powered terminals, code editors and IDE plugins has emerged, promising to supercharge our productivity. They can write boilerplate code, explain complex algorithms and even debug for us. Many of these tools are free and open-source, making them incredibly tempting to install and try out.

But in my role in security operations, I've seen the other side of this magic. I've watched as these "helpful" assistants inadvertently become data exfiltration channels, leaking sensitive company information directly from a developer's machine.

The scary part? The developer is almost always completely unaware it's happening.

This isn't about some malicious malware. The leak is often a core function of the tool itself. Let's break down how this happens and, more importantly, how you can prevent it.

The Core Problem: "Leak is a Feature, Not a Bug"

To give you a smart, context-aware response, an AI model needs one thing above all else: context.

When you ask an AI tool to "explain this function" or "find a command I ran last week" or "help me to fix this error", it doesn't just send your question to a cloud API. It packages up the surrounding "context" to get a better result. And what is that context basically?

It's,

• The code in your currently open file.
• The code in all your open tabs.
• Your terminal command history and the output on your screen.
• Sometimes, even file names from your entire project directory.

The process looks like this:

• You use an AI feature.
• The tool grabs "context" to be helpful.
• It sends that entire package to a cloud-based AI service for processing.

If a stray API key, a database password, a piece of customer data or an intellectual property (IP) or even a private key, in that context, it gets sent too.

Your Network DLP lights up (if configured properly) and people like us in security / SOC may get an alert.

Real-World Scenarios I've Seen

These aren't theoretical risks. Based on alerts and logs I've analysed, here are a few anonymised ways these leaks happen, showing you exactly how the data gets packaged and sent (of course by sanitising sensitive data)

Scenario 1: The "Intelligent" Terminal
Many new terminals use AI for natural language command search. To achieve this, they create "embeddings" (vector representations) of your command history. This involves sending the text content of your terminal buffer to their API.

I've seen network traces where the tool makes a POST request to an endpoint like https://api.ai-terminal-app.dev/v1/embeddings. The JSON payload often looks like this, sending a chunk of your recent terminal session directly:

{
   "input_text": "user@host:$ gcloud auth print-access-token\nyour-long-gcp-token-string\nuser@host:$ git push origin feature-branch\nCounting objects: 5, done.\n...",
   "model": "text-embedding-v2"
}

Notice how the output of the gcloud command, which is a temporary but highly sensitive access token, is captured right along with the benign git push command. You didn't copy it; the tool scraped it as part of its routine context gathering.

Scenario 2: The "AI-First" Code Editor
These editors let you "chat with your codebase." When you ask a question like, "How can I optimize this function?", the editor sends your code along with your question to a Large Language Model (LLM).

The API call to an endpoint like https://api.ai-code-editor.com/v2/chat/completions is often structured with a series of messages. The tool programmatically injects your code as part of the context, like so:

{
   "model": "code-gen-pro-4",
   "messages": [
     {
       "role": "system",
       "content": "The user is working with the following file named 'db_connector.py'. Use it as context."
     },
     {
       "role": "user",
       "content": "### Start of file: db_connector.py ###\n\nimport os\n\ndef connect_to_database():\n    # TODO: Move this to a secrets manager later\n    db_password = 'temp_password_for_dev_123!'\n    # ... rest of the connection logic\n\n### End of file ###"
     },
     {
       "role": "user",
       "content": "How can I optimise this function?"
     }
   ]
 }

The developer's innocent TODO comment and hardcoded password become part of the prompt payload sent over the internet, triggering a data leak alert.

Scenario 3: The "Innocent" Code Analyser Extension
This is the most dangerous one. You install a promising AI-powered security linter or code analyzer extension. In the background, it scans your code, perhaps by parsing it into an Abstract Syntax Tree (AST) to understand its structure.

For its own analytics or to report a "finding" back to a security dashboard, the extension might send telemetry about what it discovers. If it finds a hardcoded secret, its report can include the secret itself as part of the "evidence." The outbound JSON to a diagnostics service could look like this:

{
   "event_type": "code_analysis_finding",
   "file_hash": "e4f5g6h7...",
   "linter_rule": "HardcodedPrivateKey",
   "severity": "critical",
   "code_snippet": "private_key_pem = \"-----BEGIN EC PRIVATE KEY-----\\nMI...H6g==\\n-----END EC PRIVATE KEY-----\"",
   "extension_version": "1.4.1"
 }

The extension's feature of finding a hardcoded private key is genuinely useful. However, in the process of reporting this finding, it exfiltrates the actual key to a third-party server. This instantly turns a local security vulnerability into an active, critical data breach.

A Quick Note on Free v/s Enterprise AI
It's important to distinguish these free tools from enterprise-grade, subscription-based AI services like GitHub Copilot for Business, Google's Gemini for Workspace or Azure OpenAI services. Free tools often use your data to train their models—your data is the price you pay.

Enterprise services, on the other hand, operate under strict data privacy contracts. They typically offer zero-data-retention policies, meaning your code is not stored on their servers or used for model training. This is why your company may pay for one service while blocking another.

How to Prevent These Leaks and Code Safely

Remediating a leak is a painful fire drill. Preventing one is a simple habit. Here’s how you can protect yourself and your company.

1.⁠ ⁠Cultivate a "Zero-Trust" Mindset for Tools
Treat every new tool, especially those with cloud-based AI features, as a potential data leak path. Before you install that shiny new editor or extension, read its privacy policy. Understand what data it collects and where it sends it. If it’s not clear, don’t install it.

2.⁠ ⁠Configure Your Tools Defensively
Dive into the settings menu of your tools. Look for and disable any options related to:
"Send anonymous telemetry"
"Help improve our models"
"Enable cloud-based AI suggestions" (if you can live without them)
Opt-out of everything that isn't always essential for the tool's core function.

3.⁠ ⁠Practice Strict Secret Hygiene
The best way to prevent secrets from leaking is to never have them in your code in the first place. Not even for a "quick test".

• Use .env files for local development and add them to your .gitignore - (don’t expose them over the internet unless you’re in the mood to speedrun a career change.)
• Use a proper secrets manager like HashiCorp Vault, AWS Secrets Manager or Google Secret Manager.
• Install pre-commit hooks like Gitleaks or TruffleHog. These tools will scan your code for secrets before you can commit them, stopping a leak before it even begins.

4.⁠ ⁠Vet Your Extensions
Your editor is only as secure as its most permissive extension. Before installing a new one, ask:

• Who is the publisher? Is it a reputable company or a random, unknown individual?
• What are the reviews? Do other developers mention privacy concerns?
• Can I do this without an extension? Sometimes, a simple script is safer than a black-box extension.

5.⁠ ⁠Advocate for Safe, Company-Approved AI
If you need AI tools to do your job effectively, talk to your IT and Security teams. They can procure enterprise-grade tools that come with the security and privacy guarantees your organisation needs. It’s better to use a sanctioned tool than to download a free one that puts everyone at risk.

Final Thoughts

AI developer tools are a massive leap forward for productivity, but we can't afford to be naive about how they work. By being mindful of the data we expose to them and adopting a few key security habits, we can embrace the benefits of AI without creating a nightmare for our organisations.

Stay safe and code secure.