Is It Safe to Paste Code Into AI? (Secrets, IP, Client Data)

The short answer: pasting code into AI is useful, but risky if you skip one step. AI coding help is real. Yet source code carries three hidden payloads, beyond the logic you want fixed. First, secrets: hardcoded API keys, cloud credentials, database passwords, tokens, private keys. Second, proprietary logic your company hasn't shipped yet. Third, sometimes real customer data, hiding in comments, fixtures or test files. Paste all of that into a consumer AI, and it may be retained, reviewed by a human, or used to train a model. The fix is simple: strip the secrets and identifiers before the code leaves your machine.

Three things hide inside your code

Code is more than logic. It often carries very real values. Secrets are the sharpest of them. Here are the types most often left hardcoded inside code.

• API keys and access tokens (OpenAI, Stripe, Slack, GitHub, GitLab).
• Cloud credentials for AWS, Google Cloud or Azure.
• Database passwords and connection strings.
• Private keys and JWTs (signed session tokens).

How common are hardcoded secrets

Hardcoded secrets are very common. GitGuardian scans public code for leaked credentials. Its State of Secrets Sprawl 2024 report shows the scale. It found more than 12.7 million new secrets exposed in public GitHub commits during 2023. That was a 28% jump over the prior year. Roughly 1 in 10 commit authors leaked a credential that year. And about 7 of every 1,000 commits held at least one secret.

One detail stands out for AI users. GitGuardian recorded a roughly 1,212x year-over-year surge in leaked OpenAI API keys. They became the single most-detected secret type. These are public GitHub leaks, not secrets measured inside AI tools. But the inference is hard to dodge. The same hardcoded keys sitting in code are the ones people paste into a chatbot.

The Samsung lesson

The best-known case is Samsung. In 2023, engineers in its semiconductor division reportedly pasted confidential material into ChatGPT. That included source code and internal notes. It happened across several incidents, within weeks of the tool being allowed. Bloomberg broke the story, and CNBC reported it too.

Samsung then moved to restrict generative AI on company devices. The worry was structural, not a public breach. Anything typed into an outside service could be stored on third-party servers. And it could be hard to retrieve or delete later. No leak to the public was reported. The risk was retention beyond the company's control.

What OWASP says

Security standards bodies take this seriously. OWASP is a nonprofit that sets widely used software-security standards. It maintains a Top 10 for LLM Applications. On that list, 'Sensitive Information Disclosure' (LLM06) is a named risk. OWASP frames it as confidential or proprietary information surfacing through an LLM system. It warns this can bring legal consequences or lost competitive advantage. The lesson applies first to what you feed the model.

The fix: anonymize before you paste

Here's the good part. You don't have to stop using AI on your code. You just remove what shouldn't travel. Ask your question about the logic, the bug, the pattern. Keep the live secrets and identifiers out of the paste. The model helps just as well without them.

1. 1Scan the snippet for secrets: keys, tokens, passwords, private keys.
2. 2Replace each one with a reversible token, in the browser.
3. 3Paste only the anonymized code into the AI.
4. 4Restore the real values in the answer, locally.

This works because most secrets follow patterns. An AWS key, a GitHub token, a Stripe key, a JWT — each has a recognisable shape. A detector can catch them before you hit send. That's the difference between useful AI help and an accidental leak.

That's what ONYRI Sanitize does. The engine detects cloud keys and tokens — AWS, GitHub, GitLab, Stripe, OpenAI, Anthropic, GCP, Azure, Slack, JWT — right in your browser. It replaces them with reversible tokens before anything is sent. Detection and the mapping stay client-side. Only anonymized code reaches the model. The AI sees tokens, never your live secrets. You get the debugging help, without the risk that made Samsung pull back and that OWASP names.