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From Autocomplete to Autocompromise: GitHub Copilot’s Security Challenges

Oren Saban 2 May 2024 4 min read

Imagine a tool so powerful it could write up to 80% of your code. Sounds like science fiction? Well, it’s closer to reality than you might think. GitHub’s CEO has stated that their AI-driven code companion, Copilot, will be capable of handling the lion’s share of coding tasks “sooner than later.” While the productivity benefits are undeniably enticing, this brave new world of automated coding comes with a long list of security and legal risks that demand our attention.

Understanding GitHub Copilot

In order to suggest your next line of code, every time you type a character, its IDE extension is sending out to GitHub servers most of the document you are working on, and in many cases enriches the context with code from other files in your repository. This isn’t just about predicting the next line of code; it’s about understanding the whole narrative of your coding session.

The Security Risks of GitHub Copilot

But here’s the catch: all the cool features that make GitHub Copilot such a powerhouse in coding could also flip the script and make it a tricky adversary. And with its actions under the hood staying pretty much a black box, we’re left dealing with risks that are truly ‘unknown unknowns’.

Sensitive Code and Data Exposure:

GitHub Copilot could inadvertently learn and subsequently suggest secrets such as API keys, passwords, usernames and connection strings if these are present in the data. This type of data leakage could expose critical systems and lead to data breaches. For example, malicious actor that gains access to one user, can get secrets across the board (shortening dwell time); or, another company gets your secrets as was published on last October: GitHub Copilot can leak real secrets.

Generated Code threats:

AI attacks:

There’s a chance Copilot could recommend libraries with names suspiciously close to popular ones—a common tactic in supply chain attacks where malicious libraries are pushed to public repositories. Another point of concern is the problematic suggestions from “poisoned data” that OpenAI Codex was trained on, like open-source code on GitHub that contains intentionally planted backdoors.

Vulnerable Code:

Copilot may suggest code that is outdated or has known vulnerabilities, simply because it exists in the training set.

Intellectual Property Risks:

Copilot operates in a gray area concerning intellectual property. It could suggest code snippets that are copyrighted or under restrictive licenses, leading to potential legal challenges for unsuspecting developers. GitHub claim they will defend you on court for such issue under certain configuration and circumstances, but that might be way too later for your brand reputation.

Over-Reliance by Developers:

There’s a real danger in developers becoming too reliant on Copilot’s suggestions, which might not always be secure or optimal. This over-reliance could lead to significant vulnerabilities being overlooked, and due to the fact the GitHub Copilot will learn from your code and reuse it in your own environment, it will enhance and replicate current vulnerabilities instead of fixing them.

Losing trace:

The mass code generation introduces significant risks by making it unclear whether code was crafted by AI or human developers, and where AI “contributed” to your codebase. This ambiguity complicates accountability, affects precise quality control, complicates compliance with regulatory standards, and confuses intellectual property claims—each of which can have far-reaching consequences for software integrity and legal responsibilities within an organization.

So once more, do security teams need to shut it down? Or perhaps there’s another way?

No, you don’t have to be the party popper once again, just need to make sure you take the right security measure to keep to innovation running, securely.
To start, you can define list of repos and files which you don’t want GitHub Copilot to work on. Additionally,
masking secrets and sensitive code before those leak out of boundary, will prevent your secrets from going for a trip. Furthermore, by validating Copilot suggestions are “appropriate” and safe to use (legally and securely), as well as educating your developers for safe use, you reduce the risk of overreliance and Copilot echoing vulnerable code across your repo.

Thank god you’ve got Apex

Apex boosts these safeguards by offering detailed insights into each Copilot interaction, masking sensitive data, and spotting vulnerabilities in suggestions. It also automates an AI Bill of Materials (AI-BoM) to track all data interactions and code changes by Copilot. This strong defense system enables organizations to confidently and securely utilize Copilot’s features.

AI is already the core of your company, request Github Copilot solution brief

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