Assessing GitHub Copilot
AI Security · Developer Enablement · Updated Jul 2026
Tools like GitHub Copilot are changing how engineers write code: GPT-4 quietly auto-completing functions, suggesting tests, and answering questions right in the editor. The productivity story is real. But every new dev tool deserves a security review before it touches your codebase, so here’s mine, written for engineers and security leads rather than marketing decks.
Why Copilot stands out
Code completions. Copilot’s core feature is context-aware code suggestions. They’re surprisingly good, and they expose developers to patterns and practices they’d otherwise have to go searching for.
Copilot Chat. A conversational interface inside your IDE. Generate code, write docs, build unit tests, or ask why a function does what it does, all in plain English, like discussing with another developer.
CLI assistance. Copilot extends suggestions to the command line, which is sometimes weirdly delightful and sometimes wrong in confident ways.
Security and privacy concerns
Before scoring it, here are the LLM risks I care about most as an AppSec lead:
Sensitive information disclosure. Anything you type into a prompt is potentially leaving your machine: company references, secrets, proprietary code. The Samsung ChatGPT leak is the canonical example.
Overreliance. Accepting LLM-generated code without scrutiny introduces vulnerabilities, insecure patterns, and confidently wrong information. Strict reviews and validation are non-negotiable.
Supply chain and model theft. LLMs become part of your software supply chain, both as a path for malicious code (when suggested dependencies get blind trust) and as IP that can itself be stolen. Treat LLM-suggested dependencies like a random GitHub fork: cautiously.
The framework: OWASP Top 10 for LLM Applications
I based this review on the OWASP Top 10 for LLM Applications, which exists to help teams make risk-based decisions about LLM tech. Here’s how Copilot ranks against each item, in my professional opinion.
LLM01: Prompt Injection. Crafted inputs can manipulate an LLM into leaking data or bypassing safeguards.
Copilot shows sources for external content, lets users identify untrusted content, and adheres to Microsoft’s security and AI-use standards.
LLM02: Insecure Output Handling. Poorly validated LLM output flowing into other systems can enable web and backend attacks.
Copilot Chat for Business doesn’t store contextual prompts and suggestions, and organizations can block suggestions matching public code via policy.
Watch out: data handling differs for Individual accounts. If opted in, an individual’s prompts and suggestions are stored, which is a privacy risk for their code.
LLM03: Training Data Poisoning. Tampered training data can introduce vulnerabilities and biases.
Copilot trains on a very large, diverse set of public code, which dilutes the risk of concentrated poisoning, and GitHub’s review process plus OpenAI’s training expertise add further defense.
LLM04: Model Denial of Service. Attackers can overload an LLM and degrade service.
Not really a consumer-facing risk here: Copilot runs on robust infrastructure with rate limits and safeguards against excessive resource consumption.
LLM05: Supply Chain Vulnerabilities. Third-party models and data can be compromised upstream.
Copilot is built on OpenAI’s models, so it theoretically inherits the general risks above. Worth tracking, hard to act on as a consumer.
LLM06: Sensitive Information Disclosure. LLM apps can unintentionally expose sensitive data.
Business subscriptions get role-based access, org-wide policies for code completions, and telemetry controls.
Watch out: Individual accounts can turn telemetry off, but it’s up to each user, and there’s no organization-wide policy management. This is the headline risk for most enterprises.
LLM07: Insecure Plugin Design. LLM plugins often lack input validation and strong access control.
Copilot doesn’t rely on third-party LLM plugins, which sidesteps this category.
LLM08: Excessive Agency. Systems granted too much autonomy can take harmful actions.
Copilot is deliberately limited: it suggests, you accept or reject. It doesn’t initiate interactions with external systems or make changes on its own.
LLM09: Overreliance. Trusting LLM output blindly causes security issues and misinformation.
GitHub added an AI system that blocks insecure patterns like SQL injection in suggestions. Even so, this risk is cultural, not technical. Reviews, testing, security tools, and judgment still apply.
LLM10: Model Theft. Proprietary model details or sensitive data can leak through outputs.
Business accounts don’t store prompts or code snippets, transmitted data is encrypted, and private code isn’t suggested to other users. Snippets sent for suggestions are discarded once the suggestion returns.
Okay, so is it secure or not?
Of course, my answer is: it depends.
First, I don’t recommend Copilot Individual accounts for work at a company. Individuals have to manage their own policies, and several important controls only exist for Business accounts.
Second, even with GitHub’s vulnerability filtering, Copilot can still produce vulnerable code. So pair it with a security scanner. Snyk offers a free IDE extension that scans generated output in real time, and running it alongside Copilot catches vulnerable suggestions before they land in your codebase.
The right question isn’t “is Copilot safe?” It’s “are we set up to benefit from it without being burned by it?” With clear data-handling policies, strong code review, secret scanning, and developer education, the answer can be yes.