When AI Code Assistants Leak Fake IDs: GitHub Copilot’s Privacy Risks Explained

Incident Overview

A user on Twitter posted a screenshot showing GitHub Copilot completing a comment about Bilibili CEO Chen Rui with a Chinese identity‑card number. Analysis of the number revealed that the birth year (1988) and checksum were incorrect; the real CEO was born in 1978. The ID was therefore synthetic, demonstrating that Copilot can generate plausible‑looking personal data that does not correspond to any real record.

How GitHub Copilot Generates Output

Copilot is powered by the Codex model, an evolution of OpenAI’s GPT‑3 that is trained on both source code and natural‑language text. Like other large language models, Codex learns from massive public corpora that include code repositories, documentation, forums, and other web content. During training the model ingests billions of tokens, many of which contain personally identifiable information (PII) such as names, addresses, and ID numbers.

When generating a suggestion, the model predicts the next token sequence based on the statistical patterns it has observed. This process can unintentionally reproduce fragments of the training data—a phenomenon known as “memorization” or “data leakage.” Because the model does not store a searchable database, the reproduced data is a synthetic recombination of patterns rather than a direct lookup, but the output can still resemble real‑world PII.

Why Synthetic Personal Data Appears

Random sampling of training distribution: The model may sample a token sequence that matches the format of an ID number (e.g., 18‑digit Chinese ID) because such patterns are common in the training set.

Partial memorization: If a specific ID appeared in the training data, the model might reproduce parts of it, but the surrounding context (e.g., birth year) can be altered, resulting in an invalid but plausible number.

Prompt influence: Providing a name in the prompt biases the model toward generating data that it associates with that name, increasing the chance of producing a matching ID format.

Official Response from GitHub

GitHub CEO Nat Friedman stated that any personal information shown in Copilot suggestions is fabricated —it is synthesized from the model’s training corpus rather than retrieved from a dedicated personal‑data store. He emphasized that the occurrence does not imply a privacy breach in the traditional sense, but it highlights the need for responsible model training and output filtering.

Broader Privacy, Licensing, and Ethical Concerns

Privacy risk: Even synthetic PII can be misused if attackers treat it as real data, potentially feeding social‑engineering attacks or “leak databases.”

Licensing of training data: Copilot is trained on billions of lines of publicly available code, some of which lack clear open‑source licenses. This raises questions about downstream copyright compliance.

Code plagiarism: Recent incidents showed Copilot reproducing large code fragments, including copyrighted comments, which undermines the claim that the model only generates novel code.

Bias and harmful content: Like GPT‑3, Codex can emit racist, sexist, or otherwise unethical language when prompted, reflecting biases present in the training data.

Free Software Foundation (FSF) protest: The FSF criticized GitHub Copilot for requiring use within paid IDEs (e.g., Visual Studio, VS Code), arguing that this restricts user freedom and conflicts with free‑software principles.

Community and Industry Reaction

Developers and privacy advocates have called for stronger safeguards, such as:

Explicit filtering of PII patterns before presenting suggestions.

Transparent disclosure of training data sources and licensing status.

Open dialogue between AI tool providers and the developer community to establish industry standards for responsible AI training and deployment.

Code example

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