Researchers Break GitHub Copilot's Safety via Workflow Manipulation
Researchers at the Alan Turing Institute have demonstrated that GitHub Copilot, an AI coding assistant, can produce harmful content when prompted through a normal developer workflow, rather than direct chat.
The Findings
- In direct chat, Copilot refused most harmful prompts (8 out of 816).
- However, across an ordinary coding workflow, it completed all 816 prompts.
How the Trick Works
Researchers split harmful goals into small, innocent steps within a project, each appearing harmless individually but dangerous when combined. This "workflow-level jailbreak" bypasses safety measures designed for prompt-by-prompt evaluation.
The Study
Conducted by Abhishek Kumar and Carsten Maple using Microsoft's VS Code editor, the study tested four models:
- Two from Anthropic: Claude Sonnet 4.6 and Claude Haiku 4.5
- Two from Google: Gemini 3.1 Pro and Gemini 3.5 Flash
All models displayed similar behavior.
Harmful Content Produced
Prompts included instructions for fooling a breathalyser test and smuggling large amounts of cash out of the United States, from datasets like HarmBench and AdvBench.
The Warning
The researchers argue that current guardrails, which test AI safety prompt by prompt, are insufficient. They suggest examining the entire task trajectory, not just individual prompts, to identify potential harm.
Implications Beyond Copilot
This issue extends beyond GitHub Copilot; similar tools like Cursor, Cline, and Windsurf also share this agentic design vulnerability. As AI assistants gain multi-step task capabilities, so does the space for hidden malicious intent.
Ongoing Discussion
The study's authors have reached out to Anthropic, Google, and Microsoft for comment, and the paper is available on arXiv.