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A significant AI-driven security flaw in GitHub Codespaces, termed RoguePilot, which allowed intruders to stealthily commandeer a repository by incorporating harmful instructions within a GitHub Issue.
The vulnerability, discovered by analysts at the Orca Research Pod, takes advantage of the seamless connectivity between GitHub Issues and the in-Codespaces Copilot AI assistant, necessitating no direct engagement from the attacker to initiate a complete repository takeover.
The vulnerability was responsibly communicated to GitHub, and Microsoft has since resolved it following collaborative remediation efforts with the Orca team.
How the GitHub Copilot Attack Operates
RoguePilot is categorized as a Passive Prompt Injection, a variation where malicious commands are woven into data, content, or developer environments that a language model processes automatically.
In contrast to conventional prompt injection requiring a victim’s direct interaction with the AI, this attack is activated as soon as a developer initiates a Codespace from a contaminated GitHub Issue. When a Codespace is launched from an issue context, GitHub Copilot is automatically presented with the issue’s description as an initial prompt, establishing a direct injection route from untrusted user-controlled material into the AI agent’s execution context.
Researcher Roi Nisimi of Orca Security exhibited the exploit sequence by embedding concealed instructions inside a GitHub Issue using HTML comment tags (), a conventional GitHub feature that makes content invisible to human observers while remaining entirely readable by Copilot when it processes the issue description.
Once the Codespace was accessed, Copilot quietly adhered to the injected commands without displaying any obvious alert to the developer.
The attack then advances through a three-phase exfiltration sequence. Initially, the injected prompt directs Copilot to run gh pr checkout 2 through its run_in_terminal utility, importing a deliberately crafted pull request containing a symbolic link named 1.json that leads to /workspaces/.codespaces/shared/user-secrets-envs.json — the document containing the environment’s GITHUB_TOKEN.
As Copilot’s safeguards do not track symbolic links, the agent accesses the secrets file through the link using its file_read tool without activating workspace boundary constraints.
Finally, Copilot is ordered to generate a new JSON file, issue.json, with a $schema property directed to a server under the attacker’s control, exploiting VS Code’s default json.schemaDownload.enable setting, which automatically retrieves remote JSON schemas via HTTP GET.
The attacker appends the pilfered GITHUB_TOKEN as a URL parameter in this schema request, resulting in concealed out-of-band extraction of the privileged authentication token. With a valid GITHUB_TOKEN scoped to the repository, the attacker gains full read and write permissions — finalizing a covert repository takeover.
Orca Security labels RoguePilot as a novel category of AI-mediated supply chain assault, where an LLM’s agentic abilities, terminal access, file read/write, and network-connected tools are leveraged against the very developer the AI is intended to support.
The vulnerability illustrates that Copilot, functioning as an autonomous coding assistant within Codespaces, cannot reliably differentiate between a developer’s authentic command and malicious content embedded in a GitHub Issue or pull request.
The attack necessitated no special permissions, no code execution from the victim, and no social engineering beyond crafting a harmful GitHub Issue placing it firmly within the grasp of low-sophistication threat actors.
Security professionals emphasize that this is a direct outcome of granting AI agents “God Mode” permissions, tools, terminal access, and privileged tokens while the foundational model continues to function on open-book logic that regards all processed text as potentially reliable.
Orca’s report suggests that vendors implement fail-safe defaults across all LLM-integrated developer tools: treat repository, issue, and pull request content as untrusted input; deactivate passive AI agent prompting from external data sources; configure json.schemaDownload.enable to false by default; enforce strict symlink sandboxing within workspace parameters; and impose minimal-scope, short-lived token issuance for Codespaces environments.
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