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How the reported attack chain works
Adversa researcher Rony Utevsky says the page did not initially present its instructions to the model as ordinary readable text. Instead, the page included encrypted content and Python decryption steps. When Copilot CLI executed those steps in its runtime, the decrypted output was treated as trusted context. Adversa calls this technique Cryptographic Context Injection (CCI).
- A user in Copilot CLI autopilot asks the tool to fetch a webpage controlled by an attacker.
- The page supplies encrypted content and decryption steps, along with two possible keys. One is a decoy that leads the agent to read local files and incorporate their contents into a template; decryption with that key fails.
- A second key successfully decrypts instructions that tell the agent to make another request, sending the previously read contents to an attacker-controlled endpoint.
Adversa says its demonstration took 28 seconds and did not produce a confirmation prompt or a clear indication in the transcript that data had left the machine. Those are the researcher’s claims about the demonstration, not independently verified findings. The published account describes a chain of actions, rather than a webpage directly extracting data without user involvement. Adversa’s disclosure explains the mechanism without changing the key point: instructions hidden from the model at first can become operative after execution.
What conditions did Adversa say were necessary?
The reported chain required the user to ask Copilot CLI to fetch the external page, autopilot to allow autonomous action, and a model permissive enough to follow the decrypted payload. It should not be read as evidence that any webpage can make any Copilot CLI session disclose secrets.
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Adversa says Microsoft’s mai-code-1.1-flash completed the chain in 50% of its tests. It says two GPT-5.6 models available in Copilot refused the same payload. These are results from Adversa’s tests, not a standardized benchmark, a general attack success rate, or a measure of how often users are exposed. Adversa says it reproduced the chain as of October 1, 2026. The Register and Adversa also report that Auto model routing could assign different models without users seeing which model handled a session, making model selection a relevant uncertainty. The Register’s October 6, 2026 report covers the model-routing issue and the disclosure.
Why GitHub and Adversa disagree about the finding
Adversa says it submitted the report to GitHub’s bug bounty program on September 17, 2026. According to Adversa, GitHub triage validated the finding but declined to classify it as a vulnerability and ruled the report ineligible for a bounty.
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An unnamed GitHub spokesperson told The Register that the chain required a user to intentionally direct Copilot CLI to fetch attacker-controlled or untrusted content and confirm the action, so GitHub did not consider it a product vulnerability. The spokesperson also said GitHub continues to investigate reported security issues and look for ways to improve its products. Adversa disagrees with that risk assessment. The distinction is important: GitHub’s classification is its stated position, while the attack narrative and contrary assessment come from Adversa.
How to reduce risk when using Copilot CLI
Adversa recommends controlling the sequence and scope of tool actions, rather than relying only on filters that inspect visible text. The following are security practices suggested by Adversa; they are not all documented as built-in Copilot CLI protections.
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- Prefer attended use for untrusted content. Avoid autopilot when asking the CLI to inspect pages, repositories, or other material you do not control. Review proposed actions before allowing file access or network requests.
- Limit what the session can reach. Keep sensitive files, credentials, and write access out of the task’s scope where possible. Restrict outbound network destinations, and scrutinize requests to destinations unrelated to the task.
- Review resolved tool actions, not just prompts. Retain tool-call traces that show the actual arguments and destinations. Alert on a sequence in which untrusted content is followed by code execution, local file access, and an unrelated outbound request.
- Separate untrusted content from privileged tools. Avoid placing untrusted page content in a context that also has broad access to local files, credentials, or network tools.
- Be deliberate about model selection. When the model matters to a security-sensitive workflow, prefer an explicitly selected model over Auto routing if your setup allows it, and do not treat a model’s refusal in one test as a guarantee for other prompts or sessions.
What Copilot CLI hooks can—and cannot—do
GitHub’s Copilot CLI hooks tutorial documents a preToolUse hook that can allow or deny a tool action, along with examples for logging prompts and tool attempts. Teams can use policy checks to gate actions, such as requests to new network destinations, but must design and review those policies against their own permissions and workflows. The documentation does not establish that hooks alone prevent the CCI chain.
Logging needs care: GitHub warns that prompts may contain sensitive information and says it does not provide built-in secret redaction for hooks. Limit access to logs, decide what should be retained, and configure any redaction or filtering needed before storing them.
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Do not confuse this report with a separate Copilot CLI advisory
GitHub published GHSA-g8r9-g2v8-jv6f on March 6, 2026, about a separate shell safety-assessment issue in Copilot CLI. That advisory lists versions through 0.0.422 as affected and 0.0.423 as patched. It is not the October CCI disclosure, and the advisory’s patch should not be presented as a fix for the reported encrypted-page attack chain.
GitHub’s documentation about risks and mitigations for Copilot cloud agent discusses prompt injection in issues and comments, including hidden characters. That is a separate product context; it does not establish that the same filtering protects Copilot CLI.
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