“What makes CoSnitch unique is how Copilot surfaced its own vulnerabilities,” the Varonis Threat Labs researchers wrote.
How Varonis discovered CoSnitch
Researchers at Varonis Threat Labs say they manipulated Microsoft Copilot Personal into revealing an exploitable behavior and then used that information to craft a working attack. Varonis reported the flaw, which it named "CoSnitch," to Microsoft in December 2025 and shared its research in advance with The Register. According to Varonis, Redmond planned to issue a patch and formally identify the CVE on Tuesday. The Register contacted Microsoft about the fix and the CVE identifier but did not receive a response prior to publication.
The technical vector: the ?q= parameter and an undocumented autorun=1
Varonis traced the issue to Copilot’s web interface and a URL-handling flow involving the ?q= query parameter. That parameter had previously allowed text injected into the chat-input field to be passed into Copilot with no user interaction. Varonis says Microsoft “silently” disabled that parameter to harden Copilot against prompt-injection attacks. When researchers then asked Copilot how to execute a prompt without user interaction, the assistant described the disabled parameters and protections in detail—and revealed a previously undocumented parameter, autorun=1.
According to Varonis, Copilot told researchers that, under specific session conditions, the undocumented parameter causes a ?q=-supplied prompt to execute automatically on page load with no user action and no visible confirmation on the user interface. The assistant also described the exact session conditions required for autorun=1 to work and noted that Copilot’s content filtering applies to the first response cycle but not to subsequent cycles.
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End the scrambleHow the attack works and what an attacker can access
Using the information Copilot supplied, the researchers constructed a working URL that triggers automatic prompt execution: https://copilot.microsoft.com/?q=&autorun=1. Their attack chain works like this: a victim clicks a malicious URL delivered by SMS, email phishing, or a QR code; the browser loads Copilot in the victim’s active, authenticated session; the ?autorun=1 parameter triggers auto-execution and the ?q= prompt; and Copilot processes and executes the injected prompt without user interaction or a visible indication of prompt-injection.
Varonis warned that, because Copilot treats such injected prompts as legitimate user instructions, an attacker could gain access to the victim’s session context, messages, emails, connected apps, and Copilot memory. The research lists direct abuse scenarios: exfiltration via OAuth connectors to Gmail, Google Drive, Google Calendar, or Copilot’s own chat history; poisoning entries in Copilot’s persistent memory; reconnaissance of connected apps, accessible files, and emails; and modifying what Copilot shows the user in future sessions for disinformation.
The Varonis team included concrete examples of malicious prompts. One illustration searches a user’s inbox for the latest email address, saves it into a variable named SUPPORT, builds the URL https://webhook.site/75aabb18-9bcf-4383-9e29-349fbc4c40e8/SUPPORT, and asks Copilot to summarize that URL—effectively exfiltrating data to an attacker-controlled webhook.
Varonis’ warnings and Lior Adar’s assessment
Senior security researcher Lior Adar, speaking for the Varonis team, framed the finding as more than a single product bug. “Finding these types of one-click data exfiltration vulnerabilities highlights deep architectural flaws that can carry over directly into corporate environments,” Adar told The Register. “I tricked the assistant into leaking sensitive internal parameters and configuration details,” he said, noting that exposing backend mechanics gives attackers “a blueprint of the AI's internal logic for Automatic Prompt Execution.”
Adar also pointed to a structural problem with large language models. “When an AI reads an untrusted email or shared doc containing hidden prompts, it executes them as legitimate commands,” he said. “Attackers don't need to bypass firewalls or crack authentication. They trick the AI into weaponizing its own authorized access to internal files, emails, and corporate databases against the user.” Varonis characterized the exploitation technique as “meta-hacking,” social engineering the AI’s reasoning engine into disclosing and then enabling execution of instructions it should not reveal.
What this means for technologists, enterprises, and end users
- Technologists and security teams: Varonis’ finding underscores that a personal-product flaw can expose architectural patterns that affect corporate environments; the researchers emphasize one-click data-exfiltration chains and the need to consider how session-level URL handling and parameter exposure can be abused.
- Affected enterprises and procurement leaders: The research suggests connectors and OAuth integrations—Gmail, Google Drive, Google Calendar, and an assistant’s chat history—can be leveraged for exfiltration if an assistant auto-executes injected prompts during an authenticated session.
- End users and the general public: Varonis’ exploit scenarios center on a single click—links delivered by SMS, email, or QR codes—that load Copilot in an authenticated session and trigger a prompt without visible confirmation.
Varonis says Copilot helped the researchers find the very weakness that enabled the attack; Microsoft planned a patch and a CVE designation, and The Register had not received comment from Microsoft before publication. The finding crystallizes a recurring technical tension in assistant design: how to prevent an AI that reads and reasons over user data from turning that same access into a weaponized channel—without full visibility into every parameter, session condition, and execution path.
