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A brand new assault dubbed ‘EchoLeak’ is the primary identified zero-click AI vulnerability that permits attackers to exfiltrate delicate information from Microsoft 365 Copilot from a consumer’s context with out interplay.
The assault was devised by Aim Labs researchers in January 2025, who reported their findings to Microsoft. The tech large assigned the CVE-2025-32711 identifier to the data disclosure flaw, score it crucial, and stuck it server-side in May, so no consumer motion is required.
Also, Microsoft famous that there is no proof of any real-world exploitation, so this flaw impacted no clients.
Microsoft 365 Copilot is an AI assistant constructed into Office apps like Word, Excel, Outlook, and Teams that makes use of OpenAI’s GPT fashions and Microsoft Graph to assist customers generate content material, analyze information, and reply questions based mostly on their group’s inside information, emails, and chats.
Though mounted and by no means maliciously exploited, EchoLeak holds significance for demonstrating a brand new class of vulnerabilities referred to as ‘LLM Scope Violation,’ which causes a big language mannequin (LLM) to leak privileged inside information with out consumer intent or interplay.
As the assault requires no interplay with the sufferer, it may be automated to carry out silent information exfiltration in enterprise environments, highlighting how harmful these flaws could be when deployed towards AI-integrated methods.
How EchoLeak works
The assault begins with a malicious electronic mail despatched to the goal, containing textual content unrelated to Copilot and formatted to appear to be a typical enterprise doc.
The electronic mail embeds a hidden immediate injection crafted to instruct the LLM to extract and exfiltrate delicate inside information.
Because the immediate is phrased like a traditional message to a human, it bypasses Microsoft’s XPIA (cross-prompt injection assault) classifier protections.
Later, when the consumer asks Copilot a associated enterprise query, the e-mail is retrieved into the LLM’s immediate context by the Retrieval-Augmented Generation (RAG) engine because of its formatting and obvious relevance.
The malicious injection, now reaching the LLM, “methods” it into pulling delicate inside information and inserting it right into a crafted hyperlink or picture.
Aim Labs discovered that some markdown picture codecs trigger the browser to request the picture, which sends the URL mechanically, together with the embedded information, to the attacker’s server.
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Source: Aim Labs
Microsoft CSP blocks most exterior domains, however Microsoft Teams and SharePoint URLs are trusted, so these could be abused to exfiltrate information with out downside.
Source: Aim Labs
EchoLeak might have been mounted, however the growing complexity and deeper integration of LLM functions into enterprise workflows are already overwhelming conventional defenses.
The identical development is sure to create new weaponizable flaws adversaries can stealthily exploit for high-impact assaults.
It is necessary for enterprises to strengthen their immediate injection filters, implement granular enter scoping, and apply post-processing filters on LLM output to dam responses that comprise exterior hyperlinks or structured information.
Moreover, RAG engines could be configured to exclude exterior communications to keep away from retrieving malicious prompts within the first place.
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