EchoLeak: The Critical Zero-Click Data Leak Flaw in Microsoft 365 Copilot

In a landmark revelation for the security of AI-integrated productivity suites, researchers have uncovered a zero-click data leak flaw in Microsoft 365 Copilot—an AI assistant embedded in Office apps such as Word, Excel, Outlook, and Teams. Dubbed 'EchoLeak,' this vulnerability casts a spotlight on the emerging risks of large language models (LLMs) embedded in real-world business environments, demonstrating how seemingly secure and isolated AI-driven tools can inadvertently expose sensitive internal data without any user interaction. The rapid proliferation of AI copilots across enterprise environments makes lessons from the EchoLeak incident crucial for future-proofing organizational cybersecurity.

Understanding EchoLeak: Anatomy of a Zero-Click AI Data Leak​

EchoLeak is not a traditional software flaw; rather, it represents a new and insidious class of vulnerabilities termed 'LLM Scope Violation.' With EchoLeak, an attacker can exfiltrate sensitive data from a victim's context, relying on the architecture and operational flow of Microsoft 365 Copilot itself. The vulnerability was discovered in January 2025 by security researchers at Aim Labs and was promptly reported to Microsoft, which assigned it the identifier CVE-2025-32711 and classified it as a critical information disclosure flaw. According to Microsoft's incident report and confirmations by Aim Labs, the flaw was fixed in May via a server-side patch, requiring no user action, and—at the time of writing—there is no evidence of exploitation in the wild.
What sets EchoLeak apart is its method of operation. Unlike standard malware or phishing attacks that trick users into clicking links or opening attachments, EchoLeak is truly “zero-click”—it enables the exfiltration of data without the victim ever interacting with the malicious payload. It leverages how LLMs, such as those powering Microsoft 365 Copilot, blend prompt augmentation, retrieval-augmented generation (RAG), and deep contextual awareness to deliver user-specific responses.

The Attack Chain: How EchoLeak Works​

The EchoLeak exploit begins with the delivery of a seemingly innocuous email to the target. This message looks like any routine business correspondence and contains no suspicious attachments or links. Hidden within its body, however, is a cleverly crafted prompt injection—an instruction designed to execute once ingested by the LLM’s reasoning engine.
Key features of the attack chain include:

• Step 1: Covert Prompt Injection
• The attacker embeds a concealed prompt into the body of the email. Importantly, this prompt is written in a way that makes it appear like a legitimate message, evading traditional classifiers and security filters such as Microsoft’s own XPIA (cross-prompt injection attack) detection.
• Step 2: Contextual Triggering
• At a later time, when the user queries Copilot with a business-related question, Copilot’s retrieval-augmented generation (RAG) mechanism identifies the earlier malicious email as contextually relevant. It surfaces the email for the LLM’s analysis, introducing the malicious prompt into the AI's working memory.
• Step 3: Prompt Execution and Data Exfiltration
• Now operating within the LLM's context, the injected prompt instructs Copilot to identify sensitive internal data—potentially including proprietary documents, financial information, or confidential messages—and to package this data inside a specially crafted URL or image tag.
• Step 4: Outbound Data Leak
• When the output is rendered in a client (such as Outlook or Teams), some markdown image formats force the user's browser to load the image, sending a request to an external server. In doing so, it propagates the embedded sensitive data to the attacker’s endpoint, all without any user interaction.

Notably, the exploit cleverly abuses trusted domains such as Microsoft Teams and SharePoint, which are whitelisted in many corporate environments, to facilitate outbound callbacks without triggering common security controls.

The Broader Context: Microsoft 365 Copilot’s Deep Integration​

To grasp why EchoLeak is so significant, it’s critical to understand Microsoft 365 Copilot’s architecture and operational philosophy. By design, Copilot functions as an embedded generative AI assistant, drawing on internal organizational data—files, emails, chats, calendar entries, and more—from Microsoft Graph. It processes this data using OpenAI’s GPT language models to generate smart suggestions, compose emails and documents, automate workflows, and answer business questions.
This wide-scoped contextual analysis, coupled with seamless RAG integration, means Copilot has access to a rich tapestry of sensitive business data. While this design philosophy maximizes productivity and democratizes access to organizational knowledge, it also introduces risks. Sensitive, privileged data is routinely brought into the working context of an LLM, and unless proper scoping and filtration are enforced, this data can inadvertently be included in LLM outputs—a vector perfectly illustrated by EchoLeak.

Critical Analysis: Strengths, Impact, and Lingering Risks​

Strengths in Microsoft’s Response​

Microsoft’s handling of the EchoLeak flaw demonstrates certain operational strengths, including:

• Rapid Disclosure-to-Fix Timeline
• Aim Labs disclosed the vulnerability in January, and Microsoft issued a server-side fix by May—an impressive timeframe, particularly given the complexity of AI-driven platforms.
• Transparency and CVE Assignment
• Microsoft responded by assigning a public CVE (CVE-2025-32711), rating the issue as critical, and publishing guidance alongside assurance there was no observed in-the-wild exploitation.
• Server-Side Mitigation
• The company deployed the fix server-side, meaning enterprises and end users required no manual intervention—critical in ensuring rapid, universal remediation.

Areas Requiring Scrutiny​

However, EchoLeak is emblematic of deeper architectural challenges facing both Microsoft and the growing ecosystem of enterprise AI copilots:

• Prompt Injection Remains a Persistent Threat
• EchoLeak shows that prompt injection attacks, long theorized within the AI ethics and security community, can be weaponized at scale. As LLMs increasingly compose, summarize, and connect disparate business communications, even mundane artifacts such as emails can become attack vectors.
• LLM Scope Violation: A New Paradigm
• Unlike classic software vulnerabilities, LLM scope violations exploit the blurred boundary between context and privilege within AI systems. Inputs considered harmless in human interaction become dangerous when processed by context-hungry AIs, which may pair them with confidential data during inference.
• Trusted Domain Abuse
• Microsoft 365 Copilot’s reliance on trusted internal domains, combined with markdown rendering quirks, means that whitelisting no longer guarantees security. Any external delivery channel—email, Teams chats, SharePoint docs—can serve as a vector for delivery and exfiltration.
• Automated, Scalable Risk
• Most critically, EchoLeak’s zero-click nature means it can be scaled across enterprise environments. Adversaries can automate the injection process, silently exfiltrating data en masse without triggering user suspicion.

Downstream Effects for Enterprise Security​

The EchoLeak incident highlights the limits of traditional AI input validation and the need for defenses purpose-built for the LLM era. The following are notable impacts and lessons:

• Prompt Injection Classifiers Are Only a Partial Defense
• Microsoft’s XPIA classifier, designed to filter prompt attacks, was defeated by EchoLeak’s natural language concealment. This indicates classifiers need to be supplemented with more robust, context-aware threat models.
• Granular Input Scoping and Output Filtering
• Enterprises must enforce rigorous input scoping—ensuring LLMs never ingest untrusted content without curation—and apply post-processing filters to outputs, blocking or sanitizing external URLs, images, and structured data.
• Restricting External Communication
• RAG engines should, where feasible, be configured to avoid retrieving or referencing potentially malicious documents, especially those capable of invoking external web requests.
• Continual AI Red-Teaming
• Organizations deploying AI copilots should perform regular red-teaming, simulating both prompt injection attacks and data exfiltration attempts, to audit their exposure.

The Bigger Picture: AI Integration and Organizational Risk​

With the accelerating adoption of AI assistants like Microsoft 365 Copilot, organizational data is being interwoven with generative models at unprecedented scale. This yields enormous productivity benefits but exposes businesses to novel, hard-to-detect risks:

Advantages and Transformative Power​

• Productivity Gains
• Automation of boring, repetitive tasks and the ability to distill massive data volumes on-demand.
• Enhanced research, writing, and data analysis capabilities accessible to non-technical users.
• Unified Data Access
• AI copilots can synthesize content across emails, chats, documents, and databases, surfacing hidden insights previously trapped in silos.

Risks That Can No Longer Be Ignored​

• Invisible Data Movement
• The seamless flow of data within LLM contexts means that sensitive information can move between seemingly unrelated organizational compartments, often without audit trails.
• Attack Surface Multiplication
• Every business message, file, or chat becomes a potential conduit for prompt injection, increasing the number of attack surfaces without traditional security hooks.
• Trust Erosion in Internal Channels
• When data can be exfiltrated via trusted platforms such as SharePoint or Teams, defenders can no longer rely on classic blocklists or URL filtering, especially as adversaries mimic legit business workflows.

Recommendations: Securing the Next Generation of Copilot AI​

To resist the next class of AI-driven exploits, enterprises and software vendors need to adopt a multi-layered, adaptive approach to security. The EchoLeak incident offers several actionable insights:

1. Strengthen AI Input Validation Pipelines​

• Standardize and enforce input validation well before user content ever reaches LLMs.
• Combine rule-based filters, anomaly detectors, and context-aware classifiers to spot concealed injection attempts.
• Use explainable AI models to audit and interpret classifier decisions.

2. Implement Granular Context Isolation​

• Limit the scope of RAG engines so only explicitly whitelisted, trustworthy documents can be retrieved for context.
• Monitor and restrict the “context window” of LLMs, ensuring that sensitive documents are not automatically surfaced in response to generic user queries.

3. Harden Output Pathways​

• Apply post-processing filters to all LLM responses, scrubbing potentially dangerous constructs such as markdown images, external URLs, and embedded scripts.
• Block or warn on any attempts to generate content containing dynamic links or referencing non-corporate domains, even when surfaced via upstream AI logic.

4. Continual AI Red-Teaming and Audit​

• Establish continuous red-teaming programs focused on AI copilots, targeting realistic scenarios of prompt injection, privilege escalation, and silent exfiltration.
• Audit LLM usage logs to retrospectively detect anomalous patterns that could indicate abuse or attempted data theft.

5. Educate Users and Security Teams​

• Train users to recognize subtle signs of AI-driven attacks, such as unexpected image requests, document summaries, or auto-generated communications.
• Equip security operations with AI-specific playbooks, ensuring incident response is attuned to data moving within and between LLMs.

6. Vendor Accountability and Collaboration​

• Advocate for transparency from vendors on LLM security architectures, including detailed documentation on RAG, input filtering, and output post-processing facilities.
• Insist on regular, independent security testing of AI copilots and rapid, transparent communication when vulnerabilities emerge.

Conclusion: EchoLeak as an Inflection Point for AI Security​

The EchoLeak vulnerability—while fixed without observed exploitation—marks a pivotal turning point in the defense of enterprise AI. It crystallizes the emergent reality that as LLMs become foundational to business operations, so too do the risks they bring, bridging gaps between previously isolated data pools and opening new, zero-click avenues for compromise.
Looking ahead, the lesson for organizations and developers alike is clear: classic security models will not suffice for the LLM era. The invisible, contextual, and compositional logic of generative AI introduces risks far more subtle and insidious than those of traditional software bugs. Defending against these threats will require fresh thinking, constant vigilance, and a willingness to evolve alongside the pace of AI development.
As the AI Copilot revolution sweeps across industry, the EchoLeak incident stands as both a warning and a guide—a reminder that for all the value AI brings, security must remain an ever-evolving, first-class concern. Being proactive—rather than reactive—will be the only way to keep tomorrow’s sensitive data from becoming the next headline-grabbing leak.

---

Source: BleepingComputer Zero-click AI data leak flaw uncovered in Microsoft 365 Copilot