Microsoft confirmed that a bug in its Copilot Chat feature allowed the AI assistant to access and surface confidential emails that users were not authorized to view. The flaw, which affected Microsoft 365 Copilot, meant the tool could pull sensitive messages when responding to queries or summarizing content. The disclosure arrives at a time when security researchers have separately documented how large language model assistants can be weaponized through prompt injection to exfiltrate private data, raising hard questions about how much trust enterprises should place in AI tools that sit on top of their most sensitive communications.
Copilot Chat Exposed Emails It Should Not Have Seen
The bug caused Microsoft’s Copilot Chat to retrieve and display email content that should have been off-limits to the person asking. Rather than respecting existing access controls, the AI tool treated restricted messages as fair game when generating answers or summaries. Microsoft acknowledged the error and stated it had identified and fixed the issue, adding that a small number of users were affected and that the company found no evidence of data misuse. The company’s confirmation was reported by the BBC in February 2026, highlighting that Copilot could surface emails beyond what a user should normally be able to access.
What makes this incident particularly uncomfortable for Microsoft is the product’s positioning. Copilot is marketed as a productivity layer that integrates directly with Outlook, Teams, and other Microsoft 365 apps. That tight integration is the selling point, but it also means any failure in permission enforcement can cascade across an organization’s email, calendar, and document ecosystem. A single misconfigured trust boundary in the AI layer does not just expose one file; it potentially opens a window into an entire mailbox. Microsoft has not publicly released a detailed technical advisory explaining the root cause or the specific patch, leaving IT administrators to rely on the company’s assurance that the problem has been resolved and hoping that similar logic errors are not lurking in adjacent Copilot features.
A Separate Exploit Shows Deeper Risks in 365 Copilot
The Copilot Chat bug is not the only security concern tied to Microsoft’s AI assistant. A separate and technically distinct vulnerability class has been documented in a preprint paper titled “EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System,” which carries the designation CVE-2025-32711. That research, published on arXiv, describes a zero-click prompt injection attack against Microsoft 365 Copilot, meaning an attacker could trigger data exfiltration without the victim clicking anything or taking any deliberate action. The exploit works by embedding hidden instructions in content that Copilot processes, effectively hijacking the AI’s output to leak information across trust boundaries and into channels visible to the attacker.
The EchoLeak vulnerability and the Copilot Chat bug are not the same flaw. They differ in mechanism, scope, and discovery timeline. But taken together, they illustrate a pattern: LLM copilots that operate inside enterprise environments can cross trust boundaries and expose data through vectors that traditional security tools were never designed to catch. Conventional email security focuses on phishing filters, encryption, and access control lists. None of those defenses account for an AI assistant that might be tricked, or simply misconfigured, into serving up messages it was never supposed to read. The EchoLeak paper demonstrates that this is not a theoretical concern but a real-world attack surface that has been proven in a production system, forcing security teams to treat model behavior and prompt handling as first-class risk factors rather than academic curiosities.
Why AI Email Access Creates a New Class of Exposure
Traditional software bugs that leak data tend to follow predictable patterns: a misconfigured database, an unpatched server, a broken authentication token. Security teams know how to scan for these issues and have decades of tooling built around them. AI assistants like Copilot introduce something different. They sit between the user and the data store, interpreting natural language queries and deciding in real time which documents, emails, and files to retrieve. That decision-making process is opaque even to the engineers who build it, because large language models do not follow deterministic rule sets. They generate probabilistic responses shaped by training data, context windows, and retrieval-augmented generation pipelines, which can behave in surprising ways when exposed to adversarial prompts or edge-case inputs.
This opacity creates a gap that most enterprise security frameworks have not yet addressed. When an IT administrator sets a permission on a mailbox, the expectation is that only authorized users and applications will honor that permission. But if Copilot’s retrieval layer fails to check permissions correctly, or if a prompt injection overrides the model’s instructions, the permission becomes meaningless. The result is a new class of exposure where the AI itself becomes the attack surface. Organizations that deploy Copilot are effectively trusting Microsoft’s AI layer to enforce the same access boundaries that Exchange and SharePoint have enforced for years, but through a fundamentally less predictable mechanism. That shift turns every natural-language query into a potential security event, even if the underlying storage systems remain properly locked down.
Enterprise Trust Hangs on Invisible Guardrails
Microsoft’s rapid fix of the Copilot Chat bug suggests the company is treating these issues seriously, but the incident exposes a tension at the heart of enterprise AI adoption. Businesses are being told to integrate AI assistants into their most sensitive workflows, from legal review to executive communications, on the promise that existing security policies will carry over seamlessly. The Copilot Chat error shows that promise can break down in practice. When it does, the consequences are not abstract. Confidential merger discussions, personnel actions, legal strategy memos, and financial projections could all be surfaced to employees who were never meant to see them, with little technical friction and no obvious warning that anything has gone wrong.
The challenge for IT leaders is that they currently have limited visibility into how Copilot processes and retrieves information. Microsoft does not publish granular audit logs showing which emails Copilot accessed for a given query, making it difficult for security teams to detect when the AI oversteps its bounds or reconstruct what happened after a suspected incident. Without that transparency, organizations are left relying on Microsoft’s internal testing and bug reports from users who happen to notice something wrong. That is a fragile model for protecting sensitive communications, especially in regulated industries like finance, healthcare, and law, where unauthorized access to email content can trigger compliance violations, regulatory reporting obligations, and long-term reputational damage.
What Needs to Change Before AI Reads Your Inbox
The most common critique of incidents like this focuses on the vendor: Microsoft should have caught the bug sooner, tested more rigorously, and disclosed more transparently. That critique is fair but incomplete. The deeper issue is structural. Enterprises are layering AI assistants on top of data stores that were designed with static access controls, and they are assuming that a probabilistic model will behave like a traditional, rule-based application. To close that gap, organizations need new guardrails that treat the AI layer as untrusted until proven otherwise. That could include enforcing permission checks outside the model, limiting which data sources Copilot can touch for particular user groups, and introducing explicit “least privilege” scopes for AI queries instead of granting blanket access to entire mailboxes or document libraries.
At the same time, buyers should push for stronger transparency and control from AI vendors. For Copilot and similar tools, that means detailed audit trails that show which messages and files were accessed for each response, robust configuration options to disable high-risk capabilities in sensitive departments, and clear documentation of how vulnerabilities like the Copilot Chat bug and EchoLeak were discovered, mitigated, and tested against regression. Until those safeguards are in place, letting an AI assistant read corporate inboxes is not just a question of productivity gains; it is a bet that invisible guardrails will hold under pressure. The recent disclosures suggest that bet remains riskier than many enterprises have been led to believe, and that security teams should treat AI email access as a live, evolving threat surface rather than a solved problem.
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*This article was researched with the help of AI, with human editors creating the final content.
