By Swaroop Morajkar Cybersecurity Researcher & Technical Writer Connect on LinkedIn: http://linkedin.com/in/swaroop-morajkar-83071a260/
Imagine receiving a normal business email.
No malicious attachment. No suspicious link. No malware.
A few moments later, your organization's AI assistant begins leaking internal emails, documents, and confidential information to an attacker.
You never clicked anything.
This wasn't science fiction.
In 2025, security researchers disclosed EchoLeak (CVE-2025-32711), a vulnerability affecting Microsoft 365 Copilot. By hiding carefully crafted instructions inside content that Copilot could read, attackers were able to manipulate the AI assistant into exposing sensitive information through a zero-click attack chain.
EchoLeak revealed something many organizations were beginning to overlook:
The attack surface of AI applications is not limited to servers, APIs, or databases.
Every prompt, document, email, web page, and external data source consumed by an LLM can become part of the attack surface.
To understand why vulnerabilities like EchoLeak occur, we first need to understand what an AI attack surface actually is.
In traditional applications, the attack surface usually consists of components such as web interfaces, APIs, databases, authentication systems, and network services.
LLM applications introduce a much larger attack surface.
An AI system does not operate only on code. It continuously consumes and processes natural language from users, documents, emails, web pages, APIs, vector databases, plugins, and external tools.
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In AI systems, data itself can become an attack vector. Unlike traditional applications, LLMs consume instructions and content through the same interface.
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