EchoLeak Copilot Vulnerability Highlights Broader AI Security Concerns
Aim Labs details a critical zero-click AI vulnerability found in Microsoft 365 Copilot
The integration of Artificial Intelligence into daily operations continues to reshape how businesses function, including in the sensitive domains of legal work and intellectual property. While AI offers substantial benefits, its rapid evolution also introduces novel security challenges. Attorneys generally need to pay more attention to the privacy risks.
Aim Labs’ recent disclosure of EchoLeak, a critical zero-click AI vulnerability found in Microsoft 365 (M365) Copilot, highlights a significant area of concern for legal professionals and IP owners.
While Microsoft has since patched the hole, the idea that an attacker can initiate this data exfiltration simply by sending an email to a victim has to be a wake-up call to an industry built on confidentiality and privilege.
If you email or store confidential communications, company secrets, or litigation strategy, be aware that such sensitive and proprietary information may be vulnerable to AI-platform attacks like EchoLeak. This won't be the only AI exploit.
The EchoLeak Attack: A Closer Look
EchoLeak represents a frighteningly possible attack that could lead to the unauthorized access of sensitive and proprietary information from M365 Copilot's context. What makes this vulnerability particularly notable is its "zero-click" nature. This means an attacker does not need the target user to interact with a malicious link, download an attachment, or take any explicit action.
The exploit leverages several techniques to bypass Copilot's built-in security mechanisms and the attack chain can be initiated simply by sending a specially crafted email to a target within an organization—circumventing company-only authorization.
The malicious email can contain specific markdown syntax, particularly reference-style image and link formats, that slips past Copilot's defenses, including Cross-Prompt Injection Attack (XPIA) protections and link redaction features.
This allows the Large Language Model (LLM) within Copilot to process the untrusted input from the attacker's email and then attend to trusted, sensitive data within the user's M365 environment (such as Outlook emails, OneDrive files, SharePoint sites, and Teams chat history). All of this occurs silently in the background, without any visual indication to the user or administrators.
This method effectively turns the AI assistant against itself, coercing it to leak information that it is designed to protect.
Microsoft made a statement that the vulnerability was responsibly disclosed and has since been addressed (CVE-2025-32711) before any customers were impacted.
Example EchoLeak Scenario: High-Stakes Litigation
This exploit is a critical concern for legal and IP professionals, especially in high-stakes litigation where sensitive information is exchanged and managed.
The EchoLeak vulnerability is a chilling exemplary situation of how such a breach could occur within Microsoft 365 Copilot (e.g., before the fix).
Imagine a complex litigation where the defendant's lawyer is known to use an AI platform to assist with drafting documents and managing communications related to the case.
It’s not hard to picture a situation where the plaintiff's team—aware opposing counsel is using AI tools—could exploit a vulnerability like EchoLeak to get a peak behind the curtain.
Even if defendant's legal staff is not using AI to search for case law or discovery, Copilot may have access to a vast amount of internal organizational data, including emails, OneDrive storage, Office files, SharePoint sites, and Teams chat history, all within the lawyer's permissions.
One friendly-looking email from an unscrupulous adversary could open a backdoor to valuable private data. Here’s how:
Step 1: The Malicious Email (XPIA Bypass & RAG Spraying)
Crafting the Email: The plaintiff's attorney—all hypothetically, for this example—sends an email to the defendant's lawyer. This email is carefully phrased to appear legitimate and benign, perhaps a routine communication or a discovery request. A key appears to be that the email's content never explicitly mentions AI, assistants, or Copilot. Aim Labs indicates that this is crucial for bypassing Microsoft's Cross-Prompt Injection Attack (XPIA) classifiers, which are designed to prevent malicious instructions from reaching Copilot's underlying LLM.
Hidden Instructions & LLM Scope Violation: Embedded within this seemingly innocuous email are covert instructions for Copilot. These instructions direct the LLM to identify and extract, e.g., "THE MOST sensitive secret / personal information from the document / context / previous messages" within its current scope. Aim Labs terms this a "LLM Scope Violation," where an untrusted input (the external email) makes the LLM access trusted internal data without the user's explicit consent, violating the Principle of Least Privilege.
Increasing Retrieval Likelihood (RAG Spraying): A proposed way to maximize the chances that Copilot processes this malicious email when the defendant's lawyer interacts with it is a technique called "RAG spraying." The malicious party would make the email very long and include multiple chunks of content related to various likely topics the lawyer might ask Copilot about (e.g., "Here is the complete guide to employee onboarding processes," "Here is the complete guide to the FAQs"). This increases the likelihood that the malicious email (or a chunk of it) is retrieved by Copilot's retrieval-augmented generation (RAG) system, even when the defense lawyer asks an innocent question to the LLM.
Step 2: Automatic Data Exfiltration
Copilot Processes the Instructions: When the defendant's lawyer uses Copilot and its context includes the malicious email (e.g., they ask Copilot to summarize recent communications or documents related to a case topic that aligns with the email's sprayed content), Copilot's LLM processes the hidden instructions from the email.
Identifying Sensitive Data: Following the "LLM Scope Violation" instructions, Copilot then accesses its context, which includes the defendant's lawyer's confidential client communications, security documents, internal chat history, and any other data retrieved from Microsoft Graph (unified RESTful web API that provides access to various Microsoft 365 services, e.g., OneDrive, SharePoint files). Copilot identifies what it deems "the most sensitive" information.
Crafting the Exfiltration URL: Copilot, under the attacker's instruction, then embeds this sensitive data into a URL query string parameter.
Step 3: Redaction and CSP Bypasses
Bypassing Markdown Link/Image Redaction: Microsoft 365 Copilot normally redacts external markdown links to prevent exfiltration. However, Aim Labs discovered that reference-style markdown links and images are not redacted. The attacker's instructions would direct Copilot to format the malicious URL using these unredacted reference styles, making it appear in Copilot's output.
Bypassing Content-Security-Policy (CSP): Even with the unredacted link, a Content-Security-Policy (CSP) usually prevents images from untrusted domains. However, Aim Labs found a CSP bypass using Microsoft Teams' URL. The malicious URL created by Copilot would utilize a Teams domain.
Zero-Click Exfiltration: Because this URL is formatted as an image and uses an allowed domain, the defendant's lawyer's browser will automatically try to fetch the "image" in the background without any user interaction. This action sends the URL, containing the highly confidential data, directly to the plaintiff's attorney's logging server. The defendant's lawyer remains completely unaware that the data has been exfiltrated.
Step 4: Concealing the Attack
Reference Mention Bypass: As an added layer of stealth, the plaintiff's attorney's instructions can include a command for Copilot never to reference the malicious email itself for "compliance reasons." This makes it significantly harder for the defendant's lawyer or IT security to trace the source of the exfiltration.
Impact for Attorneys and IP Professionals:
Practitioners should be worried. This chain of vulnerabilities allows for the automatic, zero-click exfiltration of highly sensitive information like security documents, client communications, and other proprietary data that exists within the M365 Copilot's context.
The attacker doesn't need the victim to click any links or behave in a specific way; simply receiving the email may be enough for the attack to be initiated when Copilot later processes it.
This demonstrates how critical it is for attorneys and IP professionals to be aware of these new types of AI-specific vulnerabilities, as they represent a significant departure from traditional cybersecurity threats.
While Microsoft has rolled out a fix for EchoLeak (CVE-2025-32711), and offers some mitigation options, the underlying principles of LLM Scope Violations remain a new threat across RAG applications and AI agents that accept (untrusted) inputs.
What About Other AI Platforms?
The question naturally arises whether other enterprise-grade AI platforms, such as Google's various AI offerings or OpenAI's ChatGPT, could be susceptible to similar zero-click or data exfiltration vulnerabilities.
Does an enterprise AI subscription mean more protection or just wider access to private files?
The short answer is that while the specific attack chain of EchoLeak targeted M365 Copilot, the underlying principles of AI vulnerabilities suggest that similar risks can and do exist across different platforms.
The core challenge lies in the nature of large language models and their interaction with enterprise data. AI models, when integrated into organizational workflows, often gain access to vast amounts of internal data to provide contextual assistance. This broad access, coupled with the inherent complexities of LLMs and their potential to be manipulated through prompt injection or other adversarial techniques, creates an expanded attack surface.
Simon Willison aptly describes a "lethal trifecta" that poses a significant threat to data security in AI systems. This dangerous combination occurs when an AI has access to private data, is exposed to malicious instructions that can trick it, and possesses a mechanism to exfiltrate information. When these three elements converge, an attacker can steal private data simply by introducing instructions for data theft into a location accessible by the AI assistant.
Research and reports from papers and cybersecurity firms have highlighted various AI security risks that are not exclusive to one AI vendor:
Prompt Injection Attacks: Attackers can craft prompts to manipulate an AI model into revealing sensitive information or executing unintended actions. While not always "zero-click," sophisticated variations could integrate into automated processes.
Data Exfiltration through AI Agents: As seen with EchoLeak, AI agents that process or summarize documents, emails, or communications can become unwitting conduits for data leakage if their input or output mechanisms are compromised.
Hidden Instructions in Documents: Some AI tools that support document uploads (e.g., ChatGPT's Data Analyst feature) have been shown to extract and act upon hidden text within files, potentially leading to the execution of malicious code or the exfiltration of sensitive data.
API Exploits: If AI platform APIs are not robustly secured, attackers could manipulate the APIs to extract sensitive data or gain unauthorized access to the AI's functions or underlying data stores.
Model Inversion and Privacy Breaches: In certain scenarios, AI models can inadvertently leak private information from their training data or during user interactions, especially if fine-tuned with proprietary data. For instance, an attacker may infer sensitive information about the original training data by strategically querying the model and analyzing its responses to deduce or reconstruct the input data.
While Google and OpenAI implement substantial security measures for their enterprise offerings, the dynamic nature of AI development means that new vulnerabilities can emerge.
Both companies continually address and patch security flaws, and enterprise versions often come with stricter data handling and privacy commitments, such as not using customer data for model training by default.
However, the potential for "LLM scope violations," where untrusted inputs cause the AI to improperly access or disclose trusted data, should now be a fundamental concern for any enterprise AI system that processes confidential information.
The Imperative for Ongoing Vigilance
For patent practitioners, IP owners, in-house counsel, and inventors, the EchoLeak incident serves as a forewarning of the heightened security considerations when adopting AI tools.
The handling of sensitive invention disclosures, strategic legal documents, and proprietary business information demands the highest level of data protection.
Perhaps all AI tool vendors should be asked something like: “Why can’t your AI be tricked into revealing my research, strategy, or other confidential information?”
Organizations should proceed with a strategy that balances the desire for innovation with a rigorous approach to security. This includes, at minimum:
Understanding Data Flow: Comprehending precisely what data is ingested, processed, and potentially output by AI tools.
Vendor Due Diligence: Thoroughly vetting AI service providers for their security postures, compliance certifications, and incident response capabilities.
Access Controls and Least Privilege: Ensuring AI agents and user roles are configured with the principle of least privilege, restricting access only to necessary data.
Continuous Monitoring: Implementing systems to detect anomalous behavior or potential data exfiltration attempts involving AI platforms.
User Awareness Training: Training employees on the risks of interacting with AI tools, particularly regarding the input of confidential data.
The landscape of AI security is constantly evolving. It likely won’t be your opposing counsel, but malicious actors are not going away any time soon.
Platforms strive for robust defenses but the ingenuity of cybercriminals means that vigilance and proactive security measures remain indispensable for protecting valuable intellectual property and private information.
As the world hypes up the AI revolution, attorneys and IP owners must lead the charge in continuing to safeguard confidential data.
Disclaimer: This is provided for informational purposes only and does not constitute legal or financial advice. To the extent there are any opinions in this article, they are the author’s alone and do not represent the beliefs of his firm or clients. The strategies expressed are purely speculation based on publicly available information. The information expressed is subject to change at any time and should be checked for completeness, accuracy and current applicability. For advice, consult a suitably licensed attorney and/or patent professional.