In the tech world, vulnerabilities come and go, but the story of 'EchoLeak' caught many by surprise. This zero-click flaw, which was recently patched, posed a potential threat to Microsoft 365 Copilot users by enabling silent data theft through seemingly harmless emails. Although it was never exploited in the wild, EchoLeak has spotlighted significant security risks tied to AI and LLM Scope Violations. As AI continues to integrate into enterprise environments, understanding these risks becomes crucial for safeguarding sensitive data.
What Datapoints Were Leaked?
The EchoLeak vulnerability made headlines for a reason: it exposed highly sensitive information from Microsoft 365 Copilot without any user interaction. Let’s break down what was actually at risk.
Types of Data at Risk
EchoLeak allowed attackers to silently pull data that most users would consider off-limits:
Full email contents – including attachments, message history, and embedded files.
Calendar details – meeting links, invitees, times, and locations.
Documents from SharePoint and OneDrive – any files the user had access to, even if not directly referenced in the email.
Personal notes and Teams chat summaries – any text or summaries Copilot could access.
How Did the Leak Happen?
The real problem came down to something called an LLM Scope Violation. Here’s what that means in plain English: Large Language Models (LLMs), like those powering Copilot, are trained to answer user prompts by pulling in relevant data. If an attacker could trick Copilot into thinking a malicious email was a legitimate user prompt, it would dutifully gather and send back any information within its allowed scope—without asking questions.
No clicks required: EchoLeak was a “zero-click” attack. All it took was a cleverly crafted email.
Silent exfiltration: The information would be delivered back to the attacker, hidden in a response, without the user ever knowing.
The Mechanics of Data Exfiltration
The attacker sent an email with hidden prompts designed to activate Copilot’s data-fetching skills. Since the AI viewed the email as a trusted input, it responded with whatever data matched the prompt, including files, emails, or calendar events tied to the user’s Microsoft 365 account.
LLM Scope Violations are particularly dangerous because the AI doesn’t distinguish between a user’s real request and a sneaky prompt buried in an email. This flaw isn’t about a bug in the code—it’s about the AI being too helpful with too much access.
When thinking about solutions, some companies like Cloaked are focusing on tighter controls—limiting what data AI assistants can access and making sure every request is authenticated and tracked. This kind of approach can help limit the fallout from similar vulnerabilities in the future.
Should You Be Worried?
AI security isn’t just an IT problem—it’s a real-world issue that affects everyone, from individual users to massive enterprises. The fallout from data breaches or AI mishaps can be immediate and severe. Here’s what’s at stake:
Personal Data at Risk
When AI systems mishandle data, it’s not just abstract numbers. Usernames, passwords, financial details, and private communications could all be exposed. For someone whose data is compromised, the consequences can be felt for years—think identity theft, financial loss, or reputational damage.
Scope Violations: Large Language Models (LLMs) sometimes access or reveal data beyond their intended boundaries. If an LLM trained on sensitive internal conversations accidentally discloses them, it’s not just embarrassing—it can be disastrous.
Data Persistence: Even after deleting data from a platform, traces might linger in AI training sets, making it hard to control your digital footprint.
Enterprise: Bigger Stakes, Bigger Headaches
Enterprises hold mountains of sensitive information—customer data, trade secrets, internal documents. A single AI security slip-up can have ripple effects:
Compliance Nightmares: Failing to protect data can lead to regulatory fines and loss of business trust.
Supply Chain Risks: If your AI tool connects with partners or vendors, one weak link can expose everyone.
Insider Threats: Employees using AI tools might inadvertently leak confidential data, especially if security isn’t front and center.
Zero-Click Vulnerabilities: The Silent Threat
Zero-click vulnerabilities are particularly scary—they let attackers compromise an AI system without any action from the user. No suspicious link to click, no sketchy attachment to open. Just business as usual, and your data could be in someone else’s hands.
Stealth Attacks: These vulnerabilities are hard to detect and patch, often going unnoticed until damage is done.
AI Blind Spots: Many organizations haven’t updated their risk assessments for these new attack methods.
Where Cloaked Fits In
Given these risks, Cloaked’s product stands out for those who are serious about protecting sensitive data. By automatically detecting and masking personal information before it ever hits AI applications, Cloaked reduces exposure—especially in scenarios where zero-click vulnerabilities or scope violations could spell disaster. It’s not about adding more barriers, but about quietly locking the right doors before trouble even starts.
Security in AI isn’t just a box to check. It’s about protecting people—your users, your team, yourself—from risks that are growing sharper by the day.
What Should Be Your Next Steps?
When an AI-driven data breach hits the headlines, it’s easy to feel like the house is already on fire. But the truth is, there are clear, actionable steps you can take today to make sure you’re not tomorrow’s cautionary tale.
Immediate Actions to Strengthen Security
1. Review Current Filters and Controls
Audit your prompt injection filters. If you haven’t updated them in a while, assume they’re outdated.
Check your input validation logic. Don’t just rely on basic sanitization—think about edge cases.
2. Limit Data Exposure
Apply input scoping: Restrict what data the language model can access. If a user doesn’t need certain info, the AI shouldn’t see it either.
Segment sensitive data from general-access databases. Never put all your eggs in one basket.
3. Update Incident Response Plans
Make sure your playbook covers AI-specific threats like prompt injection and output manipulation.
Test your team’s response with drills focused on LLM (Large Language Model) risks.
Strengthening Prompt Injection Filters and Input Scoping
Prompt injection isn’t just a theoretical risk. Attackers get creative—sometimes it’s as simple as a cleverly worded input. Here’s what you can do:
Enhance prompt filters with context-aware logic. Don’t just block banned words; look for suspicious input patterns.
Scope permissions tightly. Give LLMs the smallest possible window into your data. If a prompt asks for more than it should, the request should hit a wall.
Use allow-lists instead of block-lists wherever possible. It’s easier to manage and safer in the long run.
Enterprise Strategies to Secure LLM Applications
Enterprises face bigger stakes. One slip-up can mean massive data leaks or PR nightmares. Here’s how to get serious about security:
Continuous Monitoring: Track LLM interactions for unusual patterns or unauthorized data requests. Anomalies are often the first sign of trouble.
Access Controls: Enforce strict authentication. Limit who can access the AI and what they can ask of it.
Employee Training: Make sure everyone—devs, admins, even marketing—understands the risks of prompt injection and how to spot it.
Cloaked’s privacy layersteps in at this point. By automatically detecting and masking sensitive data before it ever hits the LLM, Cloaked’s solution makes prompt injection a much harder trick for attackers to pull off. It’s a safety net, catching issues before they can cause harm.
The Bottom Line
If you’re handling customer data with LLMs, treat every prompt like a loaded question. Scrutinize your filters, lock down your input scopes, and don’t let your guard down—because attackers certainly won’t.
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*Disclaimer: You agree not to use any aspect of the Cloaked Services for FCRA purposes.
