Microsoft Defender XDR False Positive Exposes 1,700+ Sensitive Documents in Public Data Leak

A critical false positive in Microsoft Defender XDR triggered a chain reaction that led to the public exposure of over 1,700 sensitive documents from hundreds of organizations.¹

This incident highlights the risks of automated security tooling, the importance of user education, and the need for robust privacy controls in cloud-based analysis platforms.

Key Takeaways

• A Defender XDR false positive led to the mass upload of sensitive Adobe Acrobat files to ANY.RUN’s public sandbox.¹
• Over 1,700 confidential documents from hundreds of organizations were exposed and indexed for public download.²
• The root cause was a misclassification of benign Adobe URLs, compounded by default public sharing in free-tier sandboxes.¹
• ANY.RUN responded quickly by setting affected analyses to private, but Microsoft has not yet issued an official advisory.¹
• Security teams must balance detection accuracy, enforce sandboxing policies, and educate users on privacy risks.

On April 24, 2025, Microsoft Defender XDR mistakenly classified legitimate Adobe Acrobat Cloud URLs as malicious, prompting users to submit their flagged files to ANY.RUN’s online sandbox—where free-tier uploads default to public sharing. ¹

Incident Overview

Microsoft Defender XDR, part of the Microsoft 365 Defender suite, uses machine learning to detect threats across cloud and endpoint environments.

During a routine scan, the platform misidentified URLs under acrobat[.]adobe[.]com/* as malicious indicators. Concerned users then turned to the free tier of ANY.RUN, a popular malware analysis sandbox, to verify the alerts.

Because ANY.RUN’s free plan defaults to public file sharing, more than 1,700 Adobe Acrobat documents containing proprietary and confidential data from hundreds of organizations became publicly accessible. ²

Technical Analysis: Why Did This Happen?

Microsoft Defender XDR relies on machine learning and heuristic models to detect suspicious activity. In this case, the model incorrectly flagged legitimate Adobe Acrobat Cloud links as malicious.¹

False positives are a known risk in automated security tools, especially when detection models are tuned aggressively to catch emerging threats. The lack of context-aware filtering led to benign URLs being treated as indicators of compromise.

ANY.RUN, like many public sandboxes, defaults to public sharing for free-tier uploads. Many users were unaware of this, and the workflow of copying-and-pasting flagged files into external tools led to the unintended exposure.² The incident demonstrates how the intersection of automated detection and user behavior can create new, unexpected risks.

How the Leak Occurred

• False Positive Trigger: Defender XDR flagged benign Adobe Acrobat Cloud links, labeling them as potential malicious activity. ¹
• Free-Tier Sandbox Default: Many users, seeking quick verification, uploaded their documents to ANY.RUN’s free sandbox, unaware that uploads would be indexed publicly. ³
• Public Exposure: The sandbox’s default settings allowed any visitor to view and download the uploaded files, leading to a large-scale unintentional data breach.

Impact and Response

The exposed documents included internal reports, technical specifications, executive presentations, and other sensitive corporate content—potentially enabling industrial espionage, privacy violations, or regulatory non-compliance.

Within hours of discovery, ANY.RUN changed the privacy settings for affected analyses to private mode and issued warnings: “Always use a commercial license for work-related tasks to ensure privacy and compliance.” ⁴ Microsoft has yet to publish an official advisory but is investigating the root cause of the misclassification.

Industry and Vendor Response

ANY.RUN acted quickly to remediate the exposure, setting all affected analyses to private and issuing public guidance.¹² The company emphasized the importance of using commercial licenses for sensitive or work-related files to ensure privacy and compliance. Microsoft has not yet released an official advisory as of publication, but is reportedly investigating the misclassification and reviewing its detection models.

Security experts have pointed to the need for more transparent communication from vendors when false positives have the potential to trigger widespread user action and data exposure.

Lessons for Security Teams

• Balanced Detection Tuning: Security platforms must calibrate machine learning models to minimize false positives without sacrificing threat coverage.
• Sandboxing Policies: Organizations should enforce strict guidelines on the use of public analysis tools, reserving them for non-sensitive workflows or licensed private tiers.
• User Education: End users need clear instructions on safe handling of alerts and the privacy implications of external tools.
• Incident Response: Integrate threat detection with approved internal sandbox environments and ensure rapid communication channels with vendors for false positive resolution.

Historical Context: Other Security Tool False Positives

This is not the first time automated security tools have triggered large-scale data exposure. Previous incidents have shown that aggressive detection tuning or threat intelligence errors can lead to widespread operational disruptions and data exposure. These incidents underscore the need for continuous tuning, transparent vendor communication, and user training to mitigate the risks of automation in security operations.

Additional Recommendations

• Review and update internal workflows for handling security alerts, especially those involving external tools.
• Establish clear policies for sandbox use and privacy settings.
• Maintain open communication with security vendors and promptly report suspected false positives.
• Regularly audit detection models and feedback loops to improve accuracy over time.

Final Thoughts

The Microsoft Defender XDR false positive incident underscores that even trusted security tools can inadvertently cause massive data exposure if automated alerts drive user behavior into unsecured channels.

As cloud-based security and analysis services proliferate, maintaining robust configuration, user training, and vendor collaboration is critical to preventing similar leaks.

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