TAOKernelEx64_ev.sys file report

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• Overview
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This report summarizes the file identity, detection status, publisher metadata, observed locations, and technical indicators for TAOKernelEx64_ev.sys. ThreatInfo does not have a final classification for this sample yet.

TAOKernelEx64_ev.sys is a Windows file recorded in the ThreatInfo database. It is associated with 腾讯电脑管家. The reported company name is Tencent. The current detection status is Undefined, based on the latest analysis from 2024-08-09 23:02:37 (2 years ago).

ThreatInfo does not have a final classification for this file yet. Use the technical details below to compare the hash, size, signature, and observed locations with the copy found on your device.

File Details

Overview

The signature on TAOKernelEx64_ev.sys is reported as valid. A valid signature helps confirm publisher identity, but it does not automatically make the file safe if the installer was bundled, abused, or downloaded from an untrusted source.

ThreatInfo has observed TAOKernelEx64_ev.sys in the locations listed above. Files found in temporary folders, user profile folders, startup locations, or unusual application directories should be reviewed more carefully than files installed under a known program directory.

The strongest geographic signal for this file is Nigeria with 100.0% of observed hits. Geographic distribution can help identify targeted campaigns, regional software bundles, or where a file is most commonly reported.

The most common operating system signal for TAOKernelEx64_ev.sys is Windows 10 with 100.0% of observed hits. If your system differs from the common profile, check whether the file was introduced by a specific installer, archive, or removable device.

Analysis

TAOKernelEx64_ev.sys is identified as pe for 64 systems. The subsystem is Native. PE header values are useful for triage, especially when they do not match the expected publisher, product, or release timeline.

PE section names and hashes can reveal packing, injected resources, or unusual build artifacts. Sections with uncommon names, very large raw data, or hashes that differ from a trusted copy deserve additional review.