Decoding VirusTotal Results: What You Need to Know
When it comes to analyzing the results from VirusTotal, it’s essential to approach the data with a discerning eye. It’s a common misconception to consider a file as “probably clean” just because only a handful of antivirus (AV) solutions flag it as potentially malicious. I once shared this flawed thinking, but I’ve since come to understand the nuances of the system.
A Valuable Resource
For those looking to deepen their understanding of VirusTotal, I highly recommend watching a comprehensive overview by MalwareAnalysisForHedgehogs. You can find the video here. It serves as an excellent primer on interpreting what the results mean.
Key Aspects to Analyze
1. Detection Patterns
– Always reanalyze the file if it hasn’t been scanned recently; detection capabilities can evolve over time. VirusTotal will indicate if the file has been previously assessed, which can provide useful context.
– Investigate the malware classifications provided by different vendors. For example, some may label a file as “not-a-virus.” This means it’s not inherently malicious but could be susceptible to misuse—a classification not consistently adopted across all vendors.
2. File Details
– Confirm that the file type aligns with its stated purpose. Misleading file types can indicate a problem.
– Take note of the first submission date. If this predates the actual release of the software, the file might simply be repurposed malware.
– Examine any alternate names assigned to the file. If they sound unrelated, it may signify that the malware has been renamed to obscure its true nature. However, names like update.exe or random strings can typically be overlooked.
3. Behavioral Analysis
– Scrutinize the actions the file undertakes—such as files it drops or modifies. If it’s venturing into unusual directories, that’s a red flag.
– Investigate any registry changes. For instance, a legitimate software update shouldn’t need to disable critical security features like Windows Defender.
– Pay attention to suspicious API calls; for example, calls to functions like GetTickCount can indicate a program’s attempts to evade detection on virtual machines. More information on this can be found here.
4. Community Feedback
– While the community feedback
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