Alexander Sevtsov
633 posts

Alexander Sevtsov
@alexsevtsov
Threat Research / Software Engineering 👨💻 at @cybereason, previously - @sentinelone, @vmware, @lastlineinc, @avira and @kaspersky
Berlin Katılım Kasım 2012
742 Takip Edilen475 Takipçiler

I’m speechless. Sergey is the one who brought me to the GREAT like 10 years ago. Goodbye friend, your legacy will live forever.
Boris Larin@oct0xor
Heartbroken to hear about the passing of @Skvern0. He was one of the best threat hunters in the industry - even APTs were afraid of him. I’m grateful for the time we worked together and for everything I learned from him. Rest in peace.
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Hypervisors for Memory Introspection and Reverse Engineering by @memn0ps
secret.club/2025/06/02/hyp…
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Awesome Threat Detection and Hunting : github.com/0x4D31/awesome… credits @0x4D31
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Alexander Sevtsov retweetledi
Alexander Sevtsov retweetledi

Hunting Cobalt Strike Servers. A comprehensive view on the techniques… | by Bank Security | Medium #amazing hunting bank-security.medium.com/hunting-cobalt…
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Alexander Sevtsov retweetledi

The fourth article on Virtualization Internals Series is out - Digging into QEMU ! #virtualization #hypervisors #qemu #kvm
docs.saferwall.com/blog/virtualiz…

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Alexander Sevtsov retweetledi

Zero to Hero: The process of reversing and exploiting complex vulnerabilities! twitter.com/i/broadcasts/1…
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@KevinNaughtonJr lack of flexibility for the sake of being right, and not giving others a chance to make mistakes and learn from them
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@KevinNaughtonJr tests reflect the structure of the code, it’s easier to figure out what the code does by reading / debugging unit-tests, rather than the business logic itself
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@KevinNaughtonJr why there are so many people who hate Java
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Alexander Sevtsov retweetledi
Alexander Sevtsov retweetledi

Spooky(🎃) ICEDID research is out with a configuration extractor and a C2 infrastructure validation utility. Great work by @bluish_red_ @DanielStepanic @dcode
elastic.co/security-labs/…
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These machine learning cheatsheets contain some of the best and well-organized ML content I've come across.
Sometimes, it's just good to understand the concept at a high level and it's context before going deep. This resource helps with that.
stanford.edu/~shervine/teac…

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