Hacking on Medium
آموزش برنامه نویسی

آموزش برنامه نویسی کاتلین حوزه ای از دانش روانشناسی است که در آن دانش به دست آمده از مطالعات مربوط به فرایندهای تدریس و یادگیری سیستماتیک…

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Hacking on Medium
PICO CTF Wave a Flag [GENERAL SKILL]

https://cdn-images-1.medium.com/max/649/1*4Z2NnL7AIRQ0jXJcq1CRrw.png
This is a write up of below challenge :

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چگونه هکر شویم؟؟

فانتزی هکرهای سبک
این فیلم برنده جایزه آکادمی نیست. بنابراین فقط بر اساس ارزش سرگرمی امتیاز می گیرد یا نمره نمی گیرد.

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READY — HackTheBox WriteUp

https://cdn-images-1.medium.com/max/803/1*pAvummPuc06UkQNCE-DjIQ.png
This box is a part of TJnull’s list of boxes. I am doing these boxes as a part of my preparation for OSCP. I will be sharing the writeups…

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Hey guys,

what’s up I am here to learn about programming and how to become a ethical hacker and do bug bounty program. I need your help guys to get…

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Hacking on Medium
How to scan and remove malware from WordPress site?

https://cdn-images-1.medium.com/max/732/1*P2_joDHGQXzGz14Oi3Ikug.png
Have you taken these measures to safeguard your WordPress website from hackers? Want to recover a hacker website?

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The $600 Million Poly Network Hack and What It Means

https://cdn-images-1.medium.com/max/885/0*2cK4Gne1fO_JdxNF
An ignominious record was set yesterday as the DeFi platform Poly Network was hacked and drained of over $600 million. The Poly Network is…

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Deep Web
How do I know who’s a scammer?

submitted by /u/MichaelKyrg
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Deep Web
hello guys i am new here

i am just wondering if there are any crypto faucets on the deepweb ??

submitted by /u/yungfellaa
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Jarm - Active Transport Layer Security (TLS) server fingerprinting tool
http://www.kitploit.com/2021/08/jarm-active-transport-layer-security.html

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How JARM Works
Before learning how JARM works, it’s important to understand how TLS works. TLS and its predecessor, SSL, are used to encrypt communication for both common applications like Internet browsers, to keep your data secure, and malware, so it can hide in the noise. To initiate a TLS session, a client will send a TLS Client Hello message following the TCP 3-way handshake. This packet and the way in which it is generated is dependent on packages and methods used when building the client application. The server, if accepting TLS connections, will respond with a TLS Server Hello packet. TLS servers formulate their Server Hello packet based on the details received in the TLS Client Hello packet. The manner in which the Server Hello is formulated for any given Client Hello can vary based on how the application or server was built, including: Operating system Operating system version Libraries used Versions of those libraries used The order in which the libraries were called Custom configuration All of these factors lead to each TLS Server responding in a unique way. The combinations of factors make it unlikely that servers deployed by different organizations will have the same response. JARM works by actively sending 10 TLS Client Hello packets to a target TLS server and capturing specific attributes of the TLS Server Hello responses. The aggregated TLS server responses are then hashed in a specific way to produce the JARM fingerprint. This is not the first time we’ve worked with TLS fingerprinting. In 2017 we developed JA3/S (https://github.com/salesforce/ja3), a passive TLS client/server fingerprinting method now found on most network security tools. But where JA3/S is passive, fingerprinting clients and servers by listening to network traffic, JARM is an active server fingerprinting scanner. You can find out more about TLS negotiation and JA3/S passive fingerprinting here (https://engineering.salesforce.com/tls-fingerprinting-with-ja3-and-ja3s-247362855967). The 10 TLS Client Hello packets in JARM have been specially crafted to pull out unique responses in TLS servers. JARM sends different TLS versions, ciphers, and extensions in varying orders to gather unique responses. Does the server support TLS 1.3? Will it negotiate TLS 1.3 with 1.2 ciphers? If we order ciphers from weakest to strongest, which cipher will it pick? These are the types of unusual questions JARM is essentially asking the server to draw out the most unique responses. The 10 responses are then hashed to produce the JARM fingerprint. The JARM fingerprint (https://www.kitploit.com/search/label/Fingerprint) hash is a hybrid fuzzy hash, it uses the combination of a reversible and non-reversible hash algorithm to produce a 62 character fingerprint. The first 30 characters are made up of the cipher and TLS version chosen by the server for each of the 10 client hello's sent. A "000" denotes that the server refused to negotiate with that client hello. The remaining 32 characters are a truncated SHA256 hash of the cumulative extensions sent by the server, ignoring x509 certificate data. When comparing JARM fingerprints, if the first 30 characters are the same but the last 32 are different, this would mean that the servers have very similar configurations, accepting the same versions and ciphers, though not exactly the same given the extensions are different. After receiving each TLS server hello message, JARM closes the connection gracefully with a FIN as to not leave the sockets open. It is important to note that JARM is a high-performance fingerprint function and should not be considered, or confused (https://www.kitploit.com/search/label/Confused) with, a secure crypto function. We designed the JARM fingerprint to be human consumable as much as machine consumable. This means it is small enough to eyeball, share, and tweet with enough room for contextual details.
How JARM Can Be Used to Identify Malicious Servers

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Malware command and control (C2) and malicious servers are configured by their creators like any other server and then deployed across their fleet. These therefore tend to produce unique JARM fingerprints. Below are examples of common malware and offensive tools and the JARM overlap with the Alexa Top 1M websites (as of Oct. 2020): Malicious Server C2 JARM Fingerprint Overlap with Alexa Top 1M Trickbot 22b22b09b22b22b22b22b22b22b22b352842cd5d6b0278445702035e06875c 0 AsyncRAT 1dd40d40d00040d1dc1dd40d1dd40d3df2d6a0c2caaa0dc59908f0d3602943 0 Metasploit 07d14d16d21d21d00042d43d000000aa99ce74e2c6d013c745aa52b5cc042d 0 Cobalt Strike 07d14d16d21d21d07c42d41d00041d24a458a375eef0c576d23a7bab9a9fb1 0 Merlin C2 29d21b20d29d29d21c41d21b21b41d494e0df9532e75299f15ba73156cee38 303 With little to no overlap of the Alexa Top 1M Websites, it should be unlikely for a host within an organization to connect to a server with these JARM fingerprints.
JARM Team
John Althouse (https://www.linkedin.com/in/johnalthouse/) - Original idea, concept and project lead
Andrew Smart (https://www.linkedin.com/in/andrew-smart-a3b15a2/) - Concept and testing
RJ Nunnally (https://www.linkedin.com/in/rjnunnally/) - Programing and testing
Mike Brady (https://www.linkedin.com/in/mike-brady-b5293b21/) - Programing and testing Rewritten in Python for operational use by Caleb Yu (https://www.linkedin.com/in/caleb-yu/)

Download Jarm (https://github.com/salesforce/jarm)

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hacking: security in practice
An Old Hacker's Tips On Staying Employed

https://external-preview.redd.it/js0YRz2o9ifJXHlGHf1d7_o9ZC6WFL8raFR2nMxJpJI.jpg?width=640&crop=smart&auto=webp&s=b1e32c89ee7e3ad7efc7d7b9c30e8cc3a4c671af submitted by /u/mad_ned
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