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Who is a hacker?


Hey guys, in this blog lets talk some interesting stuffs about hacking and hackers.

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Hacking on Medium
How i hacked world wide Tiktok users


In this write up I am sharing a TikTok vulnerability reported via TikTok’s bug bounty program

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Powershell brute force attack via rdp
https://www.reddit.com/r/Pentesting/comments/po2xx9/powershell_brute_force_attack_via_rdp/

So I've been tasked with staging a brute force attack via rdp in powershell with no dependencies on external files. Essentially just running through a list of login names and random passwords. This is to test the functionality and response of our current SentinelOne implementation and verify it works as expected. Any ideas on this before I start going down this rabbit hole? submitted by /u/user257 (https://www.reddit.com/user/user257)
[link] (https://www.reddit.com/r/Pentesting/comments/po2xx9/powershell_brute_force_attack_via_rdp/) [comments] (https://www.reddit.com/r/Pentesting/comments/po2xx9/powershell_brute_force_attack_via_rdp/)

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Deep Web
The Difference Between These 2 Subreddits

I was wondering whats the difference between this subreddit (r/deepweb) and the subreddit r/onions ?

submitted by /u/TheToxicNinja20
[link] [comments]

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KitPloit - PenTest Tools!targetedKerberoast - Kerberoast With ACL Abuse Capabilities



targetedKerberoast is a Python script that can, like many others (e.g. GetUserSPNs.py), print "kerberoast" hashes for user accounts that have a SPN set. This tool brings the following additional feature: for each user without SPNs, it tries to set one (abuse of a write permission on the servicePrincipalNameattribute), print the "kerberoast" hash, and delete the temporary SPN set for that operation. This is called targeted Kerberoasting. This tool can be used against all users of a domain, or supplied in a list, or one user supplied in the CLI.

More information about this attack

* The Hacker Recipes - Kerberoast
* The Hacker Recipes - Targeted Kerberoasting



Usage

This tool supports the following authentications

* (NTLM) Cleartext password
* (NTLM) Pass-the-hash
* (Kerberos) Cleartext password
* (Kerberos) Pass-the-key / Overpass-the-hash
* (Kerberos) Pass-the-cache (type of Pass-the-ticket)

Among other things, targetedKerberoast supports multi-level verbosity, just append -v, -vv, ... to the command :)

Kerberos authentication. Grabs credentials from .ccache file (KRB5CCNAME) based on target parameters. If valid credentials cannot be found, it will use the ones specified in the command line --no-pass don't ask for password (useful for -k) -p PASSWORD, --password PASSWORD password to authenticate with -H [LMHASH:]NTHASH, --hashes [LMHASH:]NTHASH NT/LM hashes, format is LMhash:NThash --aes-key hex key AES key to use for Kerberos Authentication (128 or 256 bits) ">usage: targetedKerberoast.py [-h] [-v] [-q] [-D TARGET_DOMAIN] [-U USERS_FILE] [--request-user username] [-o OUTPUT_FILE] [--use-ldaps] [--only-abuse] [--no-abuse] [--dc-ip ip address] [-d DOMAIN] [-u USER]
[-k] [--no-pass | -p PASSWORD | -H [LMHASH:]NTHASH | --aes-key hex key]

Queries target domain for SPNs that are running under a user account and operate targeted Kerberoasting

optional arguments:
-h, --help show this help message and exit
-v, --verbose verbosity level (-v for verbose, -vv for debug)
-q, --quiet show no information at all
-D TARGET_DOMAIN, --target-domain TARGET_DOMAIN
Domain to query/request if different than the domain of the user. Allows for Kerberoasting across trusts.
-U USERS_FILE, --users-file USERS_FILE
File with user per line to test
--request-user username
Requests TGS for the SPN associated to the user specified (just the username, no domain needed)
-o OUTPUT_FILE, --output-file OUTPUT_FILE
Output filename to write ciphers in JtR/hashcat format
--use-ldaps Use LDAPS instead of LDAP
--only-abuse Ignore accounts that already have an SPN and focus on targeted Kerberoasting
--no-abuse Don't attempt targeted Kerberoasting

authentication & connection:
--dc-ip ip address IP Address of the domain controller or KDC (Key Distribution Center) for Kerberos. If omitted it will use the domain part (FQDN) specified in the identity parameter
-d DOMAIN, --domain DOMAIN
(FQDN) domain to authenticate to
-u USER, --user USER user to authenticate with

secrets:
-k, --kerberos Use Kerberos authentication. Grabs credentials from .ccache file (KRB5CCNAME) based on target parameter s. If valid credentials cannot be found, it will use the ones specified in the
command line
--no-pass don't ask for password (useful for -k)
-p PASSWORD, --password PASSWORD
password to authenticate with
-H [LMHASH:]NTHASH, --hashes [LMHASH:]NTHASH
NT/LM hashes, format is LMhash:NThash
--aes-key hex key AES key to use for Kerberos Authentication (128 or 256 bits)


Below is an example what the tool can do.



Credits and references

Credits to the [...]

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whole team behind Impacket and its contributors.



Download targetedKerberoast

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Vulnerability assessments vs penetration testing vs red teaming in your organization?
https://www.reddit.com/r/redteamsec/comments/po4huv/vulnerability_assessments_vs_penetration_testing/

Do you have similar or separate team for these roles? While they may have similar components, each one is different and should be used in different contexts. If similar, there might be more work for them as they need to cover 3 areas, depending on the size of your organization? If separate, what is the challenge since the objective is slightly different? submitted by /u/w0lfcat (https://www.reddit.com/user/w0lfcat)
[link] (https://www.reddit.com/r/redteamsec/comments/po4huv/vulnerability_assessments_vs_penetration_testing/) [comments] (https://www.reddit.com/r/redteamsec/comments/po4huv/vulnerability_assessments_vs_penetration_testing/)

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Bypass Server Upload Restrictions
https://infosecwriteups.com/bypass-server-upload-restrictions-69054c5e1be4?source=rss------bug_bounty-5

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How to Get a Shell on a Website using a File [Tutorial]Continue reading on InfoSec Write-ups » (https://infosecwriteups.com/bypass-server-upload-restrictions-69054c5e1be4?source=rss------bug_bounty-5)

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How I Was Able To Send SMS From Google To Anyone | $$$ Google Vulnerability:
https://asterfiester.medium.com/how-i-was-able-to-send-sms-from-google-to-anyone-google-vulnerability-3277ea0cc9d1?source=rss------bug_bounty-5

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An interesting write up about Google Vulnerability:Continue reading on Medium » (https://asterfiester.medium.com/how-i-was-able-to-send-sms-from-google-to-anyone-google-vulnerability-3277ea0cc9d1?source=rss------bug_bounty-5)

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Kali Linux TutorialsPantagrule : Large Hashcat Rulesets Generated From Real-World Compromised Passwords
Pantagrule is a series of rules for the hashcat password cracker generated from large amounts of real-world password compromise data. While Pantagrule rule files can be large, the rules are both tunable and perform better than many existing rule sets.

Pantagrule was generated using PACK’s Levenshtein Reverse Path algorithm for automated rule generation (Kacherginsky, 2013). PACK’s output was then sorted based upon the number of times PACK generated the rule to make the base ruleset. This process is similar to the rules generated by _NSAKEY for password cracking competitions in 2014 (_NSAKEY, 2014), however, Pantagrule was generated off a significantly larger set of passwords. Version 2 of Pantagrule was developed off of the publicly-available hashes.org “founds” corpus, a best-in-class public wordlist. This yields more transparent results than the original variant, which used a proprietary corpus containing 842,643,513 unique passwords.

When such large rulesets are fed through PACK, millions of rules result. However, since most of the rules generated appear only a handful of times, most of the useful rules are the ones that are most commonly generated by the algorithm. This repository contains a subset of rules generated by PACK whilst iterating through the existing corpus.

Optimised Variants

In order to generate a second-pass optimisation of the rules against real-world data, the top one million generated rules was run against the Pwned Passwords NTLM list using the rockyou wordlist. Any rule that cracked a password was added to its own list and poorer-performing rules were discarded.

Four optimisation types were created:

* popular.rule: pantagrule.1m run against the top 25,000,000 passwords of the HIBP set.
* random.rule: pantagrule.1m run against 25,000,000 randomly selected passwords from the HIBP set.
* hybrid.rule: A sorted list of a combination of the most successful popular and random rules, then cut in half, in an attempt to make a lighter, “balanced” ruleset that works across a larger sample set.
* one.rule: A version of OneRuleToRuleThemAll in which the top performing hybrid rules are appended, and the list is truncated to the size of the dive rule set. Interestingly, there is only a couple-thousand-rule overlap with OneRuleToRuleThemAll and the Pantagrule rules, making the two strategies complementary. Pantagrule’s one performs better than other known lists of this size, and it is recommended that you start with this ruleset before attempting one of the larger variants.

Pantagrule hashorg.v6

After the success of these large rulesets, an attempt was made of the inverse of the royce variant, in which the original Pantagrule methodology was used but both sets of data were different. Pantagrule now uses the public hashes.org “founds” list as its wordlist base for rule generation, and an optimisation pass was then made against the V6 NTLM list from Have I Been Pwned. Given the fully-public nature of the data used, it also allows the publication of raw reproducibility data, including pantagrule.v2.1m.rule, which are the top one million rules generated by this methodology. The data for V5 and V6 is the same for the top 25 million passwords.

For this version, the way one is generated has changed. To generate one, the full 1 million list was appended to OneRuleToRuleThemAll.rule and then the entire set calibrated on Pwned V6, vs. just appending rules and truncating.

Naming conventions for the rules have now ch[...]

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anged to be of the format pantagrule.${corpus}.${trainingversion}.${extension}. This makes it easier to understand what the rule was optimised for. For example, for pantagrule.hashorg.v6.random, We used the random methodology with hashes.org as the basis for the rule generation, optimised on Pwned Passwords V6.

Original rules

Original rules were trained using the proprietary wordlist alongside the Pwned Passwords NTLM v5 set using rockyou.txt as a base. Since the “training data” and the validation data are the same, it would make sense to see them optimised for the V5 dataset.

The royce variants

Upon request of hashcat contributor Royce Williams, optimisations of the top one million rules were also run with the hashes.org founds list. This is due to the HIBP corpus being relatively dirty, and the hashes.org founds list being likely to yield a more practical ruleset for real-world cracking. These have been added as the royce variants. The royce optimisations appear to consist of marginally fewer rules overall, and random.royce is substantially more effective on a long tail of passwords than the original random. Performance did not increase over the existing rules on some variants, but given that the training and validation data of the original Pantagrule are both from the Pwned Passwords dataset, this does not seem surprising. Pantagrule royce variants exist in the rules/royce folder.

Performance vs. other commonly-used rules

In order to test any successes of the Pantagrule strategy against other rulesets, we will run validation data across the top 25 million passwords of Pwned Passwords V5 and the top 100 million passwords of Pwned Passwords V5 to get an understanding of rule effectiveness at cracking the “long tail” with each ruleset. The canonical rockyou.txt will be our dictionary and our baseline.

Original variant generation was done on an 8x 1070Ti rig running hashcat v5.1.0. The royce Pantagrule variants were created on a 4x Radeon VII rig running hashcat git build v5.1.0-1774-gf96594ef. The hashorg.v6 variants were created and validated (very slowly) on a single NVIDIA Tesla M4, a single 1070Ti, and hashcat v6.1.0.

In order to note rule performance against very common passwords, 0-25M is broken out into its own column. The RPP column is the rules per percent on the 100M dataset. This is calculated by using the formula rpp = Math.round(num_rules / (0_100m_percent - 6.450)). The higher this number, the more rules are run per percentage cracked. This helps realise the diminishing returns in rulesets and gives an idea of the amplified cost of running the rules on slower hashes.
RulesNumber of RulesV5 25MV5 100MRPPNo Rules (just rockyou.txt)016.549%6.450%N/Apantagrule.private.v5.one99,09279.814%69.417%1,574pantagrule.private.v5.hybrid355,20581.346%73.372%5,308pantagrule.private.v5.popular478,73681.792%73.544%7,135pantagrule.private.v5.random616,23681.687%69.805%8,828pantagrule.hashorg.v6.one99,09274.500%60.573%1,831pantagrule.hashorg.v6.hybrid339,95377.649%68.341%5,493pantagrule.hashorg.v6.popular514,41680.668%72.377%6,931pantagrule.hashorg.v6.random638,77380.603%72.713%8,614pantagrule.private.hashorg.one.royce99,09279.618%69.092%1,582pantagrule.private.hashorg.hybrid.royce314,26881.068%73.082%4,716pantagrule.private.hashorg.popular.royce420,98481.386%73.102%6,316pantagrule.private.hashorg.random.royce592,23581.659%74.010%8,766best646445.117%24.985%3hob0646837.786%19.773%5OneRuleToRuleThemAll52,01478.058%64.541%895d3adhob057,54851.274%34.800%2,030dive99,09277.111%63.314[...]

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%1,743_NSAKEY V1123,28976.42%64.121%2,138_NSAKEY V2123,28976.882%64.472%2,124
Conclusion

This work confirms the limitations of the PACK LRP algorithm originally witnessed by _NSAKEY on modern data sets when using the rockyou dictionary. While the LRP algorithm does generate rules that increase cracking percentage, it does so at a large increase in search space. For this reason, Pantagrule is most useful in cases where difficult cracking requires exotic rules.

It is important to note that if you can use PACK to generate rules based off of a specific corpus and then target your remaining hashes with it, you are likely to yield a greater cracking percentage than using one of these large rulesets. For example, Pantagrule V2 does not perform as well on PPv5 as the v5-calibrated ruleset.

Since the original Pantagrule release, these rules have proven themselves on multiple red team engagements at large technology companies and consultancies alike. The original pantagrule.1m list cracked 8% of the remaining HIBP hashes that had stood up to the corpus used to generate Pantagrule, the above common rule sets, a 7-character alphanumeric brute force, and KoreLogic’s PathWell topologies.

As even the author of the One Rule to Rule Them All (Hunt, 2017) meta-rule states, there is no such thing as a rule that works better than others. Every use case is different, and every rule source may be one that helps you more than another on a specific hash dump or with a specific wordlist. Note that this data does not show what has been cracked; some rules have cracked hashes that other rules have not.


Download

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Bypass Server Upload Restrictions

How to Get a Shell on a Website using a File TutorialContinue reading on InfoSec Write-ups »
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Why is Bug bounty trending?

Bug bounty, a big name among cybersecurity analysts and tech giants, has gained tremendous fame over time. This article will help you know…Continue reading on Medium »
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This is why you shouldn’t trust your Federated Identity Provider

Last year, while taking my daily dose of HackerOne’s Hacktivity, I stumbled upon this amazing writeup of cache-money where he demonstrated…Continue reading on Medium »
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