Download 750k Txt !EXCLUSIVE!

Do not use MD5 to hash user passwords! Do not use SHA-x! Yeah, but why? I'll show you, donut worry. We'll talk about password cracking, what are the speeds, how passwords are cracked on GPUs, and of course how to defend against cracking. As an example, I'll use a recent data leak where 750k plaintext passwords have surfaced from a site that supposedly used MD5. We'll also explain what a salt is and what it's not, and that it's not there to prevent cracking, and that's fine. Eventually, I'll explain what slow hashes like bcrypt or Argon2 are and how to use them in PHP. Let's get cracking!

Download 750k txt

#10 You can test-drive cracking with precomputed tables in your browsers, thanks to CrackStation. For MD5 and SHA-1, they have 190 GB, 15-billion-entry lookup tables. Those also include all Wikipedia articles from 2010, and known breaches. You can also download a smaller 19 GB table.

#18 In July 2017, somebody posted a link to download 750k Mall.cz user accounts and passwords. Lupa.cz has discovered the dump actually contained 750k, 381 908 unique, plaintext passwords. But reportedly, Mall.cz has never stored plaintext passwords. They have used MD5 until 2012, then SHA-1 with a cryptographic salt, and since 2016 they use bcrypt, one of the recommended password hashing algorithms. The details are tracked in my project listing password storages and disclosures. Ok, that doesn't sound like plaintext. Unfortunately, Mall.cz didn't rehash existing passwords and have even stated that those dumped passwords are from the time when they used MD5.

#19 Almost immediately, you could read theories online, that Mall.cz stored plaintext passwords and that they're losers and so on. I think that passwords were indeed stored somehow hashed and somebody has cracked them and has dumped them on the Internet. This theory is supported by Mall.cz resetting roughly twice as many passwords (1.3 million) than what was in the dump (750k). To verify the theory (because I'm a fan of PoCGTFO), I've rented a Tesla. Oh, sorry, wrong picture.

#21 We can make up for the lower performance of Tesla K80 by renting a p2.16xlarge instance with 16 GPUs doing 73 billion MD5 hashes/sec in total. I wanted to try crack the passwords just like somebody who's dumped those 750k passwords. I took those plaintext passwords, hashed them again with MD5, followed a guideline to set up the instance, and with almost no preparation for the job (unlike a password cracking pro) I was able to crack almost all the password again in 12 hours (then I went to sleep and halted the machine).

#24 Let's see how you can generate some useful candidates, we'll start with the combinator attack. It combines words from two lists so you can generate candidates like firstnamelastname which can sometimes lead to quite a long passwords. First names and last names used in the Czech Republic can be downloaded from the site of the Ministry of the Interior of the Czech Republic. You can of course combine other lists, and if you'd like to combine three lists, check combinator3 from hashcat-utils.

When you click on a response via Results > Individual Responses, you'll see a link to the uploaded file(s) on the Data tab of each individual response. Click that link and you'll have access to the file (it will either display or download, depending on the file type).

The chunks can then be downloaded for example with wget or curl. The following command downloads all meta file format chunks with 8 requests (maximum) in parallel into the folder meta (which has to be created before):

The surface normal estimation benchmark consists of meshed CAD models with fixed amounts of vertices (512, 1024 and 2048) and ground truth vertex normals derived from the parametric boundary representation.There is one benchmark for patches (parts) of CAD models and one for full CAD models.The benchmarks come in different sizes (10k, 50k and 100k) and are split into training and testing data.To obtain the 100k benchmark, you need to download the 50k and 100k archives.

I have tried with several different GEO microarray datasets, and had the same problem. Just a couple of weeks ago I successfully downloaded the same datasets from GEO (including the expression data), using the same code.

The data is available on the Genomic Data Commons website for our paper ( -data/publications/pancan-driver). Please see the file described: "Mutation Scores and tool aggregation" (Mutation.CTAT.3D.Scores.txt). It contains scores for all missense mutations (750k mutations).

Microarray data is subject to noise and systematic variation that negatively affects the resolution of copy number analysis. We describe Rawcopy, an R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). Noise characteristics of a large number of reference samples are used to estimate log ratio and B-allele frequency for total and allele-specific copy number analysis. Rawcopy achieves better signal-to-noise ratio and higher proportion of validated alterations than commonly used free and proprietary alternatives. In addition, Rawcopy visualizes each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states. Software and instructions are available at

Rawcopy, described here, is a processing tool for Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 arrays. We demonstrate reduced systematic variation in log ratio and BAF compared to the currently most widely used alternatives, as well as improved prediction accuracy for copy number gain and loss.

Rawcopy is free software and may be redistributed and/or modified under the terms of the GNU General Public License as published by the Free Software Foundation; version 2. Installation, execution and access are described at The set of 947 cancer cell lines is available at GEO with accession number GSE36138. The set of 231 hepatocellular carcinomas is available at GEO with accession number GSE54504. The individual SNP 6.0 reference samples (BRCA, COAD, GBM and LUAD non-cancer samples were used as references) as well as samples used as examples can be obtained from TCGA upon request ( -data.nci.nih.gov/tcga/). Individual Swedish clinical CytoScan HD reference samples are not publically available. Individual HapMap CytoScan HD/750k reference samples can be obtained from Affymetrix Inc. upon request.

The authors acknowledge funding from Uppsala County Council, the Swedish Cancer Research Fund and Lions Cancer Research Fund Uppsala-Örebro. Ann-Charlotte Thuresson and the Department of immunology, genetics and pathology, Uppsala University, are acknowledged for making CytoScan HD reference samples available. Affymetrix provided additional HapMap CytoScan HD and CytoScan 750k reference samples. The Cancer Genome Atlas project is acknowledged for non-cancer SNP 6.0 reference samples. 041b061a72