At first, go to download and install Tune4mac Spotify Audio Converter, run it. Drag Songs or Playlist from Spotify to Tune4mac Spotify Audio Converter. Click add button or click center of Tune4mac Spotify Audio Converter, then you will see a pop-up window which indicates you to drag and drop the songs or playlist here. It was created as a way to hack into Spotify's program and exploit weaknesses in Spotify's software, as a way to get around the fact that you cannot download music off of Spotify. Downloadify allows you to download your music directly from Spotify, this means that the files keep their original quality. With our intuitive user interface, you can easily download and convert any Spotify music or playlist in just four steps while retainning 100% original quality and ID3 tags after conversion. Spotify premium download pc. Now, you can enjoy the Spotify music on all of your device offline, such as car players, iPod, iPhone, Zune, PSP and MP3 players. Capo app spotify subscription. With Spotify Premium, you can download 10,000 songs for offline listening on five different devices. Thatâs 50,000 total tracks across everything. Strangely, thereâs no way to download individual songs; you have to download either albums or playlists. Open Spotify and head to the album or playlist you want to save for offline listening. Sonos app update spotify.
Spotify is a popular music streaming service, Over 30 million Spotify songs comes with DRM protection. Spotify Free users only allowed to listen to the songs online, although Spotify subscribers can download Spotify playlist for offline listening, all downloaded Spotify songs are DRM protected and you just allowed enjoying within Spotify App. Spotify premium free for 6 months att code. Worse, all the downloaded songs becomes unavailable.
Disclaimer: Although I think DRM is both stupid and evil, I don't advocate pirating music. Therefore, this post will stop short of providing a turnkey solution for ripping Spotify music, but it will fully describe the theory behind the technique and its implementation in PANDA. Don't be evil.Update 6/6/2014: The following post assumes you know what PANDA is (a platform for dynamic analysis based on QEMU). If you want to know more, check out my introductory post on PANDA. This past weekend I spoke at REcon, a conference on reverse engineering held every year in Montreal. I had a fantastic time there getting to meet other people interested in problems of memory analysis, reverse engineering, and dynamic analysis. One of the topics of my REcon talk was how to use PANDA to break Spotify DRM, and since the video from the talk won't be posted for a while, I thought I'd write up a post showing how we can use PANDA and statistics to pull out unencrypted OGGs from Spotify. Gathering DataThe first step is to gather some data. We want to know what function inside Spotify is doing the actual decryption of the songs, so that we can then hook it and pull out the decrypted (but not decompressed) audio file. So to start with, we'll take a recording of Spotify playing a song; we can then apply whatever analysis we want to the replay. Working with a replay rather than a live system will also make our job considerably easier â no need to worry that we're going to slow things down enough to trip anti-debugging measures or network timeouts. I've prepared a record/repay log of Spotify playing 30 seconds of a song, which you can use to follow along with what comes next. The recording is 12 billion instructions, which gives us a lot of data to work with! Just for fun, here's a movie of that replay, generated by taking screenshots throughout the replay and then stitching them into a video: Some TheoryNow the same file, decrypted: You can clearly see that the one on the bottom looks significantly less 'random' â or more precisely, the distribution of bytes is not very uniform. However, if we compute the byte entropy of each, they are both very close to the theoretical maximum of 8 bits per byte â the mp3 has 7.968480 bits of entropy per byte, whereas the encrypted file has 7.999981 bits per byte. We can make this intuition more precise by turning to statistics. The Pearson chi-squared test (Ï2) lets us compute a value for how much an observed distribution deviates from some ideal distribution. In this case, we expect the bytes in an encrypted file to be uniformly random, so we can compare with the uniform distribution by computing: Free File Decrypter DownloadHere, Oi is the observed frequency of each byte, and Ei is the expected frequency, which for a uniform byte distribution with n samples will be (1/256)*n. Similarly, the entropy of some ovserved data can be computed as: Based on the work of Wang et al., if we find a function that reads a lot of high-entropy, highly random data, and writes a lot of high-entropy, non-random data, that's likely to be our guy! Enter the PANDABut enough theory. How do we actually gather the data we need in PANDA? We will want some way of gathering, for each function, statistics on the contents of buffers read and written by each function in the replay. As it happens, PANDA has a plugin called unigramsthat will get us the data we want. Theunigramsplugin works by tracking every memory read and write made by the system. When it sees a read or write, it looks up the current process context (i.e., CR3 on x86), program counter, and the callsite of the parent function (this last is done with the help of thecallstack_instrplugin). Together, these three pieces of information allow us to put the individual memory access in context and separate out memory accesses made in different program contexts into coherent streams of data. So to gather the raw data we want, we can just run: x86_64-softmmu/qemu-system-x86_64 -m 1024 -replay spotify -panda-plugin x86_64-softmmu/panda_plugins/panda_callstack_instr.so -panda-plugin x86_64-softmmu/panda_plugins/panda_unigrams.so ![]() Armed with this data, we want to now go through each callsite and look for those that meet the following criteria:
./find_drm.py unigram_mem_read_report.bin unigram_mem_write_report.bin
Among its output, we find the following promising candidate:This function read two buffers of size 701,761 bytes and wrote one of size 701,761 bytes â given that we played 30 seconds of the song, this looks just about right. The randomness of the input buffers was quite high (recall that in the Ï2 test, high numbers mean the data observed is less likely to be random), but the output buffer was not very random. Dumping the DataSo how can we confirm our guess? Well, the easiest thing is to simply dump out the data seen at that point. If we go back up to the beginning of the output of the script, we have a list of all the (callsite, program counter, CR3) identifiers for reads and writes that matched our criteria. Looking through the writes for our candidate callsite (00719b84), we find it here: We can now use another PANDA plugin, tapdump, to dump out all the data flowing through that point in the program. First we create a text file named tap_points.txt in the QEMU directory, and put in it: Next we run the replay again with the tapdump plugin enabled. x86_64-softmmu/qemu-system-x86_64 -m 1024 -replay spotify -panda-plugin x86_64-softmmu/panda_plugins/panda_callstack_instr.so -panda-plugin x86_64-softmmu/panda_plugins/panda_tapdump.so 0000000082678e78 [Caller 13] 000000008260dcc3 [Caller 12] [..] 000000000071a1a5 [Caller 2] 0000000000719b84 [Caller 1] 000000000042e2ed [PC] 000000003f1ac2e0 [Address space] 000000000b256570 [Write address] Download Dvd Decrypter Free269882976 [Index]4f [Data]
The extra callstack information is included so that, if necessary, more calling context can be used to pull out just the data we're interested in. In our case, however, just one level turns out to be enough. Finally, we want to turn this text file into a binary stream. In the scripts directory, there is a script called split_taps.py which will go through a gzipped tapdump output file and separate out each distinct stream found in the file (based on our usual identifier of (callsite, program counter, CR3)).
So now we can run this on the writes seen at our candidate for the decryption function:
./split_taps.py write_tap_buffers.txt.gz spotify
And obtain spotify.0000000000719b84.000000000042e2ed.000000003f1ac2e0.dat, which contains the binary data written at program counter 0x0042e2ed, called from callsite 0x00719b84, inside of the process with CR3 0x3f1ac2e0. So, is this audio we seek?
$ file spotify.0000000000719b84.000000000042e2ed.000000003f1ac2e0.dat
spotify.0000000000719b84.000000000042e2ed.000000003f1ac2e0.dat: Ogg data
This looks good! Of course, the proof of the pudding is in the eating, and the proof of the audio is in the listening, so do.. $ cvlc spotify.0000000000719b84.000000000042e2ed.000000003f1ac2e0.dat And you should hear a rather familiar tune :) Concluding ThoughtsAs I mentioned in the disclaimer, this by itself is just the starting point for what you would need to really break Spotify's DRM. It doesn't give you a way to obtain the key for each song and decrypt it wholesale. Instead, you would have to place a hook in the function identified by this process and pull it out as it's played, which limits it to realtime decryption (and Spotify's packing and anti-debugging may make it hard to place the hook in the first place!). Although I can certainly imagine more efficient processes, I think for now this is a nice balance between enabling piracy and showing off the power of PANDA. Spotify Download Decrypter SoftwareIf you now want to get a better understanding of the function we found inside Spotify, you can create a memory dump, extract the unpacked Spotify binary (which is packed with Themida) using Volatility, and the load it up in IDA and go to 0x0042e2ed, which is the location where decrypted data is written out. PostscriptOne may wonder what happens when the function that contains 0x0042e2ed is called by others. As it turns out, this appears to be a generic decryption function that is used for other media throughout Spotify, including album art! It is left as an exercise to the reader to dump and examine the rest of the data that this function decrypts. ReferencesSpotify Download Mac[1] Steal This Movie: Automatically Bypassing DRM Protection in Streaming Media Services. Wang, R., Shoshitaishvili, Y., Kruegel, C., and Vigna, G. USENIX Security Symposium, Washington, D.C., 2013.Spotify Download For PcComments are closed.
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