Compressed file data is generally a chain of DEFLATE non-compressed blocks the quoted local file headers followed by the compressed kernel. The output files are not all the same size: those that appear earlier in the zip file are larger than those that appear later, because they contain more quoted local file headers.
The contents of the output files are not particularly meaningful, but no one said they had to make sense. This quoted-overlap construction has better compatibility than the full-overlap construction of the previous section, but the compatibility comes at the expense of the compression ratio.
There, each added file cost only a central directory header; here, it costs a central directory header, a local file header, and another 5 bytes for the quoting header. Now that we have the basic zip bomb construction, we will try to make it as efficient as possible. We want to answer two questions:. It pays to compress the kernel as densely as possible, because every decompressed byte gets magnified by a factor of N.
All decent DEFLATE compressors will approach a compression ratio of when given an infinite stream of repeating bytes, but we care more about specific finite sizes than asymptotics. For our purposes, filenames are mostly dead weight. While filenames do contribute something to the output size by virtue of being part of quoted local file headers, a byte in a filename does not contribute nearly as much as a byte in the kernel.
We want filenames to be as short as possible, while keeping them all distinct, and subject to compatibility considerations.
The first compatibility consideration is character encoding. TXT Appendix D. But this is a major point of incompatibility across zip parsers, which may interpret filenames as being in some fixed or locale-specific encoding.
We are further restricted by filesystem naming limitations. Some filesystems are case-insensitive, so "a" and "A" do not count as distinct names. As a safe but not necessarily optimal compromise, our zip bomb will use filenames consisting of characters drawn from a character alphabet that does not rely on case distinctions or use special characters:. Filenames are generated in the obvious way, cycling each position through the possible characters and adding a position on overflow:.
There are 36 filenames of length 1, 36 2 filenames of length 2, and so on. Given that the N filenames in the zip file are generally not all of the same length, which way should we order them, shortest to longest or longest to shortest? A little reflection shows that it is better to put the longest names last, because those names are the most quoted. Ordering filenames longest last adds over MB of output to zblg.
It is a minor optimization, though, as those MB comprise only 0. The quoted-overlap construction allows us to place a compressed kernel of data, and then cheaply copy it many times.
For a given zip file size X , how much space should we devote to storing the kernel, and how much to making copies? To find the optimum balance, we only have to optimize the single variable N , the number of files in the zip file.
Every value of N requires a certain amount of overhead for central directory headers, local file headers, quoting block headers, and filenames. All the remaining space can be taken up by the kernel. Because N has to be an integer, and you can only fit so many files before the kernel size drops to zero, it suffices to test every possible value of N and select the one that yields the most output. It is not a coincidence. Let's look at a simplified model of the quoted-overlap construction.
In the simplified model, we ignore filenames, as well as the slight increase in output file size due to quoting local file headers. Analysis of the simplified model will show that the optimum split between kernel and file headers is approximately even, and that the output size grows quadratically when allocation is optimal.
Let H N be the amount of header overhead required by N files. Refer to the diagram to understand where this formula comes from. In this simplified model we ignore the minor additional expansion from quoted local file headers. Taking the derivative and finding the zero gives us N OPT , the optimal number of files.
H N OPT gives the optimal amount of space to allocate for file headers. From this we see that the output size grows quadratically in the input size. As we make the zip file larger, eventually we run into the limits of the zip format. It happens that the first limit we hit is the one on uncompressed file size. Accepting that we cannot increase N nor the size of the kernel without bound, we would like find the maximum compression ratio achievable while remaining within the limits of the zip format.
The way to proceed is to make the kernel as large as possible, and have the maximum number of files. Even though we can no longer maintain the roughly even split between kernel and file headers, each added file does increase the compression ratio—just not as fast as it would if we were able to keep growing the kernel, too. In fact, as we add files we will need to decrease the size of the kernel to make room for the maximum file size that gets slightly larger with each added file. Any major improvements to the compression ratio can only come from reducing the input size, not increasing the output size.
Among the metadata in the central directory header and local file header is a CRC checksum of the uncompressed file data. This poses a problem, because directly calculating the CRC of each file requires doing work proportional to the total unzipped size, which is large by design. It's a zip bomb, after all. We would prefer to do work that in the worst case is proportional to the zipped size. Two factors work in our advantage: all files share a common suffix the kernel , and the uncompressed kernel is a string of repeated bytes.
We will represent CRC as a matrix product—this will allow us not only to compute the checksum of the kernel quickly, but also to reuse computation across files. You can model CRC as a state machine that updates a bit state register for each incoming bit. The basic update operations for a 0 bit and a 1 bit are:. To see why, observe that multiplying a matrix by a vector is just summing the columns of the matrix, after multiplying each column by the corresponding element of the vector.
This representation is called homogeneous coordinates. The matrices M 0 and M 1 are shown. The benefit of a matrix representation is that matrices compose.
Suppose we want to represent the state change effected by processing the ASCII character 'a', whose binary representation is 2.
We can represent the cumulative CRC state change of those 8 bits in a single transformation matrix:. And we can represent the state change of a string of repeated 'a's by multiplying many copies of M a together—matrix exponentiation. For example, the matrix representing the state change of a string of 9 'a's is. The square-and-multiply algorithm is useful for computing M kernel , the matrix for the uncompressed kernel, because the kernel is a string of repeated bytes.
To produce a CRC checksum value from a matrix, multiply the matrix by the zero vector. The zero vector in homogeneous coordinates, that is: 32 0's followed by a 1. Here we omit the minor complication of pre- and post-conditioning the checksum. To compute the checksum for every file, we work backwards. Continue the procedure, accumulating state change matrices into M , until all the files have been processed. Earlier we hit a wall on expansion due to limits of the zip format—it was impossible to produce more than about TB of output, no matter how cleverly packed the zip file.
It is possible to surpass those limits using Zip64 , an extension to the zip format that increases the size of certain header fields to 64 bits. Support for Zip64 is by no means universal , but it is one of the more commonly implemented extensions. As regards the compression ratio, the effect of Zip64 is to increase the size of a central directory header from 46 bytes to 58 bytes, and the size of a local directory header from 30 bytes to 50 bytes.
Referring to the formula for optimal expansion in the simplified model, we see that a zip bomb in Zip64 format still grows quadratically, but more slowly because of the larger denominator—this is visible in the figure below in the Zip64 line's slightly lower vertical placement.
In exchange for the loss of compatibility and slower growth, we get the removal of all practical file size limits. Suppose we want a zip bomb that expands to 4. How big must the zip file be? Using binary search, we find that the smallest zip file whose unzipped size exceeds the unzipped size of With Zip64, it's no longer practically interesting to consider the maximum compression ratio, because we can just keep increasing the zip file size, and the compression ratio along with it, until even the compressed zip file is prohibitively large.
An interesting threshold, though, is 2 64 bytes 18 EB or 16 EiB —that much data will not fit on most filesystems. Binary search finds the smallest zip bomb that produces at least that much output: it contains 12 million files and has a compressed kernel of 1.
Ver 27 comentarios. En Xataka. Compartir El zip de la muerte: un "inocente" archivo comprimido capaz de explotar hasta colapsar tu PC con billones de datos Facebook Twitter Flipboard E-mail.
Temas Aplicaciones Antivirus malware. Compartir Facebook Twitter Flipboard E-mail. Comentarios cerrados. Los mejores comentarios:. Destacamos Premios Xataka Black Friday. Entretenimiento Sensacine Espinof. Decompression bombs have been around for a while. Instructions for Unzipper 1. Write a 1. Write a billion billion billion 0s 3.
Write another 1. Rinse and repeat. I cannot find any hard drives bigger than 5TB Terabyte. Sure big ass companies have way bigger custom hard drives, but still they are nowhere near the size of 4 PB. I love this video describing a petabyte in real terms. If you know the algorithm well enough that you can predict how it will zip a given file of XYZ sequence of bytes into a file of ABC bytes, then you should be able to reverse the process and hand code a zip file of ABC bytes that will hypothetically unzip to whatever XYZ sequence you want.
Bitdefender did not catch it either… It even went as far as stating that it cannot be scanned because it is password protected. Good thing they have a file shredder. Could someone explain to me why Windows starts choking when I extract one of the archives?
WinRAR finishes without any issue. Some Windows service start using an entire core of CPU and eat all available memory. I pasted in all the information it told me about it. Recommended action: Remove this software immediately.
BitDefender blocked MY access to Save my name, email, and website in this browser for the next time I comment. Please click on the following link to open the newsletter signup page: Ghacks Newsletter Sign up. Ghacks is a technology news blog that was founded in by Martin Brinkmann. It has since then become one of the most popular tech news sites on the Internet with five authors and regular contributions from freelance writers.
Search for:. Martin Brinkmann. Or, to use their own words: The file contains 16 zipped files, which again contains 16 zipped files, which again contains 16 zipped files, which again contains 16 zipped, which again contains 16 zipped files, which contain 1 file, with the size of 4. Related content Microsoft says Powerdir vulnerability in macOS could have given attackers access to user data. Avira is adding a crypto miner to its products as well. KeePass 2. LastPass: some users report compromised accounts.
Bitdefender Free will be retired on December 31, Your Browser's History Might Tell.
0コメント