Books / Crypto 101 / Chapter 6

Hash functions

Hash functions are functions that take an input of indeterminate length and produce a fixed-length value, also known as a “digest”.

Simple hash functions have many applications. Hash tables, a common data structure, rely on them. These simple hash functions really only guarantee one thing: for two identical inputs, they’ll produce an identical output. Importantly, there’s no guarantee that two identical outputs imply that the inputs were the same. That would be impossible: there’s only a finite amount of digests, since they’re fixed size, but there’s an infinite amount of inputs. A good hash function is also quick to compute.

Since this is a book on cryptography, we’re particularly interested in cryptographic hash functions. Cryptographic hash functions can be used to build secure (symmetric) message authentication algorithms, (asymmetric) signature algorithms, and various other tools such as random number generators. We’ll see some of these systems in detail in future chapters.

Cryptographic hash functions have much stronger properties than regular hash functions, such as one that you might find in a hash table. For a cryptographic hash function, we want it to be impossibly hard to:

  1. modify a message without changing the hash.
  2. generate a message that has a given hash.
  3. find two different messages with the same hash.

The first property implies that cryptographic hash functions will exhibit something known as the “avalanche effect”. Changing even a single bit in the input will produce an avalanche of changes through the entire digest: each bit of the digest will have approximately 50% chance of flipping. That doesn’t mean that every change will cause approximately half of the bits to flip, but the cryptographic hash function does guarantee that the odds of that happening are extremely large. More importantly it is impossibly hard to find such collisions or near-collisions.

The second property, which states that it should be difficult to find a message \(m\) that has a given hash value \(h\), is called pre-image resistance. This makes a hash function a one-way function: it’s very easy to compute a hash for a given message, but it’s very hard to compute a message for a given hash.

The third property talks about finding messages with the same hash value, comes in two flavors. In the first one, there’s a given message \(m\), and it should be difficult to find another message \(m^{\prime}\) with the same hash value: that’s called second pre-image resistance. The second one is stronger, stating that it should be hard to find any two messages \(m, m^{\prime}\) that have the same hash value. This is called collision resistance. Because collision resistance is a stronger form of second pre-image resistance, they’re sometimes also called weak and strong collision resistance.

These concepts are often named from the point of view of an attack, rather than the resistance to an attack. For example, you’ll often hear about a collision attack, which is an attack that attempts to generate a hash collision, or a second pre-image attack, which attempts to find a second pre-image that hashes to the same value as a given pre-image, et cetera.

See this for further reading.


MD5 is a hash function designed by Ronald Rivest in 1991 as an extension of MD4. This hash function outputs 128-bit digests. Over the course of the years, the cryptographic community has repeatedly uncovered MD5’s weaknesses. In 1993, Bert den Boer and Antoon Bosselaers published a paper demonstrating “pseudo-collisions” for the compression function of MD5. Dobbertin expanded upon this research and was able to produce collisions for the compression function. In 2004, based on Dobbertin’s work, Xiaoyun Wang, Dengguo Feng, Xuejia Lai and Hongbo Yu showed that MD5 is vulnerable to real collision attacks. The last straw came when Xiaoyun Wang et al. managed to generate colliding X.509 certificates and then presented a distinguishing attack on HMAC-MD5.

Nowadays, it is not recommended to use MD5 for generating digital signatures, but it is important to note that HMAC-MD5 is still a secure form of message authentication; however, it probably shouldn’t be implemented in new cryptosystems.

Five steps are required to compute an MD5 message digest:

  1. Add padding. First, 1 bit is appended to the message and then 0 bits are added to the end until the length is \(448 \pmod {512}\).
  2. Fill up the remaining 64 bits with the the length of the original message modulo \(2^{64}\), so that the entire message is a multiple of 512 bits.
  3. Initialize the state as four 32-bit words, A, B, C and D. These are initialized with constants defined in the spec.
  4. Process the input in 512 bit blocks; for each block, run four “rounds” consisting of 16 similar operations each. The operations all consist of shifts, modular addition, and a specific nonlinear function, different for each round.

Once done, \(A \| B \| C \| D\) is the output of the hash. This padding style combined with the concatenation at the end is what makes MD5 vulnerable to length extension attacks; more on that later.

In Python one can use the hashlib module to create an MD5 digest as follows:

import hashlib


SHA-1 is another hash function from the MD4 family designed by the NSA, which produces a 160-bit digest. Just like MD5, SHA-1 is no longer considered secure for digital signatures. Many software companies and browsers, including Google Chrome, have started to retire support of the signature algorithm of SHA-1. On February 23, 2017 researchers from CWI Amsterdam and Google managed to produce a collision on the full SHA-1 function. In the past methods to cause collisions on reduced versions of SHA-1 have been published, including one by Xiaoyun Wang. “The SHAppening” demonstrated freestart collisions for SHA-1. A freestart collision allows one to pick the initial value known as the initialization vector{.interpreted-text role=”term”} at the start of the compression function.

Once again the hashlib Python module can be used to generate a SHA-1 hash:

import hashlib


SHA-2 is a family of hash functions including SHA-224, SHA-256, SHA-384, SHA-512, SHA-512/224 and SHA-512/256 and their digest sizes 224, 256, 384, 512, 224 and 256 respectively. These hash functions are based on the Merkle–Damgård construction and can be used for digital signatures, message authentication and random number generators. SHA-2 not only performs better than SHA-1, it also provides better security, because of its increase in collision resistance.

SHA-224 and SHA-256 were designed for 32-bit processor registers, while SHA-384 and SHA-512 for 64-bit registers. The 32-bit register variants will therefore run faster on a 32-bit CPU and the 64-bit variants will perform better on a 64-bit CPU. SHA-512/224 and SHA-512/256 are truncated versions of SHA-512 allowing use of 64-bit words with an output size equivalent to the 32-bit register variants (i.e., 224 and 256 digest sizes and better performance on a 64-bit CPU).

The following is a table that gives a good overview of the SHA-2 family:

Hash function

Message size

Block size

Word size

Digest size


< 264





< 264





< 2128





< 2128





< 2128





< 2128




You can hash an empty string with the hashlib module and compare digest sizes as follows:

>>> import hashlib
>>> len(hashlib.sha224(b"").hexdigest())
>>> len(hashlib.sha256(b"").hexdigest())
>>> len(hashlib.sha384(b"").hexdigest())
>>> len(hashlib.sha512(b"").hexdigest())

Attacks on SHA-2

Several (pseudo-)collision and preimage attacks have been demonstrated using SHA-256 and SHA-512 with less rounds. It is important to note that by removing a certain amount of rounds one can’t attack the entire algorithm. For instance, Somitra Kumar Sanadhya and Palash Sarkar were able to cause collisions with SHA-256 using 24 of 64 rounds (removing the last 40 rounds).

Keccak and SHA-3

Keccak is a family of sponge functions designed by Guido Bertoni, Joan Daemen, Gilles Van Assche and Michaël Peeters, which won NIST’s Secure Hash Algorithm Competition in 2012. Keccak has since been standardized in form of the SHA3-224, SHA3-256, SHA3-384 and SHA3-512 hash functions.

Although SHA-3 sounds like it might come from the same family as SHA-2, the two are designed very differently. SHA-3 is very efficient in hardware, but is relatively slow in software in comparison to SHA-2.

Later in the book, you will find the security aspects of SHA-3, such as preventing length extension attacks.

The SHA-3 hash functions were introduced in Python version 3.6 and can be used as follows:

import hashlib

Password storage

One of the most common use cases for cryptographic hash functions, and unfortunately one which is also completely and utterly broken, is password storage.

Suppose you have a service where people log in using a username and a password. You’d have to store the password somewhere, so that next time the user logs in, you can verify the password they supplied.

Storing the password directly has several issues. Besides an obvious timing attack in the string comparison, if the password database were to be compromised, an attacker would be able to just go ahead and read all of the passwords. Since many users re-use passwords, that’s a catastrophic failure. Most user databases also contain their e-mail addresses, so it would be very easy to hi-jack a bunch of your user’s accounts that are unrelated to this service.

Hash functions to the rescue

An obvious approach would be to hash the password using a cryptographically secure hash function. Since the hash function is easy to compute, whenever the user provides their password, you can just compute the hash value of that, and compare that to what you stored in the database.

If an attacker were to steal the user database, they could only see the hash values, and not the actual passwords. Since the hash function is impossible for an attacker to inverse, they wouldn’t be able to turn those back into the original passwords. Or so people thought.

Rainbow tables

It turns out that this reasoning is flawed. The amount of passwords that people actually use is very limited. Even with very good password practices, they’re strings somewhere between 10 and 20 characters, consisting mostly of things that you can type on common keyboards. In practice though, people use even worse passwords: things based on real words (password, swordfish), consisting of few symbols and few symbol types (1234), or with predictable modifications of the above (passw0rd).

To make matters worse, hash functions are the same everywhere. If a user re-uses the same password on two sites, and both of them hash the password using MD5, the values in the password database will be the same. It doesn’t even have to be per-user: many passwords are extremely common (password), so many users will use the same one.

Keep in mind that a hash function is easy to evaluate. What if we simply try many of those passwords, creating huge tables mapping passwords to their hash values?

That’s exactly what some people did, and the tables were just as effective as you’d expect them to be, completely breaking any vulnerable password store. Such tables are called rainbow tables. This is because they’re essentially sorted lists of hash function outputs. Those outputs will be more or less randomly distributed. When written down in hexadecimal formats, this reminded some people of color specifications like the ones used in HTML, e.g. #52f211, which is lime green.


The reason rainbow tables were so incredibly effective was because everyone was using one of a handful of hash functions. The same password would result in the same hash everywhere.

This problem was generally solved by using salt{.interpreted-text role=”term”}s. By mixing (appending or prepending1) the password with some random value before hashing it, you could produce completely different hash values out of the same hash function. It effectively turns a hash function into a whole family of related hash functions, with virtually identical security and performance properties, except with completely different output values.

The salt value is stored next to the password hash in the database. When the user authenticates using the password, you just combine the salt with the password, hash it, and compare it against the stored hash.

If you pick a sufficiently large (say, 160 bits/32 bytes), cryptographically random salt, you’ve completely defeated ahead-of-time attacks like rainbow tables. In order to successfully mount a rainbow table attack, an attacker would have to have a separate table for each of those salt{.interpreted-text role=”term”} values. Since even a single table was usually quite large, storing a large amount of them would be impossible. Even if an attacker would be able to store all that data, they’d still have to compute it first. Computing a single table takes a decent amount of time; computing \(2^{160}\) different tables is impossible.

Many systems used a single salt for all users. While that prevented an ahead-of-time rainbow table attack, it still allowed attackers to attack all passwords simultaneously, once they knew the value of the salt. An attacker would simply compute a single rainbow table for that salt, and compare the results with the hashed passwords from the database. While this would have been prevented by using a different salt for each user, systems that use a cryptographic hash with a per-user salt are still considered fundamentally broken today; they are just harder to crack, but not at all secure.

Perhaps the biggest problem with salts is that many programmers were suddenly convinced they were doing the right thing. They’d heard of broken password storage schemes, and they knew what to do instead, so they ignored all talk about how a password database could be compromised. They weren’t the ones storing passwords in plaintext, or forgetting to salt their hashes, or re-using salts for different users. It was all of those other people that didn’t know what they were doing that had those problems. Unfortunately, that’s not true. Perhaps that’s why broken password storage schemes are still the norm.

Modern attacks on weak password systems

To a modern attack, salts quite simply don’t help. Modern attacks take advantage of the fact that the hash function being used is easy to compute. Using faster hardware, in particular video cards, we can simply enumerate all of the passwords, regardless of salt.

Salts may make precomputed attacks impossible, but they do very little against an attacker that actually knows the salt. One approach you might be inclined to take is to attempt to hide the salt from the attacker. This typically isn’t very useful: if an attacker can manage to access the database, attempts to hide the salt are unlikely to be successful. Like many ineffective home-grown crypto schemes, this only protects against an incredibly improbable event. It would be much more useful to just use a good password store to begin with, than trying to fix a broken one.

So where do we go from here?

In order to protect passwords, you need a (low-entropy) key derivation function. We’ll discuss them in more detail in a future chapter.

While key derivation functions can be built using cryptographic hash functions, they have very different performance properties. This is a common pattern: while cryptographic hash functions are incredibly important primitives for building secure tools (such as key derivation functions or message authentication algorithms), they are routinely abused as those tools themselves. In the rest of this chapter, we will see other examples of how cryptographic hash functions can be used and abused.

Length extension attacks

In many hash functions, particularly the previous generations, the internal state kept by the hash function is used as the digest value. In some poorly engineered systems, that causes a critical flaw: if an attacker knows \(H(M_1)\), it’s very simple to compute \(H(M_1 \| M_2)\), without actually knowing the value of \(M_1\). Since you know \(H(M_1)\), you know the state of the hash function after it’s hashed \(M_1\). You can use that to reconstruct the hash function, and ask it to hash more bytes. Setting the hash function’s internal state to a known state you got from somewhere else (such as \(H(M_1)\)) is called fixation.

For most real-world hash functions, it’s a little bit more complicated than that. They commonly have a padding step that an attacker needs to recreate. MD5 and SHA-1 have the same padding step. It’s fairly simple, so we’ll go through it:

  1. Add a 1 bit to the message.
  2. Add zero bits until the length is \(448 \pmod {512}\).
  3. Take the total length of the message, before padding, and add it as a 64-bit integer.

For the attacker to be able to compute \(H(M_1 \| M_2)\) given \(H(M_1)\), the attacker needs to fake that padding, as well. The attacker will actually compute \(H(M_1 \| G \| M_2)\), where \(G\) is the glue padding, called that way because it glues the two messages together. The hard part is knowing the length of the message \(M_1\).

In many systems, the attacker can actually make fairly educated guesses about the length of \(M_1\), though. As an example, consider the common (broken) example of a secret-prefix authentication code. People send messages \(M_i\), authenticated using \(A_i = H(S \| M_i)\), where \(S\) is a shared secret. We’ll see (and break) this MAC algorithm in a future section.

It’s very easy for the recipient to compute the same function, and verify the code is correct. Any change to the message \(M_i\) will change the value of \(A_i\) drastically, thanks to the avalanche effect. Unfortunately, it’s quite easy for attackers to forge messages. Since the MAC is usually sent together with the original message, the attacker knows the length of the original message. Then, the attacker only has to guess at the length of the secret, which is often fixed as part of the protocol, and, even if it isn’t, the attacker will probably get in a hundred tries or less. Contrast this with guessing the secret itself, which is impossible for any reasonably chosen secret.

There are secure authentication codes that can be designed using cryptographic hash functions: this one just isn’t it. We’ll see better ones in a later chapter.

Some hash functions, particularly newer ones such as SHA-3 competition finalists, do not exhibit this property. The digest is computed from the internal state, instead of using the internal state directly.

This makes the SHA-3-era hash functions not only a bit more fool-proof, but also enables them to produce simpler schemes for message authentication. (We’ll elaborate on those in a later chapter.) While length extension attacks only affected systems where cryptographic hash functions were being abused in the first place, there’s something to be said for preventing them anyway. People will end up making mistakes, we might as well mitigate where we can.

Hash trees

Hash trees are trees2 where each node is identified by a hash value, consisting of its contents and the hash value of its ancestor. The root node, not having an ancestor, simply hashes its own contents.

This definition is very wide: practical hash trees are often more restricted. They might be binary trees3, or perhaps only leaf nodes carry data of their own, and parent nodes only carry derivative data. Particularly these restricted kinds are often called Merkle trees.

Systems like these or their variants are used by many systems, particularly distributed systems. Examples include distributed version control systems such as Git, digital currencies such as Bitcoin, distributed peer-to-peer networks like Bittorrent, and distributed databases such as Cassandra.

Remaining issues

We’ve already illustrated that hash functions, by themselves, can’t authenticate messages, because anyone can compute them. Also, we’ve illustrated that hash functions can’t be used to secure passwords. We’ll tackle both of these problems in the following chapters.

While this chapter has focused heavily on what hash functions can’t do, it can’t be stressed enough that they are still incredibly important cryptographic primitives. They just happen to be commonly abused cryptographic primitives.

  1. While you could also do this with XOR, it’s needlessly more error-prone, and doesn’t provide better results. Unless you zero-pad both the password and the salt{.interpreted-text role=”term”}, you might be truncating either one. 

  2. Directed graphs, where each node except the root has exactly one ancestor. 

  3. Each non-leaf node has no more than two children 

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