Ensure the security of your smart contracts

Audit of a ZK application on example: Tornado Cash

Author: Sergey Boogerwooger
Security researcher at MixBytes
The following is an example of analyzing a ZKP-based project using a real-life example: Tornado Cash. This is not a full-scale security audit, but a demonstration of a ZK auditor's mindset. Our intention is to point out critical points in the code and demonstrate some issues specific to ZK audits. The real audit requires much more thorough analysis, studying all underlying algorithms and the proof system itself, but even without the deep study of underlying cryptographic constructions, a number of interesting issues can be mentioned, mostly in places where ZK proofs meet the outer code.

We will analyze different parts of the protocol and we'll do our best to show where an auditor's gaze should stop and which parts are security critical. Tornado Cash was chosen as an example because its codebase became the basis for many other projects that use "set-membership" proofs and it has fully proven itself as a real-world ZK application. Also, Tornado is not too large, so it can be observed in the article, and it is not too small, using multiple important SNARK-friendly primitives.
Contracts logic audit
Tornado has a part of its logic in smart-contracts that, of course, must be audited as well, but in this article, we will not look at the whole scope of Tornado's Solidity code but we will be focused only on the parts directly related to ZK proofs. At the same time, we cannot fully avoid the Solidity part of the logic because it defines how ZK proofs are used and how they can be used to attack the protocol, so, at least a high-level review of the contracts will be required. The main contract Tornado.sol is here. In short: user generates and saves two secret random values: secret and nullifier, the generates two hashes: commitment(from both values) and nullifierHash(from nullifier only). At the "deopsit()" stage user adds the commitment in the Merkle tree in the Tornado's contract with fixed amount of ETH. Later, at the "withdraw()" stage, user uses his secret, nullifier and public Merkle tree to generate the proof that she knows such a commitment, (generated from the same secret and nullifier), that belongs to this Merkle tree (without disclosing the exact leaf) and that nullifier corresponds to a correct nullifierHash. Contract at the same time saves used nullifierHash to avoid secondary "spending" of the used leaf.

Also, an important thing is the usage of the nullifierHash instead of a "pure" nullifier. It's made to mitigate the griefing of the withdrawer, when an attacker sees user's nullifier, frontruns the withdrawal, creates their own deposit with the same nullifier, withdraws it and so, denies the withdrawal of the original user.

You can read about the whole algorithm in many existing articles, we don't need to dive deeply into it, our goal is to concentrate on the ZK-related part.
Analyzing the ZK-related project structure
How ZK proofs are used
All ZKP applications include proof generation, so the most important attack vectors are those that allow verification to be bypassed based on the previously presented proofs, or by an attacker forging them using already known values. In addition, an auditor should consider the cases when proofs are frontrunned or stolen from services, calculating the proofs instead of the user (these services are rapidly evolving these times).

The main potential attack vectors to be considered are:

forging the incorrect proof
in this case an attacker can use some "wrong" values (i.e. values outside allowed ranges, zero values) producing a correct proof. These values later can be used after verification by the protocol

the reuse of existing proofs
in this case, an attacker can take a previously presented proof, change the values that are passed as public inputs keeping this proof valid, and re-submit the proof with the changed values

zero-knowledge abuse
in this case information from the public values or the proof can be used to recover private values and disclose them or to make some suggestions about their contents

Keeping in mind these vectors, we need to determine the values used as inputs and outputs of the proof system and analyze these vectors for each of them. To be more specific, for each input value(s) and proof(s) we need to check:

the value can be generated by a malicious attacker in a way, allowing to generate a valid proof

the same value or proof can be reused a second time, leading to a critical action to be performed

zero-knowledge abuse
the private value can be reconstructed or some of its critical properties can be suggested using public data

With all above, let's move to a higher-level flow analysis.
Analyzing the high-level flow
The main flow of user interaction with Tornado contracts includes two main phases: deposit() and withdraw().

Deposit() accepts a single bytes32 _commitment value. This commitment is added to the Merkle Tree, its value is stored in the Deposit emitted event, and this commitment is also stored in the contract to avoid duplicate use of the same commitment. The fixed amount of ETH is accepted with the call and added to the contract balance.

The commitment values in events are used by users to reconstruct the Merkle tree, containing their commitment later. Let's remember this fact because these are the values that are somehow controlled by the attacker (any user can add any commitment to the tree).

Withdraw() is the most critical part of logic where ZK proof is used and as the only part of the code that transfers ETH outside, it's the main entry point of possible attacks. We see input parameters:

function withdraw(
    bytes calldata _proof,
    bytes32 _root,
    bytes32 _nullifierHash,
    address payable _recipient,
    address payable _relayer,
    uint256 _fee,
    uint256 _refund
) external payable nonReentrant {
The last four parameters: _recipient, _relayer, _fee, _refund are essential for the whole logic, but in our ZK audit case, we can simply mention that any changes of these parameters must be performed strictly by the legitimate sender. No changes by any external user are allowed, while keeping the same proof.

The first three parameters are the most important for us: _proof, _root, _nullifierHash - it's the ZK proof, that proves that the user knows the secret value that is being concatenated with the nullifier and hashed, becomes one of the leaves of the Merkle tree.
The _proof allows to verify if this secret leaf belongs to our Merkle tree but not only this fact. This proof should NOT be reusable with other withdraw parameters because in case when, for example, _recipient is not included in the proof (the proof is valid for different _recipient values), an attacker can steal the proof and use it with their own _recipient address. Let's keep it in mind.
Now, three Solidity checks here:

require(_fee <= denomination, "Fee exceeds transfer value");
require(!nullifierHashes[_nullifierHash], "The note has been already spent");
require(isKnownRoot(_root), "Cannot find your merkle root"); // Make sure to use a recent one
The first one is the _fee check. The underlying logic in _processWithdraw() is not very interesting for us (it's to be checked in the full Solidity audit), but we note that _fee should be included in the ZK proof to avoid reusing the proof with another _fee value.

Another important thing to mention around this check is that arithmetic overflows are handled differently in EVM and inside the circuit (because values are processed over different fields), and some value, that doesn't lead to an overflow in EVM, overflows inside the circuit (or vice versa). So, the range check of the _fee parameter is important here not only to protect the Solidity logic.

The second one is the _nullifierHash check which is used to avoid secondary use of the same commitment. Let me recall how the nullifier is used: a user concatenates secret and nullifier and hashes it using the Pedersen hashing, receiving the commitment. The code from the test is here:

function generateDeposit() {
  let deposit = {
    secret: rbigint(31),
    nullifier: rbigint(31),
  const preimage = Buffer.concat([deposit.nullifier.leInt2Buff(31), deposit.secret.leInt2Buff(31)])
  deposit.commitment = pedersenHash(preimage)
  return deposit
So, the potential attack vector in this part of the code is the ability to bypass the require(!nullifierHashes[_nullifierHash] check by generating a new _nullifierHash, keeping the proof correct. Let's remember this point and return to it in the circuit part.

The last require(isKnownRoot(_root) check is related to a Tornado's logic of keeping several Merkle roots in a contract, creating a new Merkle root with each leaf added and tracking these roots in a list having the ROOT_HISTORY_SIZE size. This allows a user to create a proof for "one of the last ROOT_HISTORY_SIZE roots".

Here we see a low severity problem: the ability of an attacker to continuously create new Merkle roots with a speed that doesn't allow users to use "fresh" Merkle roots because they are all already outdated, blocking users from withdrawing (it's expensive for the attacker and doesn't prevent the user from generating new proofs, so it's a minor problem).

Another point to mention is the ability for an arbitrary user to add a new Merkle root and somehow control it. No ideas on this stage, but maybe it can be used in future, let's remember it as well.

The next important part of the code:

When performing an audit, an important step is to make sure whether the verification function is related exactly to the given circuit and has not been subverted. To check it, you need to generate a verification contract by yourself using the same setup and compare the verification functions.

In the parameters we see all the values provided by the user, so for all values: _root, _nullifierHash, _recipient, _relayer, _fee, _refund we need to recall the main vectors of ZKP attacks: forge, replay and zero-knowledge abuse. To accomplish it, we need to move to the circuit analysis.

The next Solidity part of the function is not in our audit scope, instead of saving the used _nullifierHash - this check is important. The withdraw logic and other Solidity parts can be checked by yourselves. Let's move to the ZKP part.
Analyzing the ZKP part
Circuit constraints
From the main Circom circuit template, we receive the whole pack of the inputs:

signal input root;
    signal input nullifierHash;
    signal input recipient; // not taking part in any computations
    signal input relayer;  // not taking part in any computations
    signal input fee;      // not taking part in any computations
    signal input refund;   // not taking part in any computations
    signal private input nullifier;
    signal private input secret;
    signal private input pathElements[levels];
    signal private input pathIndices[levels];
First, let's check for the inputs of the ZK proof: let's make sure that ALL inputs are used in proof generation and correctly constrained. It means that in the Circom circuit, each of these values must participate in the generation of constraints:

It's significant to understand that constraints in Circom are generated only using === or <== operators. Sometimes they can be missed, and something like <--- can be used instead. If you encounter an unconstrained <-- signal, it can be freely set to an arbitrary value by a malicious prover. It can be used sometimes for the optimization of the circuit, for example, here it allows to reduce a ternary operator from two constraints to one. Anyway, when you encounter <-- in Circom, you should know that a malicious prover can put any value here and the proof will still be correct. It can be used only if the following signal is correctly constrained.

As shown in the picture above, EVERY input was constrained in the circuit, even the pack of the recipient, relayer, fee, refund variables - they are specially constrained using multiplication (lines 61:64) to avoid their exclusion from the circuit by the Circom compiler (actually, it's not needed now, Circom will add the constraints for the input signal automatically, but here it requires only 4 constraints, which is a negligibly small amount). Now, the proof is "derived" from ALL input signals, and any change in any of them breaks the proof validity.
Circuit processing
The first interesting place in the circuit itself is the CommitmentHasher() used here. The hasher template is here:

As we see, it accepts two input signals: nullifier and secret and outputs two hashes: commitment and nullifier. The nullifierHash input is compared below with the result of hashing of input nullifier here in the hasher.nullifierHash === nullifierHash check. This is an important part of security that mitigates the problem when an external nullifierHash passed by an attacker doesn't correspond to a private nullifier value.

We have already checked that all inputs are constrained and each input has a corresponding constraint. Next, we see that nullifier and secret are split into 248-bits by the Num2Bits circuit. Here, we check that ALL the bits of input values are used, none is missed, for example, if the hashing of nullifier uses only 247 bits (not 248), then two different nullifiers will lead to the same commitment hash in the circuit that can lead to repeatable use of the proof with another nullifier). Sometimes developers want to reduce the amount of constraints and make proving (proof generation and size) lighter by reducing the bit lengths of some values, leading to security problems.
The next check - the Pedersen hash, which is known to have some properties described here and a homomorphic property that potentially can lead to some problems. Let's check:

  • a problem with "non-unique x-coordinate" from the article above is present here (resulting commitment and nullifierHash outputs use only coordinate "x" here, but this particular Pedersen implementation is not susceptible to this issue).
  • a "variable-length input" problem of collisions of the preimages with different bit lengths or partial bit lengths less than the curve order. It's also not present here because all inputs have fixed-size encoding.

The next point to be mentioned is the homomorphism of the Pedersen hash, meaning that (simplifying): H(M1 + M2) = H(M1) * H(M2), so having a "previous" Pedersen hash from some secret value s , one can compute H(s + <anything>) as H(s) * H(<anything>) not even knowing s. In our case it's not applicable as the secret and nullifier are used only once, but when Pedersen hashing is used multiple times, we should always consider this case.

Another part of the circuit is the Merkle proof check. Here is the code snippet:

First - let's check some low-level aspect of Merkle trees - hashing algorithm. The implementation of the Tornado's Merkle tree is here, and it uses MiMC hashing from circomlib and setup is in line 9 with component hasher = MiMCSponge(2, 1); (2 inputs, 1 output). Internally, MiMC can be configured with a different amount of rounds, and a small number of rounds can make this hashing insecure. If we follow mimcsponge.circom used in Tornado from include "../node_modules/circomlib/circuits/mimcsponge.circom", we'll see that MiMC here uses 220 rounds. It's more than enough to ensure the security of the hashing.
Then, let's examine some higher-level issues with Merkle trees. One of the first points is the use of zero leaves - a simple vector that can be easily forgotten. ZK projects usually use sparse Merkle trees where most of the leaves are zeroes, so building a Merkle proof for a zero leaf is possible. Here, it's not applicable (we cannot present a pedersen(nullifier + secret) == 0 and at the same time present a known pedersen(nullifier)). In addition, the Merkle tree implementation in Tornado uses a non-zero value for "zero" leaves (check here).

One more issue with Merkle trees is "the second preimage attack" (described here). It's working when the data used to hash the leaves has the same length as the size of Merkle tree nodes and when intermediate nodes can be used as leaves, leading to the correct Merkle proof reaching the same root. To check this, we need to be sure that our leaf (nullifier + secret) doesn't have the size of the Merkle tree hash or different hashing is used to calculate the first leaf hash (according to this). Tornado uses Pedersen for commitment hashing and compares it with nullifierNash here, so passing one of the intermediate nodes as a leaf will not work.

In Tornado's implementation the "append-only" Merkle tree is used, and each new leaf is added from left to right. So, different scenarios with inclusion/exclusion of the leaves are not applicable here (like repeatable insert/remove/insert/remove scenarios), but you should remember to check them when using more complicated logic with a Merkle tree.

Still another point to check is the size of the tree, here it's 20 levels (here) which is enough, as in case of overflow, new leaves will not be added (checked here) and a new contract with the same logic can be easily deployed.
Inputs forgeability, reuse and zero-knowledge abuse
The next part is analyzing each input and the possibility to forge, replay and abuse the zero knowledge property for each input:

This is the Merkle root of the tree, implemented in this circuit. We cannot forge this root according to this require(isKnownRoot(_root), "Cannot find your merkle root");. The reuse of the same root does make sense only with another nullifier, so there is no problem here. The root is public that's why we don't have a ZK problem here. Passed.

This is the Pedersen hash of nullifier + secret. We have already discussed Pedersen hashing above, and, since the nullifierHash and secret nullifier and secret inputs are tied and checked by the circuit, we can say it's safe against the forgeability of the nullifierHash. The replayability issues are mitigated by the require(!nullifierHashes[_nullifierHash], "..."); check in the contract here. The zero-knowledge property here is a little tricky because the Pedersen hash doesn't generate fully pseudorandom values and in some cases some suggestions about its preimage can be made, but in our case the secure generation of secret and nullifier mitigates this issue. Passed.

recipient, relayer, fee, refund
These values are external parameters used mostly by the contracts. We have already checked that they are included in the proof, so they cannot be replaced by other values using the same proof. Their replay is possible simply by design and they are public, so no zero-knowledge compromise is present. Passed.

nullifier + secret
Forging these parameters, allowing the same deposit to be reused twice or more, represents the main attack vector against Tornado. The goal of an attacker here is to submit a secondary nullifierHash "derived" from the single deposit (a single pair of secret and nullifier). If the Pedersen hash is checked in the contract, using a different Pedersen implementation (other than the one used in Circom), there could be some issues when the same values lead to different hashes in two implementations, but here the commitment hashing is checked in the circuit, making this attack vector inapplicable. Another forge/replay attack vector is possible when the secret + nullifier encoding somehow uses different bit lengths, allowing situations like (secre[t] + nullifier) == (secre + [t]nullifier), but here this situation is also not applicable. Zero-knowledge abuse cannot be exploited here as well due to the use of Pedersen hashing with enough bits of security and a suitably secure elliptic curve. Passed.

pathElements[levels] + pathIndices[levels]
These values are the Merkle proof of the commitment inclusion. The Merkle tree topics have been discussed above. The amount of levels is a circuit template parameter, so we cannot play with the size of the Merkle proof and secondary preimage attacks. Now, zero-knowledge property: in case when a single, "first in the tree" deposit was performed and a single withdrawal has followed, we, of course, can determine which leaf was used and deanonymize the depositor. Tornado works only when multiple deposits from different users were made. So, it's not possible to deploy "my own Tornado pool for myself", it will face this problem. The anonymity here is provided by many independent users, and when developing solutions based on Tornado scheme, you should always consider this issue. This is a good example of how the security of an underlying ZK protocol can become nonsense if the protocol is used incorrectly.
Final checklist-based analysis
An additional "checklist-based" check is crucial for any audit, especially in ZK applications and smart-contracts auditing. It "polishes" the previous results, covering all potentially dangerous patterns once again, checking whether anything important has been missed. Every audit company should have such a checklist. We don't plan to disclose ours but we will show some topics from it to demonstrate how this process can be performed. Let's go.

ZK proof reuse with other parameters
already checked above for all parameters, mostly mitigated by nullifierHash. This part can also be checked automatically by sequentially changing values in the witness and checking if the proof is still valid

Parameters, not included in the proof, leading to proof reuse
checked in the circuit part, all parameters are constrained

The sender disclosed by presenting the proof
covered by design, all is good if enough independent deposits have been made

Non-pseudorandom properties of SNARK-friendly hashes (Pedersen, MiMC)
pseudorandom properties of used hashings is not used here, not applicable

Pedersen, homomorphic property, bit lengths, other specific issues
discussed above, checked

MiMC, rounds, enough security
(ref) discussed above

Elliptic curves, issues with the "infinity" and (0,0) points
not applicable here

Merkle tree, zero-leaves, second preimage, multiple inclusin/exclusion issues
discussed above, checked

Field arithmetics, overflows
not applicable here

Groth16 issues, like malleability of proofs
(ref), not applicable here due to nullifierHash

Fiat-Shamir heuristic issues
(ref) this part is related to the underlying proof system. Here, it requires not only the analysis of Tornado Cash circuits, but also the implementation of the proof system. It should be perfomed in a full-scale audit, but it is not the case for our article

Trusted setup procedure
this is an important part of the audit if the proof system with the trusted setup is used. This setup procedure should also be checked by the auditor (there are many articles about this procedure)

There are many subjects in the checklist, we will not present them all, but you should definitely have your own checklist and keep it up-to-date as fresh security issues in ZK come up often and you can't miss them.
The analysis of a ZK project is a complicated procedure, lots of security topics are not only related to the logic of the project itself, but also require the understanding of the underlying proof systems, their internals: curves, hashings, Fiat-Shamir heuristics, specifics of the underlying maths. In addition, many performance issues can come up during the analysis, and the job of the auditor is to point them out as well as many decisions in ZK are made regarding the optimization of prove/verify procedures, and these decisions can lead to security flaws.

Moreover, this area is evolving rapidly, new proof systems come out every few months, and sometimes the analysis results become obsolete faster than the whole analysis gets ready - that's one of the reasons for the low number of qualified official ZK audit reports. Teams simply switch to new algorithms, change proof systems, significantly reconsider their internal logic. So, in order to be competitive in this market, you should keep up with many topics in this area, which makes this field extremely attractive for any highly skilled specialist who wants to work at the cutting edge of modern computer science and security. Good luck!
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