Audit of Silent Protocol Circuits

Description. The feeRegistration circuit is used when a user registers for zero-fee withdrawals. They pay a subscription fee and, in return, are able to use the complementary WithdrawFeeReduction method for a period of time. The block number at which the subscription ends is a public input of the feeRegistration circuit, called validUntil.

Like other Silent Protocol methods, the interacting user (sender) is kept hidden in an anonymity set of 8 accounts. The sender’s index within the array of size 8 is a private input. No-op account updates are performed on all 8 accounts, such that the actual update to the sender’s account – setting a new validUntil value – can’t be distinguished from the outside.

However, contrary to the other Silent Protocol methods reviewed by zkSecurity, feeRegistration falls short of protecting the sender’s anonymity.

For context, each user account stores ElGamal ciphertexts $(C_1,C_2)$ which represent $v:=$validUntil via

where $r$ is encryption randomness and $X$ is the users public key. The circuit takes two versions of each of the 8 ciphertexts as public inputs: before and after applying the update.

The circuit takes new encryption randomness $r^{\prime }$ as private input. The following operations are then performed to update ciphertexts:

• For the sender account, the new $v$ is entirely re-encrypted using $r^{\prime }$: $$(C_1,C_2)\leftarrow (r^{\prime }G,vG+r^{\prime }X)$$
• All 8 accounts are rerandomized, using the same $r^{\prime }$: $(C_1,C_2)\leftarrow (C_1+r^{\prime }G,C_2+r^{\prime }X)$

By contrast, in balance updates we don’t encrypt the sender value from scratch, but just add an amount $aG$ to $C_2$ exploiting the additive homomorphism of ElGamal encryption.

The problem with the outlined method of resetting $v$ is that it’s easy to distinguish the sender update from the others, just based on how the ciphertexts change. Note that for all other accounts, $C_1$ changes by adding $r^{\prime }G$. This is not the case for the sender account, which is reset to the new value $2r^{\prime }G$. So, to find out the secret index in $0,\dots ,7$ which belongs to the sender, just look at the difference $\Delta C_1$ for each account and note that one is different from all the others. Then, look up which public key belongs to that index, and you have successfully deanonymized the sender.

Impact. Deanonymizing the sender breaks the core promise of the protocol, which is to hide users within an anonymity set.

Recommendation. We can mimic how balance updates work. The recommendation is to introduce the previous value $v_{\text{old}}$ as a private circuit input, so that the sender update can just add to $C_2$:

• For the sender account, update: $C_2\leftarrow C_2+(v-v_{\text{old}})G$
• Rerandomize all 8 accounts: $(C_1,C_2)\leftarrow (C_1+r^{\prime }G,C_2+r^{\prime }X)$

For the $v$ update, the BalanceUpdate template can be reused:

component updateSender = BalanceUpdate();
updateSender.amount <== validUntil + SUBORDER - oldValidUntil;

Since validUntil is checked to be a smallish value by the smart contract logic that validates public inputs, we recommend to pass $v+p-v_{\text{old}}$ as input to BalanceUpdate, where $p$ is the scalar field size. This expression will never overflow the native field since $p$ is much smaller than the native modulus. By adding $p$, we ensure that passing $v<v_{\text{old}}$ doesn’t cause an underflow, which would lead to a very long subscription period.

Client response. Silent Protocol fixed the issue, following our recommendation.

Description. To distribute secret shares publicly, they are encrypted using hashed ElGamal encryption:

where $ss$ is the secret share, $X$ is the public key of its recipient and $r$ is encryption randomness.

In the protocol, $c_2$ and $ss$ are BabyJubJub scalars, not native field elements. The SecretSharing template represents them as bigints and computes them in non-native arithmetic.

To compute $\text{H}(rX)$, the protocol uses Poseidon over the native field. Since the native field is bigger than the scalar field, the hash output is reduced modulo the subgroup order to become a valid scalar.

The template uses the following code:

secretHasher[i] = Poseidon(2);
secretHasher[i].inputs[0] <== rPubKeys[i].out[0];
secretHasher[i].inputs[1] <== rPubKeys[i].out[1];

var SUBORDER = 2736030358979909402780800718157159386076813972158567259200215660948447373041;
hashed[i] <-- secretHasher[i].out % SUBORDER;
sum === hashed[i];

Here, sum contains $c_2-ss$, stored in a single field element. hashed[i] is supposed to represent $\text{H}(rX)$ reduced modulo SUBORDER. In the last line, the code asserts the equality $c_2-ss=\text{H}(rX)$ to show that $c_2$ is the correct encrypted secret share.

The problem is that the modulo reduction secretHasher[i].out % SUBORDER is computed outside the circuit, and stored in hashed[i] with the witness assignment operator <--. This means that hashed[i] is not provably linked to the output of secretHasher in any way. From the circuit point of view, it is completely unconstrained and its value can be set arbitrarily by the prover.

At zkSecurity, we have called this common mistake a boomerang value: A value that leaves the circuit and gets reinserted later, without being linked to the original computation.

Impact. A malicious user can publish any values they want as encrypted secret shares and have them accepted by the smart contract. This severely breaks the compliance component of the protocol.

Client response. Silent Protocol patched the issue by redefining the protocol, such that hashed ElGamal encryption now happens in the native field. The secret share $ss$ is still a bigint scalar, but it can naturally be embedded in the native field because the scalar field is smaller. The check $c_2-ss=\text{H}(rX)$ is now a simple constraint between two native field elements and we no longer need reduction modulo the subgroup order.

Description. Secret shares are computed by evaluating a polynomial $p(X)$ on a set of points $(x_i)$:

This evaluation happens in non-native arithmetic, so the polynomial coefficients $(p_j)$ and evaluation points $(x_i)$ are passed in as bigints: fixed-size arrays of signals which represent the limbs of a bigint. Concretely, 4 limbs of 64 bits each are used, so a value like $x_i$ is represented as $(x_{i0},x_{i1},x_{i2},x_{i3})$ where $x_i={\sum }_{k=0}^3{x}_{ik}{2}^{64k}$.

The polynomial evaluation circuit is built on top of non-native arithmetic gadgets like FpAdd and FpMultiply originally taken from circom-pairing, which themselves rely on bigint implementations taken from circom-ecdsa.

These non-native gadgets constrain their outputs to be valid bigints with limbs in $[0,2^{64})$. However, none of them constrain their inputs; so, input limbs to FpAdd and FpMultiply are assumed to already be range-checked to 64 bits.

The choice to range-check outputs but not inputs is very natural. If each gadget would range-check its inputs, then range checks would be applied multiple times when you used different gadgets on the same input values, wasting constraints. By following the convention of always constraining witnesses where they originate, we keep the code auditable and also ensure that the same constraints aren’t applied multiple times.

The issue is that secretShare.circom doesn’t follow this convention: None of the input bigint limbs are range-checked. In the abbreviated code below, the signals c2, indexes, polynomial and secretShare all represent bigints that are not range-checked.

template SecretSharing(THRESHOLD, SHARES, n, k, p) {

    // Public Signals
    // ...
    signal input c2[SHARES][k];
    signal input indexes[SHARES][k];

    // Private Signals
    signal input polynomial[THRESHOLD][k];
    signal input secretShares[SHARES][k];

    // ...

    /// 4- evaluate the polynomial at index_i and
    /// verify p(index_i) = secretShares_i
    component evalPoly[SHARES];
    // ...

    for (var i = 0; i < SHARES; i++) {
        evalPoly[i] = EvalPoly(THRESHOLD, n, k, p);
        for (var j = 0; j < THRESHOLD; j++) {
            for (var l= 0; l < k; l++) {
                evalPoly[i].polynomial[j][l] <== polynomial[j][l];
            }
        }

        for (var j=0; j < k; j++) {
            evalPoly[i].eval[j] <== secretShares[i][j];
            evalPoly[i].at[j] <== indexes[i][j];
        }

        // ...

Impact. To understand the impact at the hand of a simple example, let’s look at the ModSum template which is used under the hood by FpAdd to add two limbs with a carry:

// addition mod 2**n with carry bit
template ModSum(n) {
    assert(n <= 252);
    signal input a;
    signal input b;
    signal output sum;
    signal output carry;

    component n2b = Num2Bits(n + 1);
    n2b.in <== a + b;
    carry <== n2b.out[n];
    sum <== a + b - carry * (1 << n);
}

If both a and b are constrained to $n$ bits, this template indeed performs addition modulo $2^n$. However, if a and b can be any field element, we can make a + b overflow the native field. For example, let $a=q-1$ where $q$ is the native modulus, and $b=1$. Then the sum is $0$, which is not the correct result mod $2^n$.

This demonstrates a general principle: Missing range checks often let us introduce overflow of $q$ in non-native arithmetic, where it’s not supposed to happen. This can make room for the prover to modify the output or input of non-native computations, by adding or subtracting multiples of $q$.

We explain a concrete attack which breaks the compliance protocol when exploiting this in combination with finding #03; see that finding for details. But the missing range checks alone can likely be exploited to modify either commitments to polynomial coefficients or secret shares.

Recommendation. All limbs of the input signals c2, indexes and polynomial should be range-checked to 64 bits. An appropriate way to do this is to pass the checked limb as input to Num2Bits(64) from circomlib/circuits/bitify.circom. Notably, secretShares does not need to be range-checked because it is forced to equal the output of the EvalPoly template, which is already range-checked.

Client response. Silent Protocol fixed the issue by adding range checks to indexes and polynomial as recommended. The signal c2 was refactored to be a native field element in response to finding #01, so the need for a range check no longer applies to it.

Description. One of the objectives of the SecretSharing template is to prove the correctness of commitments to the polynomial which is used for secret sharing. Commitments are of the form $p_{i}G$ where $p_i$ is a polynomial coefficient and $G$ a constant base point on the BabyJubJub curve.

Polynomial coefficients $p_i$ are passed in as bigints (arrays of limbs). To perform scalar multiplication, the code collapses the limbs to obtain a single field element which is passed to the MulBase template as the scalar.

/// 1 - compute polynomial coefficients p_i
/// p = p_0 + p_1 x + p_2 x^2 + ... + p_n-1 x^n-1
signal poly[THRESHOLD];
for (var i=0; i < THRESHOLD; i ++){
    var sum = 0;
    for (var j=0; j<k; j++){
        sum += polynomial[i][j] * ((2 ** n) ** j);
    }
    poly[i] <== sum;
}

/// 2- check broadcast_i = p_i * G
component mulScalars[THRESHOLD];
for(var i=0; i<THRESHOLD; i++) {
    mulScalars[i] = MulBase();
    mulScalars[i].e <== poly[i];\
    // ...

There is an interplay between arithmetic in two fields here: the native field and the scalar field. Let’s define

• $q:=$ the native modulus
• $p:=$ the scalar modulus (i.e. $p$ is the order of the subgroup generated by $G$).

Note how poly[i] is computed: The limbs polynomial[i][j] are multiplied with their corresponding power of two and summed, in native field arithmetic. So we end up constraining poly[i] to be

But the value we actually would like to put in poly[i] is

This is the scalar that $p_{ij}$ are supposed to represent, and that is implicitly used elsewhere in the code where the polynomial is evaluated in scalar arithmetic. We also don’t care about multiples of $p$ added to $p_i$, since they are eliminated by scalar multiplication.

The problem is that the prover can easily make $p_i$ differ from $p_i^{\prime }$, if the limbs $p_{ij}$ can represent values larger than $q$. Just write

and choose limbs $p_{ij}$ that represent $p_i$. $c_i$ is an arbitrary value with the requirement that $p_i^{\prime }+c_{i}q<(c_i+1)q$ is within the max value that the limb sum can represent. Note that $p_{ij}$ are entirely controlled by the secret sharing prover and can be chosen to facilitate an attack.

In the audited version of the code, $p_{ij}$ are not range-checked and take values in $[0,q)$, see finding #02. Therefore, a limb sum can represent values up to $q{2}^{(k-1)n}$ (plus a bit more), which means that $c_i$ can be any number in $[0,2^{(k-1)n})$. Plugging in $k=4$ and $n=64$, we see that $c_i$ can take one out of $2^{192}$ values.

Impact. When picking $c_i\ne 0$, the scalar multiplication creates a modified commitment $A_i^{\prime }=(p_i-c_{i}q)G=A_i-c_{i}qG$. The recipient of the share is supposed to use this commitment to validate their share $p(x_i)$, by checking that

This fails when one of the commitments is modified, breaking the protocol. Since there are $2^{192}$ ways for the prover to modify each commitment, there is no way for the share recipient to recover the true commitments $A_i=p_{i}G$.

Assuming that finding #02 is fixed and each limb is constrained to the range $[0,2^n)$, a limb sum can only represent values up to $2^{kn}=2^{256}$. This means $c_i$ in our analysis can at most be $\lfloor 2^{256}/q\rfloor =5$. Since commitments can be modified independently and the polynomial degree is currently set to $5-1$, there are $6^5=7776$ ways for the prover to change the broadcasted commitments. This should be recoverable by a custom trial and error algorithm – assuming that the secret share $p(x_j)$ is received in untampered form; see finding #04 for why this is not the case.

Recommendation. There is no need to collapse bigint limbs to a single field element before using them for scalar multiplication. The fact that this is done seems to be just an artifact of the MulBase API which takes a field element. Under the hood, MulBase uses circomlib’s BabyPk which unpacks the field element to an array of bits. The bits are passed to EscalarMulFix which contains the core scalar multiplication circuit.

We recommend to unpack the bigint limbs polynomial[i][j] into bits directly. This can serve the double purpose of constraining those limbs to their correct bit size. The resulting bits can be passed to EscalarMulFix to perform scalar multiplication.

Client response. The issue was fixed by Silent Protocol, following our recommendation of using EscalarMulFix directly.

Description. After the SecretSharing template evaluated a polynomial on several points to compute secret shares $s{s}_i$, the goal is to prove correct encryption of those shares by showing that

Here, $c_{2i}$ is the encrypted value which is a public input. The code represents $c_{2i}$ and $s{s}_i$ as bigints and computes the subtraction $c_{2i}-s{s}_i$ in foreign-field arithmetic, using the BigSubModP template. Then, the bigint version of $c_{2i}-s{s}_i$ is collapsed into a single field element using native arithmetic, which finally is compared with the hash value on the right hand side.

/// 5- verify secret shares are encrypted correctly
subModP[i] = BigSubModP(n,k);
for (var j=0; j <k; j++){
    subModP[i].a[j] <== c2[i][j];
    subModP[i].b[j] <== secretShares[i][j];
    subModP[i].p[j] <== p[j];
}

var sum = 0;
for (var j=0; j<k; j++){
    sum += subModP[i].out[j] * ((2 ** n) ** j);
}

// ...
sum === hashed[i];

The way the sum is only constrained modulo the native modulus $q$ creates a problem analogous to finding #03. The bigint stored in subModP can hold values larger than $q$, which allows the prover to provide multiple modified versions of the public output c2[i][]. Namely, we can use

for a few different values $d_i$, which in turn will cause the share recipient to obtain the modified decrypted secret share

The value $d_i$ has to be small enough such that subModP[i]$\in [0,2^{256})$ can still hold $c_{2i}-s{s}_i+d_{i}q$. Typically, $d_i$ can take 6 different values $0,\dots ,5$. A crucial point here is that while BigSubModP forces its outputs to be valid bigints with 64-bit limbs, it doesn’t require the inputs or output to be smaller than $p$.

Impact. Allowing 6 different values for the main circuit output $c_{2i}$, exactly one of which lies in $[0,q)$, might seem non-ideal, but not catastrophic. Can’t the recipient of the secret share simply reduce their encrypted share modulo $q$ to obtain the correct value? The reason we count this as a medium-severity issue is due to its interplay with two other findings:

• Finding #05 shows that there is ambiguity in the value of the secret share $s{s}_i$: It can be modified by a multiple of $p$. The multiple of $p$ can also be added to $c_{2i}$ without affecting the difference $c_{2i}-s{s}_i$. With this extra degree of freedom in $c_{2i}$, there is no longer a unique reduction that a share recipient can perform to obtain a decrypted share in the correct range. A trial-and-error procedure using the broadcast polynomial commitments to validate secret shares might be possible.

• However, as finding #03 demonstrates, polynomial commitments can themselves be modified by the prover, which further complicates any recovery of the correct secret shares.

In combination, findings #03, #04 and #05 can likely be used to put the compliance protocol in a temporarily broken or hard-to-recover-from state.

Recommendation. There are several general recommendations to make. First, instead of collapsing the bigint $c_{2i}-s{s}_i$ to a native field element before comparing with another value, consider if it is possible to just compare the bigint limbs directly. This requires both sides to be represented in bigint form.

Second, in the case of a public input, ambiguity can be mitigated by making the verifier (here: the smart contract) validate the input; this would mean checking that each $c_{2i}$ is in $[0,p)$.

Finally, a bigint comparison in the circuit can be used to ensure a value is in its canonical range. Constraining subModP[i] to $[0,p)$ would have prevented the issue.

Client response. In light of both this finding and finding #01, Silent Protocol redefined secret share encryption to happen in the native field. $c_{2i}$ is now a native field element and this finding, as well as the recommendations, no longer apply.

Description. The EvalPoly template computes the evaluation of a polynomial in non-native arithmetic:

Multiplications use the FpMultiply gadget taken from circom-pairing. This gadget allows the reduction $b=amodp$ by witnessing bigints $X$ and $b$, and proving that $a=pX+b$. There is no check that $b<p$ which would fix the result $b$ to a unique value; let’s call a $b$ canonical if $b\in [0,p)$. Since $X$ and $b$ are provided as a witness, the prover can easily add multiples of $p$ to $b$ by subtracting from $X$.

There are good reasons to not do a canonical check after every multiplication. If you need many multiplications, nothing is gained by ensuring every intermediate result is canonical if you only care that the final result is.

The EvalPoly template does not enforce that its final output is canonical. Therefore, the prover can modify the result to $s^{\prime }=s+cp$, where $c$ can be as large as allowed by $s+cp<2^{256}$. At most, there are $\lfloor 2^{256}/p\rfloor +1=43$ possible values for $c$.

The output of EvalPoly is used as the secret share by SecretSharing, so the prover can pick between 43 possible values per secret share.

Impact. There are two ways we found this ambiguity to impact the protocol at different iterations of the code base. In the audited version of the code, the secret share was encrypted in non-native arithmetic. As explained in finding #04, the freedom to add multiples of $p$ helps the prover to modify the final encrypted secret share $c_2$ in ways that can’t trivially be recovered.

A similar impact was found in a later version of the code after Silent Protocol fixed finding #01 and #04 by performing encryption of secret shares in the native field. To collapse the EvalPoly output to a native field value, a sum in native arithmetic is performed:

// `result[N][i]` holds the polynomial evaluation

var sum = 0;
for (var j=0; j<k; j++){
    sum += result[N][j] * ((2 ** n) ** j);
}
eval <== sum;

In this case, after summing in native arithmetic, the 43 possible values for eval$=s+cpmodq$ are no longer distinguishable by size or by reducing modulo $p$; some of them will end up being smaller than the unmodified value $s$. So, yet again, the prover is able to publish incorrect secret shares.

Recommendation. We recommend to add a dedicated canonical check at the end of EvalPoly which constrains the eval output to a unique value by enforcing that result[N][j] is smaller than $p$.

Client response. Silent Protocol fixed the issue by following our recommendation.

Description. The depositAndWithdraw circuit handles either deposits or withdraws. In the deposit case, it takes a positive amount which is added to the balance. In the withdraw case, amount contains the negative amount, i.e. a value of the form $p-|a|$ where $p$ is the scalar field size, and the absolute value $|a|$ is stored in another variable called isWithdraw.

To check that the amount is within the range $[0,2^{30})$, the circuit attempts to encode the following logic:

• In the deposit case, assert that amount is smaller than $2^{30}$.
• In the withdraw case, assert that isWithdraw is smaller than $2^{30}$.

The code looks like this:

/// 9- Checks that amount/withdraw amount is nonzero and less than 2^30
component isAmountZero = IsZero();
isAmountZero.in <== amount;
component isAmountLessThan = LessThan(252);
isAmountLessThan.in[0] <== amount;
isAmountLessThan.in[1] <== 1 << 30;

component isWithdrawAmountZero = IsZero();
isWithdrawAmountZero.in <== isWithdraw;
component isWithdrawAmountLessThan = LessThan(30);
isWithdrawAmountLessThan.in[0] <== isWithdraw;
isWithdrawAmountLessThan.in[1] <== 1 << 30;

signal depositCheck <-- !isWithdrawCase.out && !isAmountZero.out && isAmountLessThan.out;
signal withdrawCheck <-- isWithdrawCase.out && !isWithdrawAmountZero.out && isWithdrawAmountLessThan.out;

depositCheck + withdrawCheck === 1;

There are several issues, the most obvious of which is that depositCheck and withdrawCheck are completeley unconstrained: They only receive their values with the witness assignment <-- and the only constraints put on them is that their sum is 1. So, nothing in the circuit relates those values to the deposit or withdraw cases, and the prover is therefore not forced to pass amount and isWithdraw that are within the $2^{30}$ range.

A second issue is the circular logic used in the LessThan checks. The LessThan(30) gadget used for isWithdraw is only valid with inputs that are at most 30 bits. So, if the isWithdraw input is larger than 30 bits, then the output is not necessarily correct, so the entire check is superfluous. The same is true for an amount that is larger than 252 bits.

Impact. The reason why this is finding was given a low severity is that range checks on amount and isWithdraw are present in the contract logic which validates the public inputs to this circuit; therefore, the problem is not actually exploitable.

Recommendation. We don’t recommend to leave broken circuit logic in place even if it is without effect. Deployed code is often used as example for other projects, copied from one place to another, etc. Therefore, the recommendation is to either fix the range-checking logic or remove it.

Here is how the conditional check could be written instead:

// the number to be checked is either `amount` or `isWithdraw`, depending on the case
signal checkedAmount <== isWithdrawCase.out * (isWithdraw - amount) + amount;

// non-zero check
signal checkedAmountInv <-- checkedAmount != 0 ? 1/checkedAmount : 0;
checkedAmount * checkedAmountInv === 1;

// 30-bit range check
component rangeCheck = Num2Bits(30);
rangeCheck.in <== checkedAmount;

Client response. Silent Protocol has addressed the finding by removing the in-circuit range-check.

Description. The insecure randomness source of Math.random() is used to select the index of the anonymity set we are using for all entrypoints to the protocol. Under certain circumstances, an adversary might learn a bias for the random value and thus the anonymity set does not provide perfect indistinguishability between the choices in the set.

Example of one such infraction:

const senderIndex = Math.floor(Math.random() * RING_SIZE);

Recommendation. We recommend using cryptographically secure randomness sources for the sender index selection. In NodeJS 19 or higher as well as in modern browsers, you can use crypto.getRandomValues(new Uint8Array(1))[0], which returns a single random byte. Since we always want to pick between indices 0 through 7, we can look at the first 3 bits to get our securely random index value:

const senderIndex =
  crypto.getRandomValues(new Uint8Array(1))[0] & (RING_SIZE - 1);

As long as RING_SIZE remains a power of 2, this code will still work when the anonymity set size is changed.

Client response. Silent Protocol pointed out that the finding is out of scope of the audit; it was going to be addressed anyway.

Description. The log_ceil() helper function is used by FpMultiply to determine the maximum number of bits that certain expressions can have. Expressions arise in the bigint multiplication code and are of the form $S={\sum }_{i=1}^k{a}_i$ where $a_i$ are known to be $n$-bit integers. To determine the max number of bits of $S$, we want to estimate

Finding the smallest $m$ on the right hand side will give us a precise upper bound on the number of bits that $S$ needs. Assuming we knew that $k\le 2^K$, we could infer

so we find $m=n+K$. Choosing the minimal $K$ will make $m$ minimal as well. Note here that the inequality on $k$ is not strict – it’s fine if $k=2^K$, thanks to the $-1$ in the other factor.

Taking logs, we get the condition

and so the minimal $K$ can be written as $K=\lceil lo{g}_2(k)\rceil$. The maximum number of bits of $S$ is precisely $n+\lceil lo{g}_2(k)\rceil$. This motivates the use of a log_ceil() function for this problem.

The log_ceil() function at the time of the audit was implementing something slightly different, namely, it was computing the number of bits of $k$.

Here are some examples:

The difference is for $k$ a power of two, when the actual implementation returns something too large by 1.

Impact. In the current usages of the function, its incorrect result is not a security issue. Because of the specific use as an upper bound, returning something too large is just more conservative than necessary. If used in other contexts in the future, it could lead to more serious issues.

Recommendations. We recommend fixing log_ceil(), first because off-by-one issues could become a problem when used in other circumstances, and second because using more bits than necessary implies using more constraints than necessary.

Furthermore, at the time of audit log_ceil() defaults to 254 for any input larger than 254 bits. Again this is fine because inputs that large are not used in practice. We recommend adding an assertion preventing this case from being exercised.