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Solana Formal Verification: A Case Study

We present a novel framework for formal verification of Solana Anchor programs -- and a case study application to the Squads multisig.

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Since the early days of computing, bugs have crept their way into programs and wreaked havoc on the intentions of the programmer. Logical fallacies, race conditions, or simple typos could manifest as crashes or lay undetected, silently breaking the functionality of the host program.

When your program is connected to the internet, there is the new risk that bugs may introduce security holes into your system. Even simple buffer overflows can be exploited by skilled attackers to compromise the integrity of your program.

In the world of Web3 we create programs that talk to strangers and control millions of dollars 🤑. Bugs in these programs are some of the juciest; anonymous attackers that can find and exploit them will walk away with potentially hundreds of millions of dollars.

At OtterSec we are highly skilled in pest control - finding and squashing bugs before they are exploited by less well-intentioned hackers. We are constantly striving to improve our techniques and develop new technologies that aid in our auditing processes.

Recently we were contacted by the Squads team to explore how formal verification could be used to verify security-critical properties of Solana programs. We were really excited about this opportunity and have been developing a prototype with the Squads Multisig Program as our main case study.

We now have a (mostly) working prototype that can be used to formally verify critical properties of Solana programs in order to ensure a higher level of security. Our tool integrates with anchor-lang and provides new APIs to specify invariants for your Solana code. It then autogenerates proof harnesses which are verified with the Kani Rust Verifier. Additionally, we are implementing a formal-verification-friendly runtime SDK layer that accelerates the expensive process of running formal verification tools on complex code.

In this blog post, we're excited to share our progress and the challenges we've encountered during the process. We will describe the main concepts behind bounded model checking (our formal verification method of choice) and explain how we've applied these concepts to Solana.

If you're interested in learning more or getting your own programs formally verified, let us know! We'd be excited to chat with you! -- Fill out this form or email us at contact@osec.io


  1. Formal Verification with Bounded Model Checking a. Overview b. A simple example c. Loop bounds & path explosion d. The Kani Rust Verifier
  2. Specification: How can we describe what we want our program to do?
  3. Verification: How do we check that our model is correct?
  4. Case Study: Squads Multisig
  5. Additional challenges in Solana
  6. Conclusion

1. Formal Verification with Bounded Model Checking


Formal verification is the process of using a formal specification to verify the correctness of a system. In this case, the systems we are verifying are programs written in Rust that run on the Solana blockchain.

There are many different flavors of formal verification, however in this research we are using bounded model checking (BMC).

In short, the idea of BMC is to execute our program symbolically rather than concretely. Instead of actually performing an add when we see the line int x = a + b, we store the symbolic expression x == a + b. We do this for every line and once we reach the end of the program we have compiled a huge list of symbolic expressions. At this point, we can feed these expressions to a SMT solver along with a correctness property P in order to check if our program satisfies this property.

If we hit a branch as we are tracing the program, we will take both sides of the branch adding the positive branch condition as a constraint to one side and the negative condition to the other side.

A simple example

As an example, consider the following function:

int foo(int x) {    int y = x + 3;    int z;    if (y > 100) {        z = y * 2;    } else {        z = y + 1;    }    // Property P:    assert(z != 105);}

This function takes an input x and does some computation. At the end of the program, the property we want to verify is that z != 105.

With BMC, we could trace this program and derive the following constraints:

Positive branch:

y == x + 3y > 100z == y * 2

Negative branch:

y == x + 3y <= 100z == y + 1

Using the z3 SMT solver, we could check both of these cases like so:

from z3 import *x = Int('x')y = Int('y')z = Int('z')# Positive branch:s = Solver()s.add(y == x + 3)s.add(y > 100)s.add(z == y * 2)# check if we can violate the correctness propertys.add(Not(z != 105))print(s.check()) # "unsat"# Negative branch:s = Solver()s.add(y == x + 3)s.add(y <= 100)s.add(z == y + 1)# check if we can violate the correctness propertys.add(Not(z != 105))print(s.check()) # "unsat"

Both of these cases return unsat meaning z3 could not find a way to violate the correctness property, hence our program is correct according to this property.

Loop bounds & path explosion

As you may have noticed, BMC requires us to take every branch in the program. To be sure that our property holds, we need to check every possible route through the program. If we have 10 branches in a row we might need to test 2^10 paths! And if our program has loops, we may need to check an infinite number of paths because the loop branches backward. This might take a while...

This is where the "bounded" part of "bounded model checking" applies. Rather than unroll an infinite number of loops, we can set a loop bound and also verify that it is not possible to loop more than the loop bound.

While this technique of bounding loops makes the problem tractable. It is still expensive to run BMC on very large programs due to the problem of path explosion. As our program gets larger, the number of possible paths scales potentially exponentially.

One of the main challenges we will discuss later is how to address this problem of path explosion in the context of Solana Rust programs.

Kani Model Checker

For our research with formally verifying Solana programs, we are using the Kani Rust Verifier: an open-source, bit-precise model checker for Rust created at AWS. Under the hood, Kani uses the C Bounded Model Checker (CBMC) to do the heavy lifting.

Kani allows you to write proof harnesses which can invoke Rust functions with symbolic values. These harnesses can assume and assert certain conditions about these symbolic values and then you can verify that a proof harness holds via the cargo kani tool (which compiles your proof harness and runs BMC).

2. Specification: How can we describe what we want our program to do?

And what even do we want it to do?

A fundamental challenge with any formal verification framework is specifying what the "correct" behavior should be.

In natural language, we can describe a few good properties for example Solana programs:

  • "It should not be possible to steal money via a swap program"
  • "A multisig should never get into a state where you can't sign anything"
  • "User funds in a staking protocol "

These are types of properties you can tell your human auditors but these English phrases are not particularly useful for automated verification techniques (at least until our AI overlords surpass human intelligence).

Instead, we need to be able to specify in code what properties we want to check. Ideally, we could define invariants that fit nicely into something like an assert statement.

Solana Invariants

In the context of Solana programs we define two different types of properties that we would like to verify: instruction invariants and account invariants.

Instruction Invariant

An instruction invariant specifies sufficient conditions for an instruction to succeed (or fail). These are specified as succeeds_if or errors_if macro annotations on the instruction handler.

In Solana, when an instruction fails, the entire transaction is reverted. Failing an instruction on purpose is commonly used as a form of access control; invalid accounts, bad state, etc... will cause an instruction to fail and get reverted.

For example, say we have a Withdraw instruction that lets a user withdraw some tokens. A security critical property we may want to verify is that the user cannot withdraw more tokens than their current balance.

Using our tool, you could specify the following errors_if property on your instruction handler:

#[errors_if(    ctx.user.balance < amount)]fn withdraw(ctx: Context<Withdraw>, amount: u64) -> Result<()> {    ...}

The errors_if expression specifies succifient but not necessary conditions for an instruction to fail. I.e. it imposes a strong lower bound on what the requirements are for an instruction to fail.

Another example is that for crank functions -- run by unauthenticated users to advance the state of the system, you may want to prove that they never fail. In that case, you could specify an invariant like the following:

#[succeeds_if(true)]fn my_crank(ctx: Context<Crank>) -> Result<()> {    ...}

With this invariant, you could prove that the function always returns Ok. This type of construction could help avoid possible denial of service attacks if a crank could get "stuck."

Note that succeeds_if and errors_if are both implications and not biconditionals. That is, a function may succeed even if succeeds_if is not satisfied and a function may fail even if errors_if is not satisfied. If you want to prove the exact condition required for an instruction to succeed, you could use a form like the following:

fn my_invariant(...) -> bool { ... }#[succeeds_if(my_invariant(...))]#[errors_if(!my_invariant(...))]fn my_instruction(ctx: Context<...>) -> Result<()> {    ...}

Note that in practice, it is usually not necessary (or useful) to find the exact condition; rather we can achieve the security properties we want purely by proving upper and lower bounds on instruction success.

Account Invariants

The other type of invariant is an Account Invariant. This invariant describes some property of an account that should always hold.

In our tool, we verify that the account invariant holds after every instruction that could modify the account data (i.e. if the account is mut or init).

For example, given a mock UserStatement account that represents how much a user owns and owes, we could write an invariant that asserts that the net balance is positive:

#[account]#[invariant(    self.assets >= self.liabilities)]struct UserStatement {    pub owner: Pubkey,    pub assets: u64,    pub liabilities: u64,}

Our tool automatically generates the relevant harnesses to ensure that this property holds every time an account of type UserStatement is created or modified.

In another example, we developed the following invariant for the Squads Multisig wallet account:

#[account]#[invariant(    !self.keys.is_empty()    && (self.keys.len() <= u16::MAX as usize)    && (self.threshold >= 1)    && (self.threshold as usize <= self.keys.len()))]pub struct Ms {    pub threshold: u16,               // threshold for signatures    pub authority_index: u16,         // index to seed other authorities under this multisig    pub transaction_index: u32,       // look up and seed reference for transactions    pub ms_change_index: u32,         // the last executed/closed transaction    pub bump: u8,                     // bump for the multisig seed    pub create_key: Pubkey,           // random key(or not) used to seed the multisig pda    pub allow_external_execute: bool, // allow non-member keys to execute txs    pub keys: Vec<Pubkey>,            // keys of the members}

Here we are verifying multiple things at once:

  • !self.keys.is_empty() : ensure there is at least one member
  • self.keys.len() <= u16::MAX as usize : set an upper limit of 65535 members
  • self.threshold >= 1 : ensure we always need at least one member to sign (threshold of zero would require no signers!)
  • self.threshold as usize <= self.keys.len() : ensure we always have enough potential members to sign; if threshold was greater than the number of keys, no one could sign

3. Verification: How do we check that our model is correct?

Now that we have defined the specific instruction and account invariants, we need to generate proof harnesses on which we can run bounded model checking. Our tool does this automagically for anchor-lang programs.

Specifically, for a given Context<T> with incoming accounts of types (init/mut) and outgoing accounts of type (mut/close) we define a pre_condition and post_condition expression that is a conjunction of all of the incoming and outcoming account invariants:

Our instruction invariants are represented as:

  • : succeeds_if
  • : errors_if

And represents whether the instruction actually succeeds (i.e. invoking the handler returned an Ok not an Err).

In order to verify these conditions we need to verify three cases:

1. Account invariants

After we execute an instruction, either the function should error and be reverted () or the account post-invariants should hold (). Furthermore, we can assume that before executing a function, the account pre-invariants () should hold since we will verify all of the functions eventually.

So we are trying to prove that

We can construct a proof harness like the following:

assume(P0)res = instruction_handler(...)assert(!K || P1)

By itself, this harness doesn't actually prove much. For example, if the instruction fails every time, this proof will still work. However, in conjunction with the two subsequent proofs we can be assured that the instruction will actually succeed when we expect it to.

2. Positive instruction invariant

Next we need to prove that success_if is a sufficient condition for instruction success. I.e. .

Just like before we can construct a proof harness:

assume(S)res = instruction_handler(...)assert(K)

This proof assures that whenever succeeds_if is satisfied, the instruction will succeed. However, remember that since this is not a biconditional, the instruction may also succeed even if succeeds_if is not satisfied. To specify explicit error conditions we need our third and final proof.

3. Negative instruction invariant

Finally, we want to prove that errors_if is a sufficient condition for instruction failure. I.e. .

This harness looks just like the previous one:

assume(E)res = instruction_handler(...)assert(!K)

With these three harnesses, we are now able to formally verify that instructions succeed or fail when we expect them to and the account invariants we expect are always being preserved.

4. Case study: Squads Multisig

During our research, we focused on formally verifying aspects of the Squads Multisig Program.

The program defines a Multisig account (Ms) which has multiple members. These members can propose and then vote on transactions to execute on behalf of the multisig. If at least some threshold of members vote yes, the transaction will be invoked. Additionally, there is functionality to add/remove users and update the threshold.

In practice, this structure provides a useful way to distribute authority across a group of individuals. From a formal verification perspective, it has both stateless and stateful features and constraints that provided a good testbed for our tooling.

In this section we will go through a few examples of properties that we can verify on this program:

  1. Incrementally verifying minimum requirements to create a multisig
  2. Verify threshold requirements
  3. Verify requirements to remove a member
  4. Safety guarantees

1. Incrementally verifying minimum requirements to create a multisig

Suppose we want to verify the minimum requirements to create a multisig, i.e. the succeeds_if expression.

Creating a multisig (Ms) requires invoking the create instruction:

#[derive(Accounts)]#[instruction(threshold: u16, create_key: Pubkey, members: Vec<Pubkey>)]pub struct Create<'info> {    #[account(        init,        payer = creator,        space = Ms::SIZE_WITHOUT_MEMBERS + (members.len() * 32),        seeds = [b"squad", create_key.as_ref(), b"multisig"], bump    )]    pub multisig: Account<'info, Ms>,    #[account(mut)]    pub creator: Signer<'info>,    pub system_program: Program<'info, System>,}pub fn create(    ctx: Context<Create>,    threshold: u16,    create_key: Pubkey,    members: Vec<Pubkey>,) -> Result<()> {    // sort the members and remove duplicates    let mut members = members;    members.sort();    members.dedup();    // check we don't exceed u16    let total_members = members.len();    if total_members < 1 {        return err!(MsError::EmptyMembers);    }    // make sure we don't exceed u16 on first call    if total_members > usize::from(u16::MAX) {        return err!(MsError::MaxMembersReached);    }    // make sure threshold is valid    if usize::from(threshold) < 1 || usize::from(threshold) > total_members {        return err!(MsError::InvalidThreshold);    }    ctx.accounts.multisig.init(        threshold,        create_key,        members,        *ctx.bumps.get("multisig").unwrap(),    )}

We can start by testing an empty succeeds_if (this will default to true):

#[succeeds_if()]pub fn create(...) { ... }

Running the solver, we get:

Verification Time: 6.404167s

This means that true does not imply that the function will succeed (which is expected looking at the implementation above).

We can ask the solver to produce a counterexample:

threshold: 33764
create_key: ...
members: SparseVec { size: 19 }

In this case, we can see that the threshold is invalid; it should not be larger than the number of members.

Note also that the verifier decided to use a SparseVec which is one of our custom vec implementations. In this case, the code we are verifying doesn't actually read or write to the vector and so we can model it simply as a symbolic size (with no data).

Using a sparse vec rather than a concrete vec is generally preferred as it speeds up computation and allows us to model arbitrarily sized vecs. push and pop are stubbed out to simply panic for the SparseVec and if this code tried to do that we would fall back to the concrete Vec type.

We can add this to our constraint and try again:

#[succeeds_if(    (threshold as usize) <= members.len())]pub fn create(...) { ... }

Verification failed again! This time we get a different counterexample:

threshold: 0
create_key: ...
members: SparseVec { size: 19 }

Aha! The threshold cannot be 0 either... Let's try again:

#[succeeds_if(    (threshold as usize) <= members.len()    && threshold != 0)]pub fn create(...) { ... }

A third counterexample:

threshold: 4
create_key: ...
members: SparseVec { size: 536870920 }

Here we see the size of our members vec is huge! We need to constrain that to be less than u16::MAX:

#[succeeds_if(    (threshold as usize) <= members.len()    && (threshold != 0)    && (members.len() <= (u16::MAX as usize)))]pub fn create(...) { ... }

And now we get:

Verification Time: 6.6634517s


The attentive reader may have noticed that we didn't need to verify this condition:

if total_members < 1 {    return err!(MsError::EmptyMembers);}

In this case this is actually redundant because if members.len() == 0 then our threshold would also have to be 0 (and our threshold is not allowed to be 0). The solver realizes that this situation is impossible and therefore the expression we have above is sufficient!

2. Verify threshold requirements

A critical security property for multisigs is that the threshold should never be zero (which would let anyone issue transactions) and the threshold should never be greater than the number of members (which would let nobody issue transactions).

Unlike the previous example, we want to verify this in all cases. I.e. any instruction that could mutate the multisig account.

In this case, we want to model this as an invariant on the Ms account struct:

#[account]#[derive(Clone, Debug)]#[invariant(    (self.threshold >= 1)    && (self.threshold as usize <= self.keys.len()))]pub struct Ms {    pub threshold: u16,               // threshold for signatures    pub authority_index: u16,         // index to seed other authorities under this multisig    pub transaction_index: u32,       // look up and seed reference for transactions    pub ms_change_index: u32,         // the last executed/closed transaction    pub bump: u8,                     // bump for the multisig seed    pub create_key: Pubkey,           // random key(or not) used to seed the multisig pda    pub allow_external_execute: bool, // allow non-member keys to execute txs    pub keys: Vec<Pubkey>,            // keys of the members}

Our verification framework will generate an invariant harness for each instruction. Instructions that can potentially modify the Ms object will be checked to ensure that the invariant still holds after modification.

Let's try this on the create instruction that we've already seen:

Verification Time: 6.8006988s

To ensure this is working, we can test by commenting out this check from create:

// if usize::from(threshold) < 1 || usize::from(threshold) > total_members {//     return err!(MsError::InvalidThreshold);// }

And run again:

Verification Time: 8.245743s

We get the following counterexample:

Account {    account: Ms {        threshold: 32768,        authority_index: 1,        transaction_index: 0,        ms_change_index: 0,        bump: 0,        create_key: ...,        allow_external_execute: false,        keys: SparseVec {            size: 5112,        },    },    info: AccountInfo { ... },}

Here we see that the threshold of the newly created Ms account is larger than the number of keys (5112) which breaks our struct invariant.

3. Verify requirements to remove a member

Now that we've seen both succeeds_if and invariant let's take a look at the remove_member function:

#[derive(Accounts, Debug)]pub struct MsAuth<'info> {    #[account(mut)]    multisig: Box<Account<'info, Ms>>,    #[account(        mut,        seeds = [            b"squad",            multisig.create_key.as_ref(),            b"multisig"        ], bump = multisig.bump    )]    pub multisig_auth: Signer<'info>,}pub fn remove_member(ctx: Context<MsAuth>, old_member: Pubkey) -> Result<()> {    // if there is only one key in this multisig, reject the removal    if ctx.accounts.multisig.keys.len() == 1 {        return err!(MsError::CannotRemoveSoloMember);    }    ctx.accounts.multisig.remove_member(old_member)?;    // if the number of keys is now less than the threshold, adjust it    if ctx.accounts.multisig.keys.len() < usize::from(ctx.accounts.multisig.threshold) {        let new_threshold: u16 = ctx.accounts.multisig.keys.len().try_into().unwrap();        ctx.accounts.multisig.change_threshold(new_threshold)?;    }    let new_index = ctx.accounts.multisig.transaction_index;    ctx.accounts.multisig.set_change_index(new_index)}

First let's establish the succeeds_if condition. We can do this either interactively, following counterexamples like in the first example or we can guess what a sufficient condition might be:

#[succeeds_if(    ctx.accounts.multisig.keys.len() > 1)]fn remove_member(...) { ... }

And for now let's remove the invariant on the Ms account:

#[invariant()]pub struct Ms { ... }

Let's test this!

Our succeeds_if harness produces:

Verification Time: 28.119272s

This tells us that if our multisig has at least two keys then the instruction will succeed.

However, remember that since succeeds_if represents just the sufficient conditions, there may be other cases where the function succeeds.

Suppose we want to be sure that this condition is the only condition in which the function will succeed (i.e. "the function will succeed if and only if the multisig has at least two keys"). We could attempt to verify the other side of this with an errors_if macro such as:

#[succeeds_if(    ctx.accounts.multisig.keys.len() > 1)]#[errors_if(    ctx.accounts.multisig.keys.len() <= 1)]fn remove_member(...) { ... }

Let's test this, we just need to run the new errors_if harness:

Verification Time: 31.900913s

Hmm, this verification failed! Let's look at the counterexample. The multisig it is trying to remove a member from looks like:

Account {    account: Ms {        threshold: 0,        authority_index: 0,        transaction_index: 0,        ms_change_index: 0,        bump: 0,        create_key: ...,        allow_external_execute: false,        keys: Vec {            data: ...,            size: 0,        },    },    info: AccountInfo { ... },}

Interestingly, the multisig has 0 keys and yet this instruction does not error. Let's take a closer look to figure out why:

Inside our handler, we see that it only checks if the number of keys exactly equals 1. Otherwise it invokes Ms::remove_member:

if ctx.accounts.multisig.keys.len() == 1 {    return err!(MsError::CannotRemoveSoloMember);}ctx.accounts.multisig.remove_member(old_member)?;

In that function, it checks if the member to remove is contained in that multisig (with Ms::is_member) and if it is not, it simply skips the removal and returns Ok(()).

pub fn remove_member(&mut self, member: Pubkey) -> Result<()> {    if let Some(ind) = self.is_member(member) {        self.keys.remove(ind);        if self.keys.len() < usize::from(self.threshold) {            self.threshold = self.keys.len().try_into().unwrap();        }    }    Ok(())}

Inside Ms::is_member, we see that it performs a binary_search on the keys vec and returns the index or None. Since the vec has size zero, this will just return None.

pub fn is_member(&self, member: Pubkey) -> Option<usize> {    match self.keys.binary_search(&member) {        Ok(ind) => Some(ind),        _ => None,    }}

So interestingly, a keys vec of size 0 is actually a sufficient condition to execute remove_member. However would it ever actually happen? Well we know from before that when we create the multisig, the threshold must be less than or equal to the number of keys and also greater than zero. So in any valid multisig, the number of keys should never be zero.

We can represent this validity with a struct invariant. In fact the invariant we defined earlier will be sufficient:

#[invariant(    (self.threshold >= 1)    && (self.threshold as usize <= self.keys.len()))]pub struct Ms { ... }

The use of a struct invariant allows us to define (and verify) the possible states that an account can be in at the start and end of an instruction. In this case, our struct invariant rules out the case where keys.len() == 0 and allows us to prove the biconditional (keys.len() >= 1) -> (instruction succeeds).

4. Safety guarantees

Formal verification is an awesome technique but it is not perfect. There are situations where things are not possible to formally verify and you need to resort to other methods.

In particular, one of the difficult-to-verify parts of the Squads Multisig program is cross-program-invocation. Specifically, since cross-program-invocation executes foreign code, it is difficult (if not impossible) to verify whether this will succeed or fail.

In the multisig program this happens in the execute_transaction instruction.

So what do you do?

For example, in a worst-case scenario you could imagine a situation like the following:

#[derive(Accounts)]pub MyCtx {    #[account(mut)]    pub my_account: Account<'info, Acc>}#[account]#[invariant(bad == false)]struct Acc {    pub bad: bool}impl Acc {    pub fn put_into_bad_state() {        self.bad = true;    }}// Instruction handler:fn hard_to_verify(ctx: Context<MyCtx>) -> Result<()> {    invoke_signed(...); // Cross-program invocation    let invoke_res = ...; // fetch result of invocation    if invoke_res == 5 {        ctx.my_account.put_into_bad_state(); // corrupt our account    }    Ok(())}

The integrity of the verification framework relies on the fact that the account invariants for the accounts contained in the instruction (in this case my_account) will be maintained as long as the instruction succeeds.

In this case, we can't really verify if the instruction succeeds or not (at least without knowing which program/instruction will be invoked).

However, we can augment our code with additional runtime constraints to ensure that the safety properties are preserved even if formal verification fails.

In this case, we can add runtime assertions that ensure our runtime invariants hold. For example:

...fn hard_to_verify(ctx: Context<MyCtx>) -> Result<()> {    invoke_signed(...); // Cross-program invocation    let invoke_res = ...; // fetch result of invocation    if invoke_res == 5 {        ctx.my_account.put_into_bad_state(); // corrupt our account    }    // Enforce invariants at runtime    assert(ctx.my_account.invariant());    Ok(())}

Here, we explicitly assert that our invariants hold at runtime which allows us to be assured that my_account will not enter a bad state as a result of some unverifiable behavior.

In general techniques like this can be used to tidy up the loose ends that formal verification may struggle with.

5. Challenges of formal verification on Solana

Expensive computation

As we started exploring this project, we were hoping to see it work straight out of the box. Unfortunately, that was not the case. Harkening back to our friend path explosion, it is often the case that bounded model checking just grinds and grinds on the problem and is not able to produce a solution.

In order to make this technique more widely applicable, we've been developing a runtime SDK layer that is more formal verification friendly. Specifically our tool will replace certain built-in SDK functions and structures with less expensive ones in the context of symbolic execution.

For example, when verifying things like the uniqueness of a Pubkey in a Vec, the native program may generate extremely large SMT expressions containing nested 32-byte comparisons and binary searches on a vector.

However, in most cases the properties we are interested in do not require specific search algorithms for the Vec or a 32-byte Pubkey. Instead, our tool can substitute in cheaper types and functions, such as a 4-byte Pubkey struct and a fixed-size, array-backed Vec implementation. These structures are API-compatible with the native SDK and the changes are functionally invisible to the Solana program we are verifying. However, the generated expressions are much simpler and we find that these techniques can greatly accelerate the speed of model-checking.

It is of key importance that these SDK modifications do not introduce any unsoundness into the model-checking process. We are actively exploring how to do this effectively.

Runtime Environment

While these techniques are quite capable of verifying pure-Rust constructs such as the logical flow of the program, use of Rust types, etc... other aspects of the Solana runtime environment are more difficult to verify.

For example, a program may resize accounts to store variable amounts of data. These types of custom serialization algorithms require specialized techniques to verify account invariants. For example, a bug with account serialization could undermine "correct" account logic.

Another example is cross-program invocation (CPI). While account data cannot be changed by other programs, when you invoke other instructions it becomes more difficult to verify instruction invariants. An instruction three levels down could fail and cause the whole transaction to revert.

6. Conclusion

Computer security is far from being a solved problem. Formal verification is a great technique but it is not a magic bullet. While it can help you verify the correctness of your program it won't catch 100% of the bugs. It won't stop you from specifying the wrong invariants or forgetting things, and it can't help you if there is a bug outside of the scope of the model -- for example in the runtime or consensus layer.

Disclaimer out of the way, we believe that formal verification can still be a very useful tool when applied correctly. We've demonstrated that it is possible to automatically prove invariants about Solana programs in a tractable and user-friendly way.

We're excited to keep pushing this research forward and enhance the security of the whole Solana ecosystem. Our tools are still in development but we're interested in working with other teams. If you have a Solana program you want to get formally verified, give us a shout! Fill out this form or email us at contact@osec.io.