For EIP-4844, Ethereum purchasers want the flexibility to compute and confirm KZG commitments. Moderately than every consumer rolling their very own crypto, researchers and builders got here collectively to jot down c-kzg-4844, a comparatively small C library with bindings for higher-level languages. The concept was to create a sturdy and environment friendly cryptographic library that each one purchasers may use. The Protocol Safety Analysis staff on the Ethereum Basis had the chance to evaluation and enhance this library. This weblog publish will focus on some issues we do to make C tasks safer.

Fuzz

Fuzzing is a dynamic code testing method that entails offering random inputs to find bugs in a program. LibFuzzer and afl++ are two fashionable fuzzing frameworks for C tasks. They’re each in-process, coverage-guided, evolutionary fuzzing engines. For c-kzg-4844, we used LibFuzzer since we had been already well-integrated with LLVM mission’s different choices.

Here is the fuzzer for verify_kzg_proof, one in every of c-kzg-4844’s features:

#embrace "../base_fuzz.h"

static const size_t COMMITMENT_OFFSET = 0;
static const size_t Z_OFFSET = COMMITMENT_OFFSET + BYTES_PER_COMMITMENT;
static const size_t Y_OFFSET = Z_OFFSET + BYTES_PER_FIELD_ELEMENT;
static const size_t PROOF_OFFSET = Y_OFFSET + BYTES_PER_FIELD_ELEMENT;
static const size_t INPUT_SIZE = PROOF_OFFSET + BYTES_PER_PROOF;

int LLVMFuzzerTestOneInput(const uint8_t* information, size_t measurement) {
    initialize();
    if (measurement == INPUT_SIZE) {
        bool okay;
        verify_kzg_proof(
            &okay,
            (const Bytes48 *)(information + COMMITMENT_OFFSET),
            (const Bytes32 *)(information + Z_OFFSET),
            (const Bytes32 *)(information + Y_OFFSET),
            (const Bytes48 *)(information + PROOF_OFFSET),
            &s
        );
    }
    return 0;
}

When executed, that is what the output seems to be like. If there have been an issue, it will write the enter to disk and cease executing. Ideally, it is best to be capable to reproduce the issue.

There’s additionally differential fuzzing, which is a way which fuzzes two or extra implementations of the identical interface and compares the outputs. For a given enter, if the output is totally different, and also you anticipated them to be the identical, you already know one thing is flawed. This system could be very fashionable in Ethereum as a result of we prefer to have a number of implementations of the identical factor. This diversification gives an additional stage of security, understanding that if one implementation had been flawed the others could not have the identical difficulty.

For KZG libraries, we developed kzg-fuzz which differentially fuzzes c-kzg-4844 (by its Golang bindings) and go-kzg-4844. Up to now, there have not been any variations.

Protection

Subsequent, we used llvm-profdata and llvm-cov to generate a protection report from working the exams. This can be a nice method to confirm code is executed (“coated”) and examined. See the protection goal in c-kzg-4844’s Makefile for an instance of easy methods to generate this report.

When this goal is run (i.e., make protection) it produces a desk that serves as a high-level overview of how a lot of every perform is executed. The exported features are on the prime and the non-exported (static) features are on the underside.

There’s plenty of inexperienced within the desk above, however there may be some yellow and crimson too. To find out what’s and is not being executed, discuss with the HTML file (protection.html) that was generated. This webpage reveals your complete supply file and highlights non-executed code in crimson. On this mission’s case, a lot of the non-executed code offers with hard-to-test error instances similar to reminiscence allocation failures. For instance, here is some non-executed code:

In the beginning of this perform, it checks that the trusted setup is sufficiently big to carry out a pairing verify. There is not a take a look at case which gives an invalid trusted setup, so this does not get executed. Additionally, as a result of we solely take a look at with the proper trusted setup, the results of is_monomial_form is all the time the identical and would not return the error worth.

Profile

We do not advocate this for all tasks, however since c-kzg-4844 is a efficiency crucial library we expect it is vital to profile its exported features and measure how lengthy they take to execute. This will help establish inefficiencies which may probably DoS nodes. For this, we used gperftools (Google Efficiency Instruments) as a substitute of llvm-xray as a result of we discovered it to be extra feature-rich and simpler to make use of.

The next is an easy instance which profiles my_function. Profiling works by checking which instruction is being executed every now and then. If a perform is quick sufficient, it might not be seen by the profiler. To scale back the possibility of this, you might have to name your perform a number of occasions. On this instance, we name my_function 1000 occasions.

#embrace 

int task_a(int n) {
    if (n <= 1) return 1;
    return task_a(n - 1) * n;
}

int task_b(int n) {
    if (n <= 1) return 1;
    return task_b(n - 2) + n;
}

void my_function(void) {
    for (int i = 0; i < 500; i++) {
        if (i % 2 == 0) {
            task_a(i);
        } else {
            task_b(i);
        }
    }
}

int major(void) {
    ProfilerStart("instance.prof");
    for (int i = 0; i < 1000; i++) {
        my_function();
    }
    ProfilerStop();
    return 0;
}

Use ProfilerStart(““) and ProfilerStop() to mark which components of your program to profile. When re-compiled and executed, it would write a file to disk with profiling information. You possibly can then use pprof to visualise this information.

Right here is the graph generated from the command above:

Here is an even bigger instance from one in every of c-kzg-4844’s features. The next picture is the profiling graph for compute_blob_kzg_proof. As you may see, 80% of this perform’s time is spent performing Montgomery multiplications. That is anticipated.

Reverse

Subsequent, view your binary in a software program reverse engineering (SRE) device similar to Ghidra or IDA. These instruments will help you perceive how high-level constructs are translated into low-level machine code. We predict it helps to evaluation your code this fashion; like how studying a paper in a special font will power your mind to interpret sentences otherwise. It is also helpful to see what sort of optimizations your compiler makes. It is uncommon, however typically the compiler will optimize out one thing which it deemed pointless. Hold an eye fixed out for this, one thing like this truly occurred in c-kzg-4844, among the exams had been being optimized out.

While you view a decompiled perform, it is not going to have variable names, advanced varieties, or feedback. When compiled, this data is not included within the binary. It will likely be as much as you to reverse engineer this. You may typically see features are inlined right into a single perform, a number of variables declared in code are optimized right into a single buffer, and the order of checks are totally different. These are simply compiler optimizations and are usually high-quality. It might assist to construct your binary with DWARF debugging data; most SREs can analyze this part to supply higher outcomes.

For instance, that is what blob_to_kzg_commitment initially seems to be like in Ghidra:

With just a little work, you may rename variables and add feedback to make it simpler to learn. Here is what it may appear to be after a couple of minutes:

Static Evaluation

Clang comes built-in with the Clang Static Analyzer, which is a wonderful static evaluation device that may establish many issues that the compiler will miss. Because the identify “static” suggests, it examines code with out executing it. That is slower than the compiler, however quite a bit sooner than “dynamic” evaluation instruments which execute code.

Here is a easy instance which forgets to free arr (and has one other drawback however we’ll discuss extra about that later). The compiler is not going to establish this, even with all warnings enabled as a result of technically that is fully legitimate code.

#embrace 

int major(void) {
    int* arr = malloc(5 * sizeof(int));
    arr[5] = 42;
    return 0;
}

The unix.Malloc checker will establish that arr wasn’t freed. The road within the warning message is a bit deceptive, however it is smart if you consider it; the analyzer reached the return assertion and seen that the reminiscence hadn’t been freed.

Not all the findings are that easy although. Here is a discovering that Clang Static Analyzer present in c-kzg-4844 when initially launched to the mission:

Given an sudden enter, it was attainable to shift this worth by 32 bits which is undefined conduct. The answer was to limit the enter with CHECK(log2_pow2(n) != 0) in order that this was inconceivable. Good job, Clang Static Analyzer!

Sanitize

Santizers are dynamic evaluation instruments which instrument (add directions) to applications which might level out points throughout execution. These are notably helpful at discovering frequent errors related to reminiscence dealing with. Clang comes built-in with a number of sanitizers; listed below are the 4 we discover most helpful and straightforward to make use of.

Handle

AddressSanitizer (ASan) is a quick reminiscence error detector which might establish out-of-bounds accesses, use-after-free, use-after-return, use-after-scope, double-free, and reminiscence leaks.

Right here is identical instance from earlier. It forgets to free arr and it’ll set the sixth ingredient in a 5 ingredient array. This can be a easy instance of a heap-buffer-overflow:

#embrace 

int major(void) {
    int* arr = malloc(5 * sizeof(int));
    arr[5] = 42;
    return 0;
}

When compiled with -fsanitize=deal with and executed, it would output the next error message. This factors you in a superb course (a 4-byte write in major). This binary might be considered in a disassembler to determine precisely which instruction (at major+0x84) is inflicting the issue.

Equally, here is an instance the place it finds a heap-use-after-free:

#embrace 

int major(void) {
    int *arr = malloc(5 * sizeof(int));
    free(arr);
    return arr[2];
}

It tells you that there is a 4-byte learn of freed reminiscence at major+0x8c.

Reminiscence

MemorySanitizer (MSan) is a detector of uninitialized reads. Here is a easy instance which reads (and returns) an uninitialized worth:

int major(void) {
    int information[2];
    return information[0];
}

When compiled with -fsanitize=reminiscence and executed, it would output the next error message:

Undefined Conduct

UndefinedBehaviorSanitizer (UBSan) detects undefined conduct, which refers back to the scenario the place a program’s conduct is unpredictable and never specified by the langauge normal. Some frequent examples of this are accessing out-of-bounds reminiscence, dereferencing an invalid pointer, studying uninitialized variables, and overflow of a signed integer. For instance, right here we increment INT_MAX which is undefined conduct.

#embrace 

int major(void) {
    int a = INT_MAX;
    return a + 1;
}

When compiled with -fsanitize=undefined and executed, it would output the next error message which tells us precisely the place the issue is and what the circumstances are:

Thread

ThreadSanitizer (TSan) detects information races, which might happen in multi-threaded applications when two or extra threads entry a shared reminiscence location on the similar time. This case introduces unpredictability and might result in undefined conduct. Here is an instance by which two threads increment a world counter variable. There are no locks or semaphores, so it is totally attainable that these two threads will increment the variable on the similar time.

#embrace 

int counter = 0;

void *increment(void *arg) {
    (void)arg;
    for (int i = 0; i < 1000000; i++)
        counter++;
    return NULL;
}

int major(void) {
    pthread_t thread1, thread2;
    pthread_create(&thread1, NULL, increment, NULL);
    pthread_create(&thread2, NULL, increment, NULL);
    pthread_join(thread1, NULL);
    pthread_join(thread2, NULL);
    return 0;
}

When compiled with -fsanitize=thread and executed, it would output the next error message:

This error message tells us that there is a information race. In two threads, the increment perform is writing to the identical 4 bytes on the similar time. It even tells us that the reminiscence is counter.

Valgrind

Valgrind is a robust instrumentation framework for constructing dynamic evaluation instruments, however its finest recognized for figuring out reminiscence errors and leaks with its built-in Memcheck device.

The next picture reveals the output from working c-kzg-4844’s exams with Valgrind. Within the crimson field is a sound discovering for a “conditional soar or transfer [that] relies on uninitialized worth(s).”

This recognized an edge case in expand_root_of_unity. If the flawed root of unity or width had been supplied, it was attainable that the loop will break earlier than out[width] was initialized. On this scenario, the ultimate verify would rely on an uninitialized worth.

static C_KZG_RET expand_root_of_unity(
    fr_t *out, const fr_t *root, uint64_t width
) {
    out[0] = FR_ONE;
    out[1] = *root;

    for (uint64_t i = 2; !fr_is_one(&out[i - 1]); i++) {
        CHECK(i <= width);
        blst_fr_mul(&out[i], &out[i - 1], root);
    }
    CHECK(fr_is_one(&out[width]));

    return C_KZG_OK;
}

Safety Evaluate

After growth stabilizes, it has been totally examined, and your staff has manually reviewed the codebase themselves a number of occasions, it is time to get a safety evaluation by a good safety group. This would possibly not be a stamp of approval, however it reveals that your mission is not less than considerably safe. Remember there isn’t a such factor as excellent safety. There’ll all the time be the danger of vulnerabilities.

For c-kzg-4844 and go-kzg-4844, the Ethereum Basis contracted Sigma Prime to conduct a safety evaluation. They produced this report with 8 findings. It accommodates one crucial vulnerability in go-kzg-4844 that was a very good discover. The BLS12-381 library that go-kzg-4844 makes use of, gnark-crypto, had a bug which allowed invalid G1 and G2 factors to be sucessfully decoded. Had this not been fastened, this might have resulted in a consensus bug (a disagreement between implementations) in Ethereum.

Bug Bounty

If a vulnerability in your mission might be exploited for beneficial properties, like it’s for Ethereum, contemplate establishing a bug bounty program. This enables safety researchers, or anybody actually, to submit vulnerability reviews in change for cash. Typically, that is particularly for findings which might show that an exploit is feasible. If the bug bounty payouts are cheap, bug finders will notify you of the bug quite than exploiting it or promoting it to a different social gathering. We advocate beginning your bug bounty program after the findings from the primary safety evaluation are resolved; ideally, the safety evaluation would value lower than the bug bounty payouts.

Conclusion

The event of strong C tasks, particularly within the crucial area of blockchain and cryptocurrencies, requires a multi-faceted strategy. Given the inherent vulnerabilities related to the C language, a mixture of finest practices and instruments is important for producing resilient software program. We hope our experiences and findings from our work with c-kzg-4844 present helpful insights and finest practices for others embarking on comparable tasks.