Writing Robust C – Best Practices for Finding and Preventing Vulnerabilities

For EIP-4844, Ethereum shoppers want the power to compute and confirm KZG commitments. Reasonably than every shopper rolling their very own crypto, researchers and builders got here collectively to put in writing c-kzg-4844, a comparatively small C library with bindings for increased-degree languages. The concept was to create a sturdy and environment friendly cryptographic library that each one shoppers might use. The Protocol Safety Analysis staff on the Ethereum Basis had the chance to evaluate and enhance this library. This weblog put up will focus on some issues we do to make C initiatives safer.


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 initiatives. They’re each in-course of, protection-guided, evolutionary fuzzing engines. For c-kzg-4844, we used LibFuzzer since we have been already properly-built-in with LLVM challenge’s different choices.

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

#embrace "../base_fuzz.h"

static const size_t COMMITMENT_OFFSET = 0;

int LLVMFuzzerTestOneInput(const uint8_t* information, size_t dimension) {
    if (dimension == INPUT_SIZE) {
        bool okay;
            (const Bytes48 *)(information + COMMITMENT_OFFSET),
            (const Bytes32 *)(information + Z_OFFSET),
            (const Bytes32 *)(information + Y_OFFSET),
            (const Bytes48 *)(information + PROOF_OFFSET),
    return 0;

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

There’s additionally differential fuzzing, which is a method 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 you anticipated them to be the identical, you understand one thing is mistaken. This system could be very fashionable in Ethereum as a result of we wish to have a number of implementations of the identical factor. This diversification offers an additional degree of security, realizing that if one implementation have been flawed the others could not have the identical subject.

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


Subsequent, we used llvm-profdata and llvm-cov to generate a protection report from operating the exams. It is a nice strategy to confirm code is executed (“covered”) and examined. See the coverage goal in c-kzg-4844’s Makefile for an instance of how you can generate this report.

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

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

Originally of this operate, it checks that the trusted setup is large enough to carry out a pairing examine. There is not a check case which offers an invalid trusted setup, so this does not get executed. Additionally, as a result of we solely check with the proper trusted setup, the results of is_monomial_form is at all times the identical and would not return the error worth.


We do not advocate this for all initiatives, however since c-kzg-4844 is a efficiency crucial library we predict it is essential to profile its exported capabilities and measure how lengthy they take to execute. This might help determine inefficiencies which might doubtlessly 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 function-wealthy and simpler to make use of.

The next is a straightforward instance which profiles my_function. Profiling works by checking which instruction is being executed on occasion. If a operate is quick sufficient, it is probably not observed by the profiler. To scale back the prospect of this, chances are you’ll have to name your operate a number of instances. On this instance, we name my_function 1000 instances.

#embrace <gperftools/profiler.h>

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) {
        } else {

int most important(void) {
    for (int i = 0; i < 1000; i++) {
    return 0;

Use ProfilerStart(“<filename>”) and ProfilerStop() to mark which components of your program to profile. When re-compiled and executed, it’s going to write a file to disk with profiling information. You’ll be able to then use pprof to visualise this information.

Right here is the graph generated from the command above:

This is a much bigger instance from one in every of c-kzg-4844’s capabilities. The next picture is the profiling graph for compute_blob_kzg_proof. As you possibly can see, 80% of this operate’s time is spent performing Montgomery multiplications. That is anticipated.


Subsequent, view your binary in a software program reverse engineering (SRE) device similar to Ghidra or IDA. These instruments might help you perceive how excessive-degree constructs are translated into low-degree machine code. We expect it helps to evaluate your code this fashion; like how studying a paper in a special font will pressure your mind to interpret sentences in a different way. It is also helpful to see what sort of optimizations your compiler makes. It is uncommon, however generally the compiler will optimize out one thing which it deemed pointless. Preserve a watch out for this, one thing like this truly occurred in c-kzg-4844, some of the tests were being optimized out.

Once you view a decompiled operate, it is not going to have variable names, advanced varieties, or feedback. When compiled, this info is not included within the binary. It will likely be as much as you to reverse engineer this. You will typically see capabilities are inlined right into a single operate, 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 typically nice. It might assist to construct your binary with DWARF debugging info; most SREs can analyze this part to supply higher outcomes.

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

With a bit work, you possibly can rename variables and add feedback to make it simpler to learn. This is what it might appear like after a couple of minutes:

Static Evaluation

Clang comes constructed-in with the Clang Static Analyzer, which is a wonderful static evaluation device that may determine 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 loads quicker than “dynamic” evaluation instruments which execute code.

This 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 determine this, even with all warnings enabled as a result of technically that is utterly legitimate code.

#embrace <stdlib.h>

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

The unix.Malloc checker will determine 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 observed that the reminiscence hadn’t been freed.

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

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


Santizers are dynamic evaluation instruments which instrument (add directions) to packages which might level out points throughout execution. These are significantly helpful at discovering widespread errors related to reminiscence dealing with. Clang comes constructed-in with a number of sanitizers; listed here are the 4 we discover most helpful and simple to make use of.


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

Right here is similar instance from earlier. It forgets to free arr and it’s going to set the sixth aspect in a 5 aspect array. It is a easy instance of a heap-buffer-overflow:

#embrace <stdlib.h>

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

When compiled with -fsanitize=tackle and executed, it’s going to output the next error message. This factors you in route (a 4-byte write in most important). This binary might be considered in a disassembler to determine precisely which instruction (at most important+0x84) is inflicting the issue.

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

#embrace <stdlib.h>

int most important(void) {
    int *arr = malloc(5 * sizeof(int));
    return arr[2];

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


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

int most important(void) {
    int information[2];
    return information[0];

When compiled with -fsanitize=reminiscence and executed, it’s going to output the next error message:

Undefined Conduct

UndefinedBehaviorSanitizer (UBSan) detects undefined habits, which refers back to the scenario the place a program’s habits is unpredictable and not specified by the langauge normal. Some widespread 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 habits.

#embrace <limits.h>

int most important(void) {
    int a = INT_MAX;
    return a + 1;

When compiled with -fsanitize=undefined and executed, it’s going to output the next error message which tells us precisely the place the issue is and what the situations are:


ThreadSanitizer (TSan) detects information races, which might happen in multi-threaded packages when two or extra threads entry a shared reminiscence location on the identical time. This example introduces unpredictability and can result in undefined habits. This is an instance during which two threads increment a world counter variable. There are no locks or semaphores, so it is solely doable that these two threads will increment the variable on the identical time.

#embrace <pthread.h>

int counter = 0;

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

int most important(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’s going to output the next error message:

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


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

The next picture exhibits the output from operating c-kzg-4844’s exams with Valgrind. Within the pink field is a sound discovering for a “conditional jump or move [that] depends on uninitialized value(s).”

This identified an edge case in expand_root_of_unity. If the mistaken root of unity or width have been offered, it was doable that the loop will break earlier than out[width] was initialized. On this scenario, the ultimate examine would depend upon 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);

    return C_KZG_OK;

Safety Assessment

After improvement stabilizes, it has been completely examined, and your staff has manually reviewed the codebase themselves a number of instances, it is time to get a safety evaluate by a good safety group. This would possibly not be a stamp of approval, however it exhibits that your challenge is not less than considerably safe. Take note there isn’t a such factor as excellent safety. There’ll at all times be the chance of vulnerabilities.

For c-kzg-4844 and go-kzg-4844, the Ethereum Basis contracted Sigma Prime to conduct a safety evaluate. They produced this report with 8 findings. It comprises one crucial vulnerability in go-kzg-4844 that was a extremely 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 mounted, this might have resulted in a consensus bug (a disagreement between implementations) in Ethereum.

Bug Bounty

If a vulnerability in your challenge might be exploited for beneficial properties, like it’s for Ethereum, think about establishing a bug bounty program. This permits safety researchers, or anybody actually, to submit vulnerability experiences in alternate 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 relatively than exploiting it or promoting it to a different get together. We advocate beginning your bug bounty program after the findings from the primary safety evaluate are resolved; ideally, the safety evaluate would value lower than the bug bounty payouts.


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

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