For EIP-4844, Ethereum shoppers want the flexibility to compute and confirm KZG commitments. Fairly than every consumer rolling their very own crypto, researchers and builders got here collectively to write 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 shoppers might use. The Protocol Safety Analysis workforce on the Ethereum Basis had the chance to overview and enhance this library. This weblog put up will focus on some issues we do to make C initiatives 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 well-liked fuzzing frameworks for C initiatives. They’re each in-process, coverage-guided, evolutionary fuzzing engines. For c-kzg-4844, we used LibFuzzer since we have been already well-integrated with LLVM undertaking’s different choices.

This is the fuzzer for verify_kzg_proof, one among c-kzg-4844’s features:

#embody "../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 dimension) {
    initialize();
    if (dimension == 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 appears to be like like. If there have been an issue, it will write the enter to disk and cease executing. Ideally, it’s best to have the ability 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 recognize one thing is mistaken. This method could be very well-liked in Ethereum as a result of we prefer to have a number of implementations of the identical factor. This diversification gives an additional degree of security, realizing that if one implementation have been flawed the others could not have the identical difficulty.

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

Protection

Subsequent, we used llvm-profdata and llvm-cov to generate a protection report from operating the checks. This can be a nice solution to confirm code is executed (“coated”) and examined. See the protection goal in c-kzg-4844’s Makefile for an instance of learn how 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 operate is executed. The exported features are on the high and the non-exported (static) features are on the underside.

There’s a variety of inexperienced within the desk above, however there’s 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 reveals your complete supply file and highlights non-executed code in pink. On this undertaking’s case, a lot of the non-executed code offers with hard-to-test error instances comparable to reminiscence allocation failures. For instance, here is some non-executed code:

At first of this operate, it checks that the trusted setup is large enough to carry out a pairing test. 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 right trusted setup, the results of is_monomial_form is all the time the identical and does not return the error worth.

Profile

We do not advocate this for all initiatives, however since c-kzg-4844 is a efficiency crucial library we predict it is vital to profile its exported features and measure how lengthy they take to execute. This will help establish inefficiencies which might doubtlessly DoS nodes. For this, we used gperftools (Google Efficiency Instruments) as an alternative 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 operate is quick sufficient, it is probably not seen by the profiler. To cut back the possibility of this, you could must name your operate a number of instances. On this instance, we name my_function 1000 instances.

#embody <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) {
            task_a(i);
        } else {
            task_b(i);
        }
    }
}

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

Use ProfilerStart(“<filename>”) and ProfilerStop() to mark which elements 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:

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

Reverse

Subsequent, view your binary in a software program reverse engineering (SRE) software comparable to Ghidra or IDA. These instruments will help you perceive how high-level constructs are translated into low-level machine code. We expect it helps to overview your code this fashion; like how studying a paper in a unique font will power your mind to interpret sentences in another way. 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. Preserve a watch out for this, one thing like this really occurred in c-kzg-4844, a few of the checks have been being optimized out.

Whenever you view a decompiled operate, it is not going to have variable names, complicated varieties, or feedback. When compiled, this data is not included within the binary. Will probably be as much as you to reverse engineer this. You will usually see features 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 usually advantageous. 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 appears to be like like in Ghidra:

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

Static Evaluation

Clang comes built-in with the Clang Static Analyzer, which is a superb static evaluation software 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 so much quicker than “dynamic” evaluation instruments which execute code.

This is a easy instance which forgets to free arr (and has one other downside however we are going to 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.

#embody <stdlib.h>

int predominant(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, but it surely is sensible if you consider it; the analyzer reached the return assertion and seen that the reminiscence hadn’t been freed.

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

Given an surprising 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 inconceivable. Good job, Clang Static Analyzer!

Sanitize

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

Deal with

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

#embody <stdlib.h>

int predominant(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 predominant). This binary might be considered in a disassembler to determine precisely which instruction (at predominant+0x84) is inflicting the issue.

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

#embody <stdlib.h>

int predominant(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 predominant+0x8c.

Reminiscence

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

int predominant(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 habits, which refers back to the state of affairs the place a program’s habits is unpredictable and never specified by the langauge commonplace. 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.

#embody <limits.h>

int predominant(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 packages when two or extra threads entry a shared reminiscence location on the identical time. This example introduces unpredictability and might result in undefined habits. This is an instance wherein two threads increment a world counter variable. There are no locks or semaphores, so it is totally doable that these two threads will increment the variable on the identical time.

#embody <pthread.h>

int counter = 0;

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

int predominant(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 operate is writing to the identical 4 bytes on the identical 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 software.

The next picture reveals the output from operating c-kzg-4844’s checks with Valgrind. Within the pink field is a legitimate discovering for a “conditional bounce or transfer [that] depends upon uninitialized worth(s).”

This recognized an edge case in expand_root_of_unity. If the mistaken root of unity or width have been supplied, it was doable that the loop will break earlier than out[width] was initialized. On this state of affairs, the ultimate test 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 Evaluation

After improvement stabilizes, it has been totally examined, and your workforce has manually reviewed the codebase themselves a number of instances, it is time to get a safety overview by a good safety group. This would possibly not be a stamp of approval, but it surely reveals that your undertaking is not less than considerably safe. Take into accout there isn’t any such factor as good 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 overview. They produced this report with 8 findings. It incorporates 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 undertaking might be exploited for good points, like it’s for Ethereum, contemplate establishing a bug bounty program. This enables safety researchers, or anybody actually, to submit vulnerability experiences in alternate for cash. Usually, that is particularly for findings which might show that an exploit is feasible. If the bug bounty payouts are affordable, bug finders will notify you of the bug moderately 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 overview are resolved; ideally, the safety overview would value lower than the bug bounty payouts.

Conclusion

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 finest practices and instruments is crucial 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 related initiatives.