Secured #6 – Writing Robust C – Best Practices for Finding and Preventing Vulnerabilities

For EIP-4844, Ethereum purchasers want the power to compute and confirm KZG commitments. Somewhat 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 higher-level languages. The concept was to create a sturdy and environment friendly cryptographic library that each one purchasers might use. The Protocol Safety Analysis group on the Ethereum Basis had the chance to evaluate and enhance this library. This weblog put up will talk about some issues we do to make C initiatives safer.

Fuzz

Fuzzing is a dynamic code testing method that includes offering random inputs to find bugs in a program. LibFuzzer and afl++ are two widespread 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 challenge’s different choices.

Here is the fuzzer for verify_kzg_proof, certainly one of 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* knowledge, size_t measurement) {
    initialize();
    if (measurement == INPUT_SIZE) {
        bool okay;
        verify_kzg_proof(
            &okay,
            (const Bytes48 *)(knowledge + COMMITMENT_OFFSET),
            (const Bytes32 *)(knowledge + Z_OFFSET),
            (const Bytes32 *)(knowledge + Y_OFFSET),
            (const Bytes48 *)(knowledge + PROOF_OFFSET),
            &s
        );
    }
    return 0;
}

When executed, that is what the output seems to be like. If there have been an issue, it might 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 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 also you anticipated them to be the identical, you realize one thing is improper. This method could be very widespread in Ethereum as a result of we prefer to have a number of implementations of the identical factor. This diversification offers an additional stage of security, figuring out that if one implementation have been flawed the others might not have the identical challenge.

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 operating the exams. This can be a nice method to confirm code is executed (“coated”) 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 high-level overview of how a lot of every operate is executed. The exported features are on the prime and the non-exported (static) features are on the underside.

There may be lots 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, many of the non-executed code offers with hard-to-test error instances resembling reminiscence allocation failures. For instance, here is some non-executed code:

At the start of this operate, it checks that the trusted setup is sufficiently big to carry out a pairing verify. 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 right trusted setup, the results of is_monomial_form is at all times the identical and would not return the error worth.

Profile

We do not suggest this for all initiatives, however since c-kzg-4844 is a efficiency essential library we predict it is essential to profile its exported features and measure how lengthy they take to execute. This can assist establish inefficiencies which might probably 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 a straightforward instance which profiles my_function. Profiling works by checking which instruction is being executed sometimes. If a operate is quick sufficient, it might not be seen by the profiler. To scale back the possibility of this, you could have to name your operate a number of occasions. On this instance, we name my_function 1000 occasions.

#embody 
int task_a(int n) {
    if (n     return task_a(n - 1) * n;
}

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

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

int predominant(void) {
    ProfilerStart("instance.prof");
    for (int i = 0; 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’s going to write a file to disk with profiling knowledge. You possibly can then use pprof to visualise this knowledge.

Right here is the graph generated from the command above:

Here is a much bigger instance from certainly one of c-kzg-4844’s features. 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.

Reverse

Subsequent, view your binary in a software program reverse engineering (SRE) instrument resembling Ghidra or IDA. These instruments can assist you perceive how high-level constructs are translated into low-level machine code. We expect it helps to evaluate your code this fashion; like how studying a paper in a distinct 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 typically the compiler will optimize out one thing which it deemed pointless. Maintain an eye fixed out for this, one thing like this truly occurred in c-kzg-4844, some of the tests were being optimized out.

If you view a decompiled operate, it won’t have variable names, complicated sorts, or feedback. When compiled, this info is not included within the binary. Will probably be as much as you to reverse engineer this. You may 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 typically superb. It could assist to construct your binary with DWARF debugging info; most SREs can analyze this part to offer higher outcomes.

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

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

Static Evaluation

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

Here is a easy instance which forgets to free arr (and has one other downside however we are going to speak extra about that later). The compiler won’t establish this, even with all warnings enabled as a result of technically that is utterly legitimate code.

#embody 
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 smart if you concentrate on it; the analyzer reached the return assertion and seen that the reminiscence hadn’t been freed.

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

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

Sanitize

Santizers are dynamic evaluation instruments which instrument (add directions) to packages which may 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 here are the 4 we discover most helpful and simple to make use of.

Tackle

AddressSanitizer (ASan) is a quick reminiscence error detector which may 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 factor in a 5 factor array. This can be a easy instance of a heap-buffer-overflow:

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

When compiled with -fsanitize=handle and executed, it’s going to output the next error message. This factors you in an excellent route (a 4-byte write in predominant). This binary could possibly be seen 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 
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. Here is a easy instance which reads (and returns) an uninitialized worth:

int predominant(void) {
    int knowledge[2];
    return knowledge[0];
}

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

Undefined Habits

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.

#embody 
int predominant(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 circumstances are:

Thread

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

#embody 
int counter = 0;

void *increment(void *arg) {
    (void)arg;
    for (int i = 0; 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’s going to output the next error message:

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

Valgrind

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

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 soar or transfer [that] will depend on uninitialized worth(s).”

This identified an edge case in expand_root_of_unity. If the improper root of unity or width have been offered, 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         blst_fr_mul(&out[i], &out[i - 1], root);
    }
    CHECK(fr_is_one(&out[width]));

    return C_KZG_OK;
}

Safety Evaluate

After improvement stabilizes, it has been totally examined, and your group has manually reviewed the codebase themselves a number of occasions, it is time to get a safety evaluate by a good safety group. This would possibly not be a stamp of approval, but it surely exhibits that your challenge is not less than considerably safe. Take into account there is no such thing as a such factor as excellent safety. There’ll at all times be the danger 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 essential 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 challenge could possibly be exploited for positive aspects, like it’s for Ethereum, take into account organising a bug bounty program. This permits safety researchers, or anybody actually, to submit vulnerability stories in change for cash. Usually, that is particularly for findings which may show that an exploit is feasible. If the bug bounty payouts are cheap, bug finders will notify you of the bug reasonably than exploiting it or promoting it to a different social gathering. We suggest beginning your bug bounty program after the findings from the primary safety evaluate are resolved; ideally, the safety evaluate would price lower than the bug bounty payouts.

Conclusion

The event of sturdy C initiatives, particularly within the essential 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 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 initiatives.