For EIP-4844, Ethereum shoppers want the power 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 strong and environment friendly cryptographic library that each one shoppers may use. The Protocol Safety Analysis workforce on the Ethereum Basis had the chance to overview and enhance this library. This weblog submit will talk about 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 in style fuzzing frameworks for C tasks. 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, one among c-kzg-4844’s features:
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 dimension) {
initialize();
if (dimension == 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 like. If there have been an issue, it could 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 method which fuzzes two or extra implementations of the identical interface and compares the outputs. For a given enter, if the output is completely different, and also you anticipated them to be the identical, you recognize one thing is flawed. This system may be very in style in Ethereum as a result of we prefer 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 concern.
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 assessments. 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 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 perform is executed. The exported features are on the prime and the non-exported (static) features are on the underside.
There may be loads 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, check with the HTML file (protection.html) that was generated. This webpage reveals all the supply file and highlights non-executed code in pink. On this challenge’s case, a lot of the non-executed code offers with hard-to-test error instances reminiscent of reminiscence allocation failures. For instance, here is some non-executed code:
Initially of this perform, it checks that the trusted setup is large enough to carry out a pairing examine. There is not a take a look at case which offers 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 at all times the identical and would not return the error worth.
Profile
We do not suggest this for all tasks, however since c-kzg-4844 is a efficiency crucial library we predict it is necessary to profile its exported features and measure how lengthy they take to execute. This may also help determine inefficiencies which may 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 an easy instance which profiles my_function. Profiling works by checking which instruction is being executed sometimes. If a perform is quick sufficient, it will not be seen by the profiler. To cut back the possibility of this, you could have to name your perform a number of occasions. On this instance, we name my_function 1000 occasions.
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 knowledge. You may then use pprof to visualise this knowledge.
Right here is the graph generated from the command above:
Here 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’ll be able to 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 reminiscent of Ghidra or IDA. These instruments may also 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 special font will drive your mind to interpret sentences in a different way. It is also helpful to see what kind of optimizations your compiler makes. It is uncommon, however generally the compiler will optimize out one thing which it deemed pointless. Preserve an eye fixed out for this, one thing like this really occurred in c-kzg-4844, among the assessments have been being optimized out.
If you view a decompiled perform, it is not going to have variable names, complicated sorts, or feedback. When compiled, this info is not included within the binary. It is going to be as much as you to reverse engineer this. You will 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 completely different. These are simply compiler optimizations and are typically fantastic. It might 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 like in Ghidra:
With a little bit work, you’ll be able to 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 determine 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 quite a bit quicker than “dynamic” evaluation instruments which execute code.
Here is a easy instance which forgets to free arr (and has one other drawback however we are going to speak 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.
int predominant(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, nevertheless it is sensible 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 applications 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 straightforward to make use of.
Tackle
AddressSanitizer (ASan) is a quick reminiscence error detector which may determine 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:
int predominant(void) {
int* arr = malloc(5 * sizeof(int));
arr[5] = 42;
return 0;
}
When compiled with -fsanitize=tackle and executed, it would output the next error message. This factors you in a very good path (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:
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 knowledge[2];
return knowledge[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 state of affairs 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.
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 situations are:
Thread
ThreadSanitizer (TSan) detects knowledge races, which may happen in multi-threaded applications when two or extra threads entry a shared reminiscence location on the identical time. This example introduces unpredictability and may result in undefined conduct. Here is an instance through which two threads increment a world counter variable. There are not any locks or semaphores, so it is totally attainable that these two threads will increment the variable on the identical time.
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 knowledge race. In two threads, the increment perform is writing to the identical 4 bytes on the identical time. It even tells us that the reminiscence is counter.
Valgrind
Valgrind is a strong instrumentation framework for constructing dynamic evaluation instruments, however its greatest identified for figuring out reminiscence errors and leaks with its built-in Memcheck device.
The next picture reveals the output from operating c-kzg-4844’s assessments with Valgrind. Within the pink field is a legitimate discovering for a “conditional leap or transfer [that] will depend on uninitialized worth(s).”
This recognized an edge case in expand_root_of_unity. If the flawed root of unity or width have been supplied, it was attainable that the loop will break earlier than out[width] was initialized. On this state of affairs, the ultimate examine would rely upon an uninitialized worth.
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 Overview
After growth stabilizes, it has been totally examined, and your workforce has manually reviewed the codebase themselves a number of occasions, it is time to get a safety overview by a good safety group. This may not be a stamp of approval, nevertheless it reveals that your challenge is at the least considerably safe. Take into accout there isn’t any 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 overview. 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 enables safety researchers, or anybody actually, to submit vulnerability reviews in alternate for cash. Usually, that is particularly for findings which may show that an exploit is feasible. If the bug bounty payouts are affordable, bug finders will notify you of the bug relatively than exploiting it or promoting it to a different occasion. We suggest beginning your bug bounty program after the findings from the primary safety overview are resolved; ideally, the safety overview would price 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 mix 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 helpful insights and greatest practices for others embarking on related tasks.
