agentic-actions-audito…
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI,…
Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an
Claude Code
$ npx -y skills add trailofbits/skills --skill libfuzzer --agent claude-code
Codex
$ npx skills add trailofbits/skills --skill libfuzzer -a codex -y
Cursor
$ npx skills add trailofbits/skills --skill libfuzzer -a cursor -y
Gemini CLI
$ npx skills add trailofbits/skills --skill libfuzzer -a gemini-cli -y
GitHub Copilot
$ npx skills add trailofbits/skills --skill libfuzzer -a github-copilot -y
OpenCode
$ npx skills add trailofbits/skills --skill libfuzzer -a opencode -y
Windsurf
$ npx skills add trailofbits/skills --skill libfuzzer -a windsurf -y
name: libfuzzer type: fuzzer description: "Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an LLVMFuzzerTestOneInput harness, starting fuzzing on a C/C++ library, choosing between libFuzzer and AFL++, or working out why a libFuzzer run finds nothing."
libFuzzer is an in-process, coverage-guided fuzzer that is part of the LLVM project. It's the recommended starting point for fuzzing C/C++ projects due to its simplicity and integration with the LLVM toolchain. While libFuzzer has been in maintenance-only mode since late 2022, it is easier to install and use than its alternatives, has wide support, and will be maintained for the foreseeable future.
| Fuzzer | Best For | Complexity | |--------|----------|------------| | libFuzzer | Quick setup, single-project fuzzing | Low | | AFL++ | Multi-core fuzzing, diverse mutations | Medium | | LibAFL | Custom fuzzers, research projects | High | | Honggfuzz | Hardware-based coverage | Medium |
**Choose libFuzzer when:**
**Note:** Fuzzing harnesses written for libFuzzer are compatible with AFL++, making it easy to transition if you need more advanced features like better multi-core support.
#include <stdint.h>
#include <stddef.h>
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
// Validate input if needed
if (size < 1) return 0;
// Call your target function with fuzzer-provided data
my_target_function(data, size);
return 0;
}Compile and run:
clang++ -fsanitize=fuzzer,address -g -O2 harness.cc target.cc -o fuzz mkdir corpus/ ./fuzz corpus/
apt install clang llvm
For the latest LLVM version:
# Add LLVM repository from apt.llvm.org # Then install specific version, e.g.: apt install clang-18 llvm-18
# Using Homebrew brew install llvm # Or using Nix nix-env -i clang
Install Clang through Visual Studio. Refer to [Microsoft's documentation](https://learn.microsoft.com/en-us/cpp/build/clang-support-msbuild?view=msvc-170) for setup instructions.
**Recommendation:** If possible, fuzz on a local x86_64 VM or rent one on DigitalOcean, AWS, or Hetzner. Linux provides the best support for libFuzzer.
clang++ --version # Should show LLVM version information
The harness is the entry point for the fuzzer. libFuzzer calls the `LLVMFuzzerTestOneInput` function repeatedly with different inputs.
#include <stdint.h>
#include <stddef.h>
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
// 1. Optional: Validate input size
if (size < MIN_REQUIRED_SIZE) {
return 0; // Reject inputs that are too small
}
// 2. Optional: Convert raw bytes to structured data
// Example: Parse two integers from byte array
if (size >= 2 * sizeof(uint32_t)) {
uint32_t a = *(uint32_t*)(data);
uint32_t b = *(uint32_t*)(data + sizeof(uint32_t));
my_function(a, b);
}
// 3. Call target function
target_function(data, size);
// 4. Always return 0 (non-zero reserved for future use)
return 0;
}| Do | Don't | |----|-------| | Handle all input types (empty, huge, malformed) | Call `exit()` - stops fuzzing process | | Join all threads before returning | Leave threads running | | Keep harness fast and simple | Add excessive logging or complexity | | Maintain determinism | Use random number generators or read `/dev/random` | | Reset global state between runs | Rely on state from previous executions | | Use narrow, focused targets | Mix unrelated data formats (PNG + TCP) in one harness |
**Rationale:**
For complex inputs (strings, multiple parameters), use the `FuzzedDataProvider` helper:
#include <stdint.h>
#include <stddef.h>
#include "FuzzedDataProvider.h" // From LLVM project
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
FuzzedDataProvider fuzzed_data(data, size);
// Extract structured data
size_t allocation_size = fuzzed_data.ConsumeIntegral<size_t>();
std::vector<char> str1 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);
std::vector<char> str2 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);
// Call target with extracted data
char* result = concat(&str1[0], str1.size(), &str2[0], str2.size(), allocation_size);
if (result != NULL) {
free(result);
}
return 0;
}Download `FuzzedDataProvider.h` from the [LLVM repository](https://github.com/llvm/llvm-project/blob/main/compiler-rt/include/fuzzer/FuzzedDataProvider.h).
Use a single harness to test multiple related functions:
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
if (size < 1 + 2 * sizeof(int32_t)) {
return 0;
}
uint8_t mode = data[0];
int32_t numbers[2];
memcpy(numbers, data + 1, 2 * sizeof(int32_t));
// Select function based on first byte
switch (mode % 4) {
case 0: add(numbers[0], numbers[1]); break;
case 1: subtraTrail of Bits skills and plugins for AI-assisted security analysis, testing, and development. Install this marketplace in Claude Code or Codex, or import it into your ChatGPT workspace marketplace.
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