Coverage Analysis

Coverage analysis is essential for understanding which parts of your code are exercised during fuzzing. It helps identify fuzzing blockers like magic value checks and tracks the effectiveness of harness improvements over time.

Overview

Code coverage during fuzzing serves two critical purposes:

1. Assessing harness effectiveness: Understand which parts of your application are actually executed by your fuzzing harnesses
2. Tracking fuzzing progress: Monitor how coverage changes when updating harnesses, fuzzers, or the system under test (SUT)

Coverage is a proxy for fuzzer capability and performance. While coverage is not ideal for measuring fuzzer performance in absolute terms, it reliably indicates whether your harness works effectively in a given setup.

Key Concepts

When to Apply

Apply this technique when:

• Starting a new fuzzing campaign to establish a baseline
• Fuzzer appears to plateau without finding new paths
• After harness modifications to verify improvements
• When migrating between different fuzzers
• Identifying areas requiring dictionary entries or seed inputs
• Debugging why certain code paths aren't reached

Skip this technique when:

• Fuzzing campaign is actively finding crashes
• Coverage infrastructure isn't set up yet
• Working with extremely large codebases where full coverage reports are impractical
• Fuzzer's internal coverage metrics are sufficient for your needs

Quick Reference

Ideal Coverage Workflow

The following workflow represents best practices for integrating coverage analysis into your fuzzing campaigns:

[Fuzzing Campaign]
       |
       v
[Generate Corpus]
       |
       v
[Coverage Analysis]
       |
       +---> Coverage Increased? --> Continue fuzzing with larger corpus
       |
       +---> Coverage Decreased? --> Fix harness or investigate SUT changes
       |
       +---> Coverage Plateaued? --> Add dictionary entries or seed inputs

Key principle: Use the corpus generated after each fuzzing campaign to calculate coverage, rather than real-time fuzzer statistics. This approach provides reproducible, comparable measurements across different fuzzing tools.

Step-by-Step

Step 1: Build with Coverage Instrumentation

Choose your instrumentation method based on toolchain:

LLVM/Clang (C/C++):

clang++ -fprofile-instr-generate -fcoverage-mapping \
  -O2 -DNO_MAIN \
  main.cc harness.cc execute-rt.cc -o fuzz_exec

GCC (C/C++):

g++ -ftest-coverage -fprofile-arcs \
  -O2 -DNO_MAIN \
  main.cc harness.cc execute-rt.cc -o fuzz_exec_gcov

Rust:

rustup toolchain install nightly --component llvm-tools-preview
cargo +nightly fuzz coverage fuzz_target_1

Step 2: Create Execution Runtime (C/C++ only)

For C/C++ projects, create a runtime that executes your corpus:

// execute-rt.cc
#include <stdio.h>
#include <stdlib.h>
#include <dirent.h>
#include <stdint.h>

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size);

void load_file_and_test(const char *filename) {
    FILE *file = fopen(filename, "rb");
    if (file == NULL) {
        printf("Failed to open file: %s\n", filename);
        return;
    }

    fseek(file, 0, SEEK_END);
    long filesize = ftell(file);
    rewind(file);

    uint8_t *buffer = (uint8_t*) malloc(filesize);
    if (buffer == NULL) {
        printf("Failed to allocate memory for file: %s\n", filename);
        fclose(file);
        return;
    }

    long read_size = (long) fread(buffer, 1, filesize, file);
    if (read_size != filesize) {
        printf("Failed to read file: %s\n", filename);
        free(buffer);
        fclose(file);
        return;
    }

    LLVMFuzzerTestOneInput(buffer, filesize);

    free(buffer);
    fclose(file);
}

int main(int argc, char **argv) {
    if (argc != 2) {
        printf("Usage: %s <directory>\n", argv[0]);
        return 1;
    }

    DIR *dir = opendir(argv[1]);
    if (dir == NULL) {
        printf("Failed to open directory: %s\n", argv[1]);
        return 1;
    }

    struct dirent *entry;
    while ((entry = readdir(dir)) != NULL) {
        if (entry->d_type == DT_REG) {
            char filepath[1024];
            snprintf(filepath, sizeof(filepath), "%s/%s", argv[1], entry->d_name);
            load_file_and_test(filepath);
        }
    }

    closedir(dir);
    return 0;
}

Step 3: Execute on Corpus

LLVM (C/C++):

LLVM_PROFILE_FILE=fuzz.profraw ./fuzz_exec corpus/

GCC (C/C++):

./fuzz_exec_gcov corpus/

Rust: Coverage data is automatically generated when running cargo fuzz coverage.

Step 4: Process Coverage Data

LLVM:

# Merge raw profile data
llvm-profdata merge -sparse fuzz.profraw -o fuzz.profdata

# Generate text report
llvm-cov report ./fuzz_exec \
  -instr-profile=fuzz.profdata \
  -ignore-filename-regex='harness.cc|execute-rt.cc'

# Generate HTML report
llvm-cov show ./fuzz_exec \
  -instr-profile=fuzz.profdata \
  -ignore-filename-regex='harness.cc|execute-rt.cc' \
  -format=html -output-dir fuzz_html/

GCC with gcovr:

# Install gcovr (via pip for latest version)
python3 -m venv venv
source venv/bin/activate
pip3 install gcovr

# Generate report
gcovr --gcov-executable "llvm-cov gcov" \
  --exclude harness.cc --exclude execute-rt.cc \
  --root . --html-details -o coverage.html

Rust:

# Install required tools
cargo 
how to use coverage-analysis
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use coverage-analysis on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add coverage-analysis
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/trailofbits/skills --skill coverage-analysiscopy
The skills CLI fetches coverage-analysis from GitHub repository trailofbits/skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/coverage-analysis
Reload or restart Cursor to activate coverage-analysis. Access the skill through slash commands (e.g., /coverage-analysis) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?