跳到正文
cccskills
免费在 GitHub 公开

coverage-analysis

Measure which code fuzzing reaches as it tries many generated inputs. Use coverage reports to find unreachable areas, compare progress, and guide improvements to test harnesses, starting inputs, or dictionaries of useful input values.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.

查看安装方法

适用场景

  • Establish initial coverage for a fuzzing run
  • Investigate why fuzzing stops reaching new code
  • Compare coverage before and after harness changes
  • Find starting inputs and dictionary entries to add

包含的文件(3)

  • SKILL.md20.5 KB
  • agents/openai.yaml238 B
  • assets/trail-of-bits-mark.svg3.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

ConceptDescription
Coverage instrumentationCompiler flags that track which code paths are executed
Corpus coverageCoverage achieved by running all test cases in a fuzzing corpus
Magic value checksHard-to-discover conditional checks that block fuzzer progress
Coverage-guided fuzzingFuzzing strategy that prioritizes inputs that discover new code paths
Coverage reportVisual or textual representation of executed vs. unexecuted code

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

TaskCommand/Pattern
LLVM coverage instrumentation (C/C++)-fprofile-instr-generate -fcoverage-mapping
GCC coverage instrumentation-ftest-coverage -fprofile-arcs
cargo-fuzz coverage (Rust)cargo +nightly fuzz coverage <target>
Generate LLVM profile datallvm-profdata merge -sparse file.profraw -o file.profdata
LLVM coverage reportllvm-cov report ./binary -instr-profile=file.profdata
LLVM HTML reportllvm-cov show ./binary -instr-profile=file.profdata -format=html -output-dir html/
gcovr HTML reportgcovr --html-details -o coverage.html

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 (uv manages the isolated environment)
uv tool install gcovr
export PATH="$HOME/.local/bin:$PATH"   # uv's tool bin, if not already present

# 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 install cargo-binutils rustfilt

# Create HTML generation script
cat <<'EOF' > ./generate_html
#!/bin/sh
if [ $# -lt 1 ]; then
    echo "Error: Name of fuzz target is required."
    echo "Usage: $0 fuzz_target [sources...]"
    exit 1
fi
FUZZ_TARGET="$1"
shift
SRC_FILTER="$@"
TARGET=$(rustc -vV | sed -n 's|host: ||p')
cargo +nightly cov -- show -Xdemangler=rustfilt \
  "target/$TARGET/coverage/$TARGET/release/$FUZZ_TARGET" \
  -instr-profile="fuzz/coverage/$FUZZ_TARGET/coverage.profdata" \
  -show-line-counts-or-regions -show-instantiations \
  -format=html -o fuzz_html/ $SRC_FILTER
EOF
chmod +x ./generate_html

# Generate HTML report
./generate_html fuzz_target_1 src/lib.rs

Step 5: Analyze Results

Review the coverage report to identify:

  • Uncovered code blocks: Areas that may need better seed inputs or dictionary entries
  • Magic value checks: Conditional statements with hardcoded values that block progress
  • Dead code: Functions that may not be reachable through your harness
  • Coverage changes: Compare against baseline to track improvements or regressions

Common Patterns

Pattern: Identifying Magic Values

Problem: Fuzzer cannot discover paths guarded by magic value checks.

Coverage reveals:

// Coverage shows this block is never executed
if (buf == 0x7F454C46) {  // ELF magic number
    // start parsing buf
}

Solution: Add magic values to dictionary file:

# magic.dict
"\x7F\x45\x4C\x46"

Pattern: Handling Crashing Inputs

Problem: Coverage generation fails when corpus contains crashing inputs.

Before:

./fuzz_exec corpus/  # Crashes on bad input, no coverage generated

After:

// Fork before executing to isolate crashes
int main(int argc, char **argv) {
    // ... directory opening code ...

    while ((entry = readdir(dir)) != NULL) {
        if (entry->d_type == DT_REG) {
            pid_t pid = fork();
            if (pid == 0) {
                // Child process - crash won't affect parent
                char filepath[1024];
                snprintf(filepath, sizeof(filepath), "%s/%s", argv[1], entry->d_name);
                load_file_and_test(filepath);
                exit(0);
            } else {
                // Parent waits for child
                waitpid(pid, NULL, 0);
            }
        }
    }
}

Pattern: CMake Integration

Use Case: Adding coverage builds to CMake projects.

project(FuzzingProject)
cmake_minimum_required(VERSION 3.0)

# Main binary
add_executable(program main.cc)

# Fuzzing binary
add_executable(fuzz main.cc harness.cc)
target_compile_definitions(fuzz PRIVATE NO_MAIN=1)
target_compile_options(fuzz PRIVATE -g -O2 -fsanitize=fuzzer)
target_link_libraries(fuzz -fsanitize=fuzzer)

# Coverage execution binary
add_executable(fuzz_exec main.cc harness.cc execute-rt.cc)
target_compile_definitions(fuzz_exec PRIVATE NO_MAIN)
target_compile_options(fuzz_exec PRIVATE -O2 -fprofile-instr-generate -fcoverage-mapping)
target_link_libraries(fuzz_exec -fprofile-instr-generate)

Build:

cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ .
cmake --build . --target fuzz_exec

Advanced Usage

Tips and Tricks

TipWhy It Helps
Use LLVM 18+ with -show-directory-coverageOrganizes large reports by directory structure instead of flat file list
Export to lcov format for better HTMLllvm-cov export -format=lcov + genhtml provides cleaner per-file reports
Compare coverage across campaignsStore .profdata files with timestamps to track progress over time
Filter harness code from reportsUse -ignore-filename-regex to focus on SUT coverage only
Automate coverage in CI/CDGenerate coverage reports automatically after scheduled fuzzing runs
Use gcovr 5.1+ for Clang 14+Older gcovr versions have compatibility issues with recent LLVM

Incremental Coverage Updates

GCC's gcov instrumentation incrementally updates .gcda files across multiple runs. This is useful for tracking coverage as you add test cases:

# First run
./fuzz_exec_gcov corpus_batch_1/
gcovr --html coverage_v1.html

# Second run (adds to existing coverage)
./fuzz_exec_gcov corpus_batch_2/
gcovr --html coverage_v2.html

# Start fresh
gcovr --delete  # Remove .gcda files
./fuzz_exec_gcov corpus/

Handling Large Codebases

For projects with hundreds of source files:

  1. Filter by prefix: Only generate reports for relevant directories

    llvm-cov show ./fuzz_exec -instr-profile=fuzz.profdata /path/to/src/
    
  2. Use directory coverage: Group by directory to reduce clutter (LLVM 18+)

    llvm-cov show -show-directory-coverage -format=html -output-dir html/
    
  3. Generate JSON for programmatic analysis:

    llvm-cov export -format=lcov > coverage.json
    

Differential Coverage

Compare coverage between two fuzzing campaigns:

# Campaign 1
LLVM_PROFILE_FILE=campaign1.profraw ./fuzz_exec corpus1/
llvm-profdata merge -sparse campaign1.profraw -o campaign1.profdata

# Campaign 2
LLVM_PROFILE_FILE=campaign2.profraw ./fuzz_exec corpus2/
llvm-profdata merge -sparse campaign2.profraw -o campaign2.profdata

# Compare
llvm-cov show ./fuzz_exec \
  -instr-profile=campaign2.profdata \
  -instr-profile=campaign1.profdata \
  -show-line-counts-or-regions

Anti-Patterns

Anti-PatternProblemCorrect Approach
Using fuzzer-reported coverage for comparisonsDifferent fuzzers calculate coverage differently, making cross-tool comparison meaninglessUse dedicated coverage tools (llvm-cov, gcovr) for reproducible measurements
Generating coverage with optimizations-O3 optimizations can eliminate code, making coverage misleadingUse -O2 or -O0 for coverage builds
Not filtering harness codeHarness coverage inflates numbers and obscures SUT coverageUse -ignore-filename-regex or --exclude to filter harness files
Mixing LLVM and GCC instrumentationIncompatible formats cause parsing failuresStick to one toolchain for coverage builds
Ignoring crashing inputsCrashes prevent coverage generation, hiding real coverage dataFix crashes first, or use process forking to isolate them
Not tracking coverage over timeOne-time coverage checks miss regressions and improvementsStore coverage data with timestamps and track trends

Tool-Specific Guidance

libFuzzer

libFuzzer uses LLVM's SanitizerCoverage by default for guiding fuzzing, but you need separate instrumentation for generating reports.

Build for coverage:

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

Execute corpus and generate report:

LLVM_PROFILE_FILE=fuzz.profraw ./fuzz_exec corpus/
llvm-profdata merge -sparse fuzz.profraw -o fuzz.profdata
llvm-cov show ./fuzz_exec -instr-profile=fuzz.profdata -format=html -output-dir html/

Integration tips:

  • Don't use -fsanitize=fuzzer for coverage builds (it conflicts with profile instrumentation)
  • Reuse the same harness function (LLVMFuzzerTestOneInput) with a different main function
  • Use the -ignore-filename-regex flag to exclude harness code from coverage reports
  • Consider using llvm-cov's -show-instantiation flag for template-heavy C++ code

AFL++

AFL++ provides its own coverage feedback mechanism, but for detailed reports use standard LLVM/GCC tools.

Build for coverage with LLVM:

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

Build for coverage with GCC:

AFL_USE_ASAN=0 afl-gcc -ftest-coverage -fprofile-arcs \
  main.cc harness.cc execute-rt.cc -o fuzz_exec_gcov

Execute and generate report:

# LLVM approach
LLVM_PROFILE_FILE=fuzz.profraw ./fuzz_exec afl_output/queue/
llvm-profdata merge -sparse fuzz.profraw -o fuzz.profdata
llvm-cov report ./fuzz_exec -instr-profile=fuzz.profdata

# GCC approach
./fuzz_exec_gcov afl_output/queue/
gcovr --html-details -o coverage.html

Integration tips:

  • Don't use AFL++'s instrumentation (afl-clang-fast) for coverage builds
  • Use standard compilers with coverage flags instead
  • AFL++'s queue/ directory contains your corpus
  • AFL++'s built-in coverage statistics are useful for real-time monitoring but not for detailed analysis

cargo-fuzz (Rust)

cargo-fuzz provides built-in coverage generation using LLVM tools.

Install prerequisites:

rustup toolchain install nightly --component llvm-tools-preview
cargo install cargo-binutils rustfilt

Generate coverage data:

cargo +nightly fuzz coverage fuzz_target_1

Create HTML report script:

cat <<'EOF' > ./generate_html
#!/bin/sh
FUZZ_TARGET="$1"
shift
SRC_FILTER="$@"
TARGET=$(rustc -vV | sed -n 's|host: ||p')
cargo +nightly cov -- show -Xdemangler=rustfilt \
  "target/$TARGET/coverage/$TARGET/release/$FUZZ_TARGET" \
  -instr-profile="fuzz/coverage/$FUZZ_TARGET/coverage.profdata" \
  -show-line-counts-or-regions -show-instantiations \
  -format=html -o fuzz_html/ $SRC_FILTER
EOF
chmod +x ./generate_html

Generate report:

./generate_html fuzz_target_1 src/lib.rs

Integration tips:

  • Always use the nightly toolchain for coverage
  • The -Xdemangler=rustfilt flag makes function names readable
  • Filter by source files (e.g., src/lib.rs) to focus on crate code
  • Use -show-line-counts-or-regions and -show-instantiations for better Rust-specific output
  • Corpus is located in fuzz/corpus/<target>/

honggfuzz

honggfuzz works with standard LLVM/GCC coverage instrumentation.

Build for coverage:

# Use standard compiler, not honggfuzz compiler
clang -fprofile-instr-generate -fcoverage-mapping \
  -O2 harness.c execute-rt.c -o fuzz_exec

Execute corpus:

LLVM_PROFILE_FILE=fuzz.profraw ./fuzz_exec honggfuzz_workspace/

Integration tips:

  • Don't use hfuzz-clang for coverage builds
  • honggfuzz corpus is typically in a workspace directory
  • Use the same LLVM workflow as libFuzzer

Troubleshooting

IssueCauseSolution
error: no profile data availableProfile wasn't generated or wrong pathVerify LLVM_PROFILE_FILE was set and .profraw file exists
Failed to load coverageMismatch between binary and profile dataRebuild binary with same flags used during execution
Coverage reports show 0%Wrong binary used for report generationUse the instrumented binary, not the fuzzing binary
no_working_dir_found error (gcovr).gcda files in unexpected locationAdd --gcov-ignore-errors=no_working_dir_found flag
Crashes prevent coverage generationCorpus contains crashing inputsFilter crashes or use forking approach to isolate failures
Coverage decreases after harness changeHarness now skips certain code pathsReview harness logic; may need to support more input formats
HTML report is flat file listUsing older LLVM versionUpgrade to LLVM 18+ and use -show-directory-coverage
incompatible instrumentationMixing LLVM and GCC coverageRebuild everything with same toolchain

Related Skills

Tools That Use This Technique

SkillHow It Applies
libfuzzerUses SanitizerCoverage for feedback; coverage analysis evaluates harness effectiveness
aflppUses edge coverage for feedback; detailed analysis requires separate instrumentation
cargo-fuzzBuilt-in cargo fuzz coverage command for Rust projects
honggfuzzUses edge coverage; analyze with standard LLVM/GCC tools

Related Techniques

SkillRelationship
fuzz-harness-writingCoverage reveals which code paths harness reaches; guides harness improvements
fuzzing-dictionariesCoverage identifies magic value checks that need dictionary entries
corpus-managementCoverage analysis helps curate corpora by identifying redundant test cases
sanitizersCoverage helps verify sanitizer-instrumented code is actually executed

Resources

Key External Resources

LLVM Source-Based Code Coverage Comprehensive guide to LLVM's profile instrumentation, including advanced features like branch coverage, region coverage, and integration with existing build systems. Covers compiler flags, runtime behavior, and profile data formats.

llvm-cov Command Guide Detailed CLI reference for llvm-cov commands including show, report, and export. Documents all filtering options, output formats, and integration with llvm-profdata.

gcovr Documentation Complete guide to gcovr tool for generating coverage reports from gcov data. Covers HTML themes, filtering options, multi-directory projects, and CI/CD integration patterns.

SanitizerCoverage Documentation Low-level documentation for LLVM's SanitizerCoverage instrumentation. Explains inline 8-bit counters, PC tables, and how fuzzers use coverage feedback for guidance.

On the Evaluation of Fuzzer Performance Research paper examining limitations of coverage as a fuzzing performance metric. Argues for more nuanced evaluation methods beyond simple code coverage percentages.

Video Resources

Not applicable - coverage analysis is primarily a tooling and workflow topic best learned through documentation and hands-on practice.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.

中文简介正在准备中。当前显示英语简介。

  • Detect memory errors while fuzzing C/C++
  • Test unsafe Rust and calls to external libraries
  • Interpret an ASan crash report
trailofbits/skills76372026年10月16日 更新

aflpp

免费

Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.

中文简介正在准备中。当前显示英语简介。

  • Running C/C++ fuzzing across multiple CPU cores
  • Exploring further after libFuzzer progress stalls
  • Monitoring fuzzing progress and stability
trailofbits/skills76372026年10月16日 更新

Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.

中文简介正在准备中。当前显示英语简介。

  • Reviewing workflows that run AI coding agents
  • Tracing issue and PR content into agent prompts
  • Checking sandbox settings and user allowlists
trailofbits/skills76372026年10月16日 更新

Reviews Algorand smart contracts written in TEAL or PyTeal for 11 vulnerability patterns. Produces a complete coverage table and findings with code references, severity ratings, and suggested fixes, with optional automated checks using Tealer.

中文简介正在准备中。当前显示英语简介。

  • Security checks before an Algorand contract audit
  • Reviewing TEAL or PyTeal transaction validation
  • Verifying fixes for reported vulnerabilities
trailofbits/skills76372026年10月16日 更新

atheris

免费

Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.

中文简介正在准备中。当前显示英语简介。

  • Fuzzing a Python package or parser
  • Investigating memory corruption in Python C extensions
  • Turning raw bytes into typed test arguments
trailofbits/skills76372026年10月16日 更新

Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.

中文简介正在准备中。当前显示英语简介。

  • Connecting Semgrep or CodeQL results to code structure
  • Combining human audit notes with automated findings
  • Prioritizing findings using input flow and impact
trailofbits/skills76372026年10月16日 更新

查看 trailofbits 的全部技能

报告此技能的问题