fuzzer-generator
Skill17 starsChanged 9 months agoApache-2.0View on GitHub
Creates fuzzing tools that generate random inputs to find bugs and security vulnerabilities.
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The file
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2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
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≈2
Distinctive · estimate
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0
Very specific
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- Common
- Distinctive
- Very specific
- Distinctive: line 41
Use libFuzzer for C/C++, Atheris for Python
- Distinctive: line 101
Approach: Use language models for input generation
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Fuzzer Generator
Creates fuzzing tools that automatically generate random inputs to discover bugs, crashes, and security vulnerabilities in programs.
When to Use This Skill
- Finding crashes and memory safety issues
- Discovering input validation bugs
- Security vulnerability discovery
- Compiler bug finding
What This Skill Does
- Input Generation: Creates random valid inputs for target programs
- Coverage Tracking: Monitors code coverage to guide fuzzing
- Mutation Strategies: Implements mutation and generation-based fuzzing
- Crash Analysis: Triages and minimizes crashing inputs
Key Concepts
| Concept | Description |
| Coverage-guided fuzzing | Uses coverage to prioritize inputs |
| Corpus | Set of interesting inputs |
| Mutation | Small changes to existing inputs |
| Crash minimization | Reducing inputs to minimal forms |
Tips
- Build with AddressSanitizer for memory safety bugs
- Use libFuzzer for C/C++, Atheris for Python
- Maintain corpus of interesting inputs
- Minimize crashes to find root causes
Related Skills
property-based-tester- Structured property testingtaint-analysis- Track untrusted inputsandbox-builder- Safe fuzzing environment
Canonical References
| Reference | Why It Matters |
| libFuzzer: in-process coverage-guided fuzzing | LLVM fuzzing engine |
| AFL: American Fuzzy Lop | Popular greybox fuzzer |
| Finding Bugs in JavaScript Engines | Fuzzing case study |
Tradeoffs and Limitations
| Approach | Pros | Cons |
| Coverage-guided | Efficient, finds deep bugs | Requires instrumentation |
| Blackbox | No source needed | Less effective |
| Grammar-based | Generates valid inputs | Requires grammar |
Assessment Criteria
| Criterion | What to Look For |
| Coverage | Efficient code coverage |
| Bug finding | Discovers real bugs |
| Performance | High tests/second |
Quality Indicators
✅ Good: Good coverage, finds bugs, high throughput ⚠️ Warning: Limited coverage, slow ❌ Bad: No bugs found, crashes
Research Tools & Artifacts
Fuzzing tools and frameworks:
| Tool | Language | What to Learn |
| libFuzzer | C/C++ | In-process coverage-guided |
| AFL++ | C | Greybox fuzzing |
| Honggfuzz | C | Feedback-driven |
| Fuzzilli | JavaScript | JavaScript engine fuzzing |
| Oathbreaker | Python | Grammar-based |
Fuzzing Infrastructure
- OSS-Fuzz - Continuous fuzzing service
- Fuzzbench - Fuzzer evaluation framework
Research Frontiers
1. Fuzzing with LLMs
- Approach: Use language models for input generation
- Papers: "Fuzzing with LLMs" (2023+)
- Tools: ChatGPT-assisted fuzzing
2. Hybrid Fuzzing
- Approach: Combine symbolic execution with fuzzing
- Papers: "Driller" (Shoshitaishvili)
- Tools: QSYM, Angora
Implementation Pitfalls
| Pitfall | Real Consequence | Solution |
| Coverage plateau | Stops finding new paths | Add corpus, mutations |
| Crash duplication | Too many similar crashes | Minimize inputs |
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