Fuzzing Dictionary
A fuzzing dictionary provides domain-specific tokens to guide the fuzzer toward interesting inputs. Instead of purely random mutations, the fuzzer incorporates known keywords, magic numbers, protocol commands, and format-specific strings that are more likely to reach deeper code paths in parsers, protocol handlers, and file format processors.
Best use case
Fuzzing Dictionary is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
A fuzzing dictionary provides domain-specific tokens to guide the fuzzer toward interesting inputs. Instead of purely random mutations, the fuzzer incorporates known keywords, magic numbers, protocol commands, and format-specific strings that are more likely to reach deeper code paths in parsers, protocol handlers, and file format processors.
Teams using Fuzzing Dictionary should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fuzzing-dictionary/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Fuzzing Dictionary Compares
| Feature / Agent | Fuzzing Dictionary | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
A fuzzing dictionary provides domain-specific tokens to guide the fuzzer toward interesting inputs. Instead of purely random mutations, the fuzzer incorporates known keywords, magic numbers, protocol commands, and format-specific strings that are more likely to reach deeper code paths in parsers, protocol handlers, and file format processors.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Fuzzing Dictionary
A fuzzing dictionary provides domain-specific tokens to guide the fuzzer toward interesting inputs. Instead of purely random mutations, the fuzzer incorporates known keywords, magic numbers, protocol commands, and format-specific strings that are more likely to reach deeper code paths in parsers, protocol handlers, and file format processors.
## Overview
Dictionaries are text files containing quoted strings that represent meaningful tokens for your target. They help fuzzers bypass early validation checks and explore code paths that would be difficult to reach through blind mutation alone.
### Key Concepts
| Concept | Description |
|---------|-------------|
| **Dictionary Entry** | A quoted string (e.g., `"keyword"`) or key-value pair (e.g., `kw="value"`) |
| **Hex Escapes** | Byte sequences like `"\xF7\xF8"` for non-printable characters |
| **Token Injection** | Fuzzer inserts dictionary entries into generated inputs |
| **Cross-Fuzzer Format** | Dictionary files work with libFuzzer, AFL++, and cargo-fuzz |
## When to Apply
**Apply this technique when:**
- Fuzzing parsers (JSON, XML, config files)
- Fuzzing protocol implementations (HTTP, DNS, custom protocols)
- Fuzzing file format handlers (PNG, PDF, media codecs)
- Coverage plateaus early without reaching deeper logic
- Target code checks for specific keywords or magic values
**Skip this technique when:**
- Fuzzing pure algorithms without format expectations
- Target has no keyword-based parsing
- Corpus already achieves high coverage
## Quick Reference
| Task | Command/Pattern |
|------|-----------------|
| Use with libFuzzer | `./fuzz -dict=./dictionary.dict ...` |
| Use with AFL++ | `afl-fuzz -x ./dictionary.dict ...` |
| Use with cargo-fuzz | `cargo fuzz run fuzz_target -- -dict=./dictionary.dict` |
| Extract from header | `grep -o '".*"' header.h > header.dict` |
| Generate from binary | `strings ./binary \| sed 's/^/"&/; s/$/&"/' > strings.dict` |
## Step-by-Step
### Step 1: Create Dictionary File
Create a text file with quoted strings on each line. Use comments (`#`) for documentation.
**Example dictionary format:**
```conf
# Lines starting with '#' and empty lines are ignored.
# Adds "blah" (w/o quotes) to the dictionary.
kw1="blah"
# Use \\ for backslash and \" for quotes.
kw2="\"ac\\dc\""
# Use \xAB for hex values
kw3="\xF7\xF8"
# the name of the keyword followed by '=' may be omitted:
"foo\x0Abar"
```
### Step 2: Generate Dictionary Content
Choose a generation method based on what's available:
**From LLM:** Prompt ChatGPT or Claude with:
```text
A dictionary can be used to guide the fuzzer. Write me a dictionary file for fuzzing a <PNG parser>. Each line should be a quoted string or key-value pair like kw="value". Include magic bytes, chunk types, and common header values. Use hex escapes like "\xF7\xF8" for binary values.
```
**From header files:**
```bash
grep -o '".*"' header.h > header.dict
```
**From man pages (for CLI tools):**
```bash
man curl | grep -oP '^\s*(--|-)\K\S+' | sed 's/[,.]$//' | sed 's/^/"&/; s/$/&"/' | sort -u > man.dict
```
**From binary strings:**
```bash
strings ./binary | sed 's/^/"&/; s/$/&"/' > strings.dict
```
### Step 3: Pass Dictionary to Fuzzer
Use the appropriate flag for your fuzzer (see Quick Reference above).
## Common Patterns
### Pattern: Protocol Keywords
**Use Case:** Fuzzing HTTP or custom protocol handlers
**Dictionary content:**
```conf
# HTTP methods
"GET"
"POST"
"PUT"
"DELETE"
"HEAD"
# Headers
"Content-Type"
"Authorization"
"Host"
# Protocol markers
"HTTP/1.1"
"HTTP/2.0"
```
### Pattern: Magic Bytes and File Format Headers
**Use Case:** Fuzzing image parsers, media decoders, archive handlers
**Dictionary content:**
```conf
# PNG magic bytes and chunks
png_magic="\x89PNG\r\n\x1a\n"
ihdr="IHDR"
plte="PLTE"
idat="IDAT"
iend="IEND"
# JPEG markers
jpeg_soi="\xFF\xD8"
jpeg_eoi="\xFF\xD9"
```
### Pattern: Configuration File Keywords
**Use Case:** Fuzzing config file parsers (YAML, TOML, INI)
**Dictionary content:**
```conf
# Common config keywords
"true"
"false"
"null"
"version"
"enabled"
"disabled"
# Section headers
"[general]"
"[network]"
"[security]"
```
## Advanced Usage
### Tips and Tricks
| Tip | Why It Helps |
|-----|--------------|
| Combine multiple generation methods | LLM-generated keywords + strings from binary covers broad surface |
| Include boundary values | `"0"`, `"-1"`, `"2147483647"` trigger edge cases |
| Add format delimiters | `:`, `=`, `{`, `}` help fuzzer construct valid structures |
| Keep dictionaries focused | 50-200 entries perform better than thousands |
| Test dictionary effectiveness | Run with and without dict, compare coverage |
### Auto-Generated Dictionaries (AFL++)
When using `afl-clang-lto` compiler, AFL++ automatically extracts dictionary entries from string comparisons in the binary. This happens at compile time via the AUTODICTIONARY feature.
**Enable auto-dictionary:**
```bash
export AFL_LLVM_DICT2FILE=auto.dict
afl-clang-lto++ target.cc -o target
# Dictionary saved to auto.dict
afl-fuzz -x auto.dict -i in -o out -- ./target
```
### Combining Multiple Dictionaries
Some fuzzers support multiple dictionary files:
```bash
# AFL++ with multiple dictionaries
afl-fuzz -x keywords.dict -x formats.dict -i in -o out -- ./target
```
## Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|--------------|---------|------------------|
| Including full sentences | Fuzzer needs atomic tokens, not prose | Break into individual keywords |
| Duplicating entries | Wastes mutation budget | Use `sort -u` to deduplicate |
| Over-sized dictionaries | Slows fuzzer, dilutes useful tokens | Keep focused: 50-200 most relevant entries |
| Missing hex escapes | Non-printable bytes become mangled | Use `\xXX` for binary values |
| No comments | Hard to maintain and audit | Document sections with `#` comments |
## Tool-Specific Guidance
### libFuzzer
```bash
clang++ -fsanitize=fuzzer,address harness.cc -o fuzz
./fuzz -dict=./dictionary.dict corpus/
```
**Integration tips:**
- Dictionary tokens are inserted/replaced during mutations
- Combine with `-max_len` to control input size
- Use `-print_final_stats=1` to see dictionary effectiveness metrics
- Dictionary entries longer than `-max_len` are ignored
### AFL++
```bash
afl-fuzz -x ./dictionary.dict -i input/ -o output/ -- ./target @@
```
**Integration tips:**
- AFL++ supports multiple `-x` flags for multiple dictionaries
- Use `AFL_LLVM_DICT2FILE` with `afl-clang-lto` for auto-generated dictionaries
- Dictionary effectiveness shown in fuzzer stats UI
- Tokens are used during deterministic and havoc stages
### cargo-fuzz (Rust)
```bash
cargo fuzz run fuzz_target -- -dict=./dictionary.dict
```
**Integration tips:**
- cargo-fuzz uses libFuzzer backend, so all libFuzzer dict flags work
- Place dictionary file in `fuzz/` directory alongside harness
- Reference from harness directory: `cargo fuzz run target -- -dict=../dictionary.dict`
### go-fuzz (Go)
go-fuzz does not have built-in dictionary support, but you can manually seed the corpus with dictionary entries:
```bash
# Convert dictionary to corpus files
grep -o '".*"' dict.txt | while read line; do
echo -n "$line" | base64 > corpus/$(echo "$line" | md5sum | cut -d' ' -f1)
done
go-fuzz -bin=./target-fuzz.zip -workdir=.
```
## Troubleshooting
| Issue | Cause | Solution |
|-------|-------|----------|
| Dictionary file not loaded | Wrong path or format error | Check fuzzer output for dict parsing errors; verify file format |
| No coverage improvement | Dictionary tokens not relevant | Analyze target code for actual keywords; try different generation method |
| Syntax errors in dict file | Unescaped quotes or invalid escapes | Use `\\` for backslash, `\"` for quotes; validate with test run |
| Fuzzer ignores long entries | Entries exceed `-max_len` | Keep entries under max input length, or increase `-max_len` |
| Too many entries slow fuzzer | Dictionary too large | Prune to 50-200 most relevant entries |
## Related Skills
### Tools That Use This Technique
| Skill | How It Applies |
|-------|----------------|
| **libfuzzer** | Native dictionary support via `-dict=` flag |
| **aflpp** | Native dictionary support via `-x` flag; auto-generation with AUTODICTIONARIES |
| **cargo-fuzz** | Uses libFuzzer backend, inherits `-dict=` support |
### Related Techniques
| Skill | Relationship |
|-------|--------------|
| **fuzzing-corpus** | Dictionaries complement corpus: corpus provides structure, dictionary provides keywords |
| **coverage-analysis** | Use coverage data to validate dictionary effectiveness |
| **harness-writing** | Harness structure determines which dictionary tokens are useful |
## Resources
### Key External Resources
**[AFL++ Dictionaries](https://github.com/AFLplusplus/AFLplusplus/tree/stable/dictionaries)**
Pre-built dictionaries for common formats (HTML, XML, JSON, SQL, etc.). Good starting point for format-specific fuzzing.
**[libFuzzer Dictionary Documentation](https://llvm.org/docs/LibFuzzer.html#dictionaries)**
Official libFuzzer documentation on dictionary format and usage. Explains token insertion strategy and performance implications.
### Additional Examples
**[OSS-Fuzz Dictionaries](https://github.com/google/oss-fuzz/tree/master/projects)**
Real-world dictionaries from Google's continuous fuzzing service. Search project directories for `*.dict` files to see production examples.Related Skills
fuzzing-apis
This skill enables Claude to perform automated fuzz testing on APIs to discover vulnerabilities, crashes, and unexpected behavior. It leverages malformed inputs, boundary values, and random payloads to generate comprehensive fuzz test suites. Use this skill when you need to identify potential SQL injection, XSS, command injection vulnerabilities, input validation failures, and edge cases in APIs. Trigger this skill by requesting fuzz testing, vulnerability scanning, or security analysis of an API. The skill is invoked using the `/fuzz-api` command.
Writing Fuzzing Harnesses
A fuzzing harness is the entrypoint function that receives random data from the fuzzer and routes it to your system under test (SUT). The quality of your harness directly determines which code paths get exercised and whether critical bugs are found. A poorly written harness can miss entire subsystems or produce non-reproducible crashes.
Overcoming Fuzzing Obstacles
Codebases often contain anti-fuzzing patterns that prevent effective coverage. Checksums, global state (like time-seeded PRNGs), and validation checks can block the fuzzer from exploring deeper code paths. This technique shows how to patch your System Under Test (SUT) to bypass these obstacles during fuzzing while preserving production behavior.
api-fuzzing-for-bug-bounty
This skill should be used when the user asks to "test API security", "fuzz APIs", "find IDOR vulnerabilities", "test REST API", "test GraphQL", "API penetration testing", "bug bounty API testing", or needs guidance on API security assessment techniques.
api-fuzzing-bug-bounty
This skill should be used when the user asks to "test API security", "fuzz APIs", "find IDOR vulnerabilities", "test REST API", "test GraphQL", "API penetration testing", "bug b...
SKILL: Fuzzing
## Metadata
SKILL: Week 2: Finding Vulnerabilities Through Fuzzing
## Metadata
dictionary-api-automation
Automate Dictionary API tasks via Rube MCP (Composio). Always search tools first for current schemas.
Daily Logs
Record the user's daily activities, progress, decisions, and learnings in a structured, chronological format.
Socratic Method: The Dialectic Engine
This skill transforms Claude into a Socratic agent — a cognitive partner who guides
Sokratische Methode: Die Dialektik-Maschine
Dieser Skill verwandelt Claude in einen sokratischen Agenten — einen kognitiven Partner, der Nutzende durch systematisches Fragen zur Wissensentdeckung führt, anstatt direkt zu instruieren.
College Football Data (CFB)
Before writing queries, consult `references/api-reference.md` for endpoints, conference IDs, team IDs, and data shapes.