claude-red is a library of structured skills that give Claude specialized offensive-security methods for areas such as web vulnerabilities, shellcode, exploit development, and identity systems. It is intended for authorized red-team work, bug-bounty triage, security research, CTF preparation, and operator training. Its catalogue contains the project's skills for loading these security specializations into Claude.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/snailsploit/claude-red/offensive-fuzzingnpx skills add SnailSploit/Claude-Red --skill offensive-fuzzinggit clone --depth 1 https://github.com/SnailSploit/Claude-RedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/snailsploit/claude-red/offensive-fuzzing)<a href="https://agentmods.dev/skills/snailsploit/claude-red/offensive-fuzzing"><img src="https://agentmods.dev/badge/skills/snailsploit/claude-red/offensive-fuzzing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.03545 |
| Opus 5 | $0.00046 | $0.01773 |
| Sonnet 5 | $0.00018 | $0.00709 |
| Haiku 4.5 | $0.00009 | $0.00354 |
Grade A, and why
offensive-fuzzing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offensive Fuzzing
Fuzzer Types
| Type | Coverage | Speed | Tools |
|---|---|---|---|
| BlackBox | Poor | Fast | Peach, Boofuzz |
| GreyBox | Good | Fast | AFL++, Honggfuzz, libFuzzer, WinAFL |
| Snapshot | Good | Fastest | Nyx, wtf, Snapchange |
| WhiteBox | Best | Slow | KLEE, QSYM, SymSan |
| Ensemble | Best | Fast | AFL++ + Honggfuzz + libFuzzer |
GreyBox sub-variants: Directed (AFLGo, UAFuzz), Grammar (AFLSmart, Tlspuffin), Concolic (QSYM, Driller), Kernel (syzkaller, kAFL, wtf).
Core Workflow
Research target → Choose analyses → Build harness → Seed corpus → Instrument → Fuzz → Triage crashes → Report
1. Research Target
- Map all input surfaces (files, network, IPC, syscalls, IOCTL)
- Identify high-value areas: previously patched code, complex parsers, newly added code, input ingestion points
- For kernel modules: look beyond
copy_from_user— DMA-BUF ops, page fault handlers, VM operation structs, allocation callbacks
2. Instrument and Build
# AFL++ (preferred for GreyBox)
CC=afl-clang-fast CXX=afl-clang-fast++ cmake -DCMAKE_BUILD_TYPE=Release .. && make -j
# libFuzzer + ASan/UBSan (C/C++)
cmake -DCMAKE_CXX_FLAGS="-fsanitize=fuzzer,address,undefined -O1 -g" ..
# CmpLog build for hard compares
AFL_LLVM_CMPLOG=1 CC=afl-clang-fast CXX=afl-clang-fast++ make clean all
Windows (MSVC): Project Properties → C/C++ → Address Sanitizer: Yes (/fsanitize=address)
3. Write Harness
libFuzzer (C++):
#include <cstdint>
#include <cstddef>
extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
parse_or_process(data, size);
return 0;
}
Honggfuzz HF_ITER (persistent mode — preferred for large targets):
#include "honggfuzz.h"
int main(int argc, char** argv) {
initialize_target(); // runs once
for (;;) {
size_t len; uint8_t *buf;
HF_ITER(&buf, &len);
FILE* s = fmemopen(buf, len, "r");
target_function(s);
fclose(s);
reset_target_state();
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 339 lines · 91 tokens per session scan A fcd129b42cdf
offensive-fuzzing is a skill published in the GitHub repository SnailSploit/Claude-Red (3,026 stars, last pushed 6d ago), licensed MIT. It adds 91 tokens to every session and 3,545 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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