Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Encod3d-Sec/TORCHnpx agentmods add skills/encod3d-sec/torch/fuzzWrote 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/encod3d-sec/torch/fuzz)<a href="https://agentmods.dev/skills/encod3d-sec/torch/fuzz"><img src="https://agentmods.dev/badge/skills/encod3d-sec/torch/fuzz/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/encod3d-sec/torch/fuzz"><img src="https://agentmods.dev/badge/skills/encod3d-sec/torch/fuzz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00177 | $0.01463 |
| Opus 5.5 | $0.00071 | $0.00585 |
| Sonnet 5.5 | $0.00035 | $0.00293 |
| Haiku 4.5 | $0.00018 | $0.00146 |
Grade A, and why
fuzz 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 1mo 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fuzz - adaptive web fuzzing
0. Profile (do this first)
Read engagement_type from the active targets/<eng>/state.md frontmatter and set the profile:
- ctf - loud/fast, ignore WAF, recurse deep, exhaust the big list.
- pt - calibrated rate, obey RoE flags (
no_bruteforce/no_dos-> SKIP the brute tiers entirely). - bb - stealth: low rate + jitter, watch for the ban BEFORE it lands, request-budget aware.
wl-pick.shemits the profile flags; you apply them.
1. Two axes
- Surface (widen): content, files, vhost, api, artifacts. Recursive by default.
- Parameter (deepen): once an endpoint takes input, fuzz hidden params. Triggered by OBSERVING a param-accepting endpoint, never blind.
2. Select (deterministic) - always via wl-pick.sh
# what to run for a surface, given the engagement type and any fingerprint:
bash scripts/wl-pick.sh content "" ctf # generic content discovery
bash scripts/wl-pick.sh content wordpress bb # WordPress-aware, BB-stealth
bash scripts/wl-pick.sh vhost "" pt
bash scripts/wl-pick.sh params "" bb
It prints the seclists base, the profile flags line, and the ordered absolute paths (T0 harness -> T3 fingerprint list -> T1 surface lists, size-ordered). NEVER hand-pick a list from memory and NEVER start with directory-list-2.3-medium (220k). The size order is already correct in the output; run top-to-bottom, stop climbing when you have enough signal.
3. Calibrate (native first, backstop with judgment)
- Default to ffuf
-ac/-accand feroxbuster auto-filtering. - If a wildcard/soft-404 fools
-ac(everything returns 200 with varying size): fire 2-3 known-bogus random paths first, read status/size/words, then set explicit-fs/-fwon the catch-all baseline, or-mc 200,301,302,401,403on a clean 404. - READ tool output END-TO-END, never a grep. A real hit hides in the noise.
4. Climb tiers on SIGNAL (the adaptive core)
Climb T0 -> T1 -> T2 -> T3 when the current tier is exhausted OR a fingerprint unlocks a better list:
- T2 cewl when T0/T1 run dry:
cewl -d 3 -m 5 --lowercase -w targets/<eng>/custom-words.txt https://TARGETthen feed that list back through the same axis. See [[cewl]]. - T3 app-specific the moment you fingerprint a known product: re-run
wl-pick.sh <surface> <product> <type>to jump straight to its shipped list. For a product with no shipped list,Skill(wiki-arsenal)for its known paths, then cewl its docs / proberobots.txtsitemap.xmlswagger.jsonopenapi.json.
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.
- 1mo ago First seen · 55 lines · 177 tokens per session scan A 5d448e65d763
fuzz is a skill published in the GitHub repository Encod3d-Sec/TORCH (336 stars, last pushed 1mo ago), licensed MIT. It adds 177 tokens to every session and 1,463 once invoked, about $0.0007 per session on Opus 5.5. 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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