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Krypto-Whitehat/qwen3.8-9b-uncensored-cyber-exploit-XRPL-v3

Krypto-Whitehat Qwen 9B GGUF second-order 262K ctx
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  • author_summary 3 models
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
3K
2K last 30d - stable
Likes
8
Model age
7w ago
created 2026-08-19
Downloads over time
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6721.9K3.1K4.3K828 on Aug 193.9K on Oct 11AugSepOct
Aug 19 → Oct 11 · 48 snapshots · spans 53 days

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Metadata

License
other
Languages
en
Quantizations
Q4_K Q5_K
Tags
transformers gguf uncensored cybersecurity xrpl bug-triage exploit-writeup qlora agent text-generation en base_model:rohit267/Qwen3.8-9B-heretic-uncensored

Related

Total size
11.6 GB
Files
18
Quantizations
3
Registered
2026-08-22 13:56
Last updated on HF
2026-08-19 23:55

Files by quantization

Q5_K 1 file 6.19 GB
Qwen3.8-9B-Cyber-Exploit-Agent-v3-Q5_K_M.gguf 6.19 GB 1d8c483e download
Q4_K 1 file 5.38 GB
Qwen3.8-9B-Cyber-Exploit-Agent-v3-Q4_K_M.gguf 5.38 GB b6803ed2 download
Auxiliary files 16 files 63.8 MB
xrpl_raw_issues_v3.jsonl 35.8 MB 32d8650a download
xrpl-drill.zip 17.2 MB ec727d7b download
xrpl_raw_comments_full.jsonl 8.72 MB eadb45fd download
train_all_v3.jsonl 2.01 MB e4c4509a download
agent_v3.py 33.8 KB 73c1bb0e download
INFERENCE_RUNBOOK.md 9.80 KB c8fbcc26 download
README.md 9.13 KB 907f3ca4 download
eval_lock_v4.json 8.01 KB 8bfb2f32 download
ship_gate_v4.log 4.56 KB bbaf627c download
inference_system.txt 4.01 KB ac5c80d7 download
agent_smoke_v2.md 2.74 KB 5eb2aea8 download
.gitattributes 1.70 KB 987f0e07 download
dataset_gate_report_v3.txt 1.02 KB 51f72404 download
agent_v3.sh 808 B dee663ad download
MANIFEST.txt 730 B 80fb2516 download
BASE.txt 690 B b1e234ce download

README current version from Hugging Face


license: other
license_name: qwen-research
base_model: rohit267/Qwen3.8-9B-heretic-uncensored
tags:

  • uncensored
  • cybersecurity
  • xrpl
  • bug-triage
  • exploit-writeup
  • qlora
  • gguf
  • agent
    language:
  • en
    pipeline_tag: text-generation
    library_name: transformers

Qwen3.8-9B Cyber Exploit Agent — v3 (Writer + Host Agent)

  1. Host classifies C0–C8; 9B writes under CLASS_LOCK.
  2. GGUF alone is NOT an XRPL triage oracle — run agent_v3.py.
  3. Measured, frozen lock-eval: v3.3 adapter alone 7/9 gates (FAIL, refusals 2/8) · v3c DPO continue 6/9 + L2 regression (FAIL, discarded) · smoke v2 with agent_v3.py: 6/6.
  4. SFT format adapter; live fetch XRPLF/rippled develop + XRPL-Standards via the host. No Mythos/GLM claims.

What this is

An uncensored (heretic base, refusal-smoke 8/8 pre-SFT) QLoRA SFT adapter that reliably
produces structured offensive-security output (<think> + ### TRIGGER + ### EXPLOIT WRITEUP

  • ### VERDICT) for C/C++/Python labs, public CVE writeups and XRPL bug reports.
    Triage classification is done by the bundled host agent (agent_v3.py), which fetches
    live source (raw.githubusercontent.com, XRPLF repos only, rippled@develop) and applies
    deterministic rules C0–C8. The 9B writes the trigger/writeup under CLASS_LOCK; the host
    enforces the verdict line and strips track-tag drift on memory labs.

What this is NOT

  • Not an oracle: without agent_v3.py, XRPL VALID/FALSE_POSITIVE verdicts oscillate
    (see gate table). Do not use the GGUF alone for XRPL VALID/FP decisions.
  • No built-in web search. All fetching happens in the host script.
  • SFT-only (no RL). Not trained on the frozen lock set.

Gate table (frozen eval_lock_v4.json, 3 draws @ 0.6/0.95/20/rep1.05/1600)

Config Lock-Eval result
v3.3 adapter alone 7/9 gates, refusals 2/8 — FAIL
v3c (DPO continue, discarded) 6/9 + L2 regression — FAIL
v3.3 + agent_v3.py (host C0–C8) agent_smoke_v2: 6/6 — PASS

Per-item draws and rule evidence: agent_smoke_v2.md, ship_gate_v4.log, eval_lock_v4.json.

Usage

# 1) serve the Q4 GGUF (no mmproj):
llama-server -m Qwen3.8-9B-Cyber-Exploit-Agent-v3-Q4_K_M.gguf \
  --jinja -c 8192 -ngl 99 --port 8739

# 2) run the two-role pipeline (host classifies on live source, 9B writes):
python agent_v3.py --smoke2          # 6-item acceptance smoke
python agent_v3.py --ask "<bug report>"
python agent_v3.py --ask "..." --hunt
python agent_v3.py --test-rules      # offline rule unit tests, no GPU

Sampling: temp 0.6, top_p 0.95, top_k 20, repeat_penalty 1.05, max_tokens ≥ 3000
(see INFERENCE_RUNBOOK.md). Host allowlist: raw.githubusercontent.com + api.github.com,
XRPLF repos only, rippled@develop / XRPL-Standards@master / clients@main.

LM Studio instead of llama-server

The host speaks plain OpenAI-style chat completions, so LM Studio works too:

  1. Load the Q4 GGUF in LM Studio, open Developer → Start Server (default http://127.0.0.1:1234).
  2. Point the agent at it: AGENT_API_URL=http://127.0.0.1:1234/v1/chat/completions python agent_v3.py --ask "<report>"
    (or edit the API default at the top of agent_v3.py). Sampling (temp 0.6 / top_p 0.95 /
    top_k 20 / repeat_penalty 1.05 / max_tokens ≥ 3000) is sent with every request by the host.
  3. In OpenCode / any agent harness: keep the xrpl-drill skill on the orchestrating agent
    (fetch + six tests + CLASS_LOCK) and let this 9B serve as the writer endpoint. Never ask
    the bare GGUF for XRPL VALID/FP.

Bug Hunting — CVE sources (via the xrpl-drill skill, hand-in-hand with this model)

For bug hunting and triage, download the paired skill xrpl-drill.zip from this
repo's Files section and unpack it next to agent_v3.py. The skill is decisive: it encodes
the hunt engine (CVE → rippled develop map), the six ledger tests, the FP catalog from
maintainer-closed issues (xrpl_raw_comments_full.jsonl in Files) and the N11–N18 pattern
families. Model (writer) + host (classifier) + skill (drill) work hand-in-hand — for XRPL
none of them is optional.

The hunt walks live public CVE feeds before any "no new C++/Python/JS primitive in the
tree" claim. Generic C++ CVEs are the funnel; blockchain CVEs are optional. Rule:
primitive + symbol + patch, then grep a local rippled develop clone and
raw.githubusercontent.com/XRPLF/rippled/develop. A CVE name-drop without a develop or
client file:line is not a finding.

Priority Sources (public only)
P0 — earliest, fetch first oss-security (openwall.com/lists/oss-security) · OSS-Fuzz bugs (bugs.chromium.org/p/oss-fuzz) + oss-fuzz-vulns · OSV (osv.dev, api.osv.dev/v1/query) · GHSA (github.com/advisories, github/advisory-database) · Full Disclosure (seclists.org) · Project Zero · ZDI published · syzbot (only if same allocator idiom)
P1 — official CVE cve.org · NVD (nvd.nist.gov + services.nvd.nist.gov REST 2.0) · OpenCVE (app.opencve.io) · CISA KEV (in-the-wild only) · EPSS · EUVD · distro trackers (Debian/Ubuntu/RedHat)
P2 — public PoCs Exploit-DB · Packet Storm · AttackerKB · huntr · HackerOne Hacktivity · GitHub code search (poc CVE-… + lib)
P3 — XRPL dependency feeds Boost / OpenSSL / RocksDB / Protobuf / libsecp256k1 / NuDB releases · npm (xrpl) · PyPI (xrpl-py) · Maven (xrpl4j) via OSV/GHSA

Per hunt session: at least 2× P0 + 1× P1 + local grep, then map into develop /
clients (references/15-cve-to-rippled-hunt.md, full feed catalog in
references/17-cve-feeds.md inside the zip). N11 is XRPL self-protection — never a kernel
UAF label. linux-distros embargo list: know it exists, do not crawl. Darknet/0day
brokers/leaked embargo drops: skip. Full raw crawl corpus (issues + maintainer comments)
ships in Files: xrpl_raw_issues_v3.jsonl, xrpl_raw_comments_full.jsonl.

Acknowledgments

This work stands on the public track record of the rippled maintainers and contributors.
Their bug submissions, reproductions and — above all — their verdict comments ("intentional",
"working as designed", fix confirmations
) in the XRPLF/rippled GitHub tracker are the raw
material this model's triage discipline and the false-positive catalog were built on
(xrpl_raw_issues_v3.jsonl / xrpl_raw_comments_full.jsonl in Files; 105 contributors in
the crawl, 1,930 unique issues).

Special thanks to the top contributors whose historical records shaped the corpus:

mvadari · JoelKatz (David Schwartz) · vinniefalco · ahbritto · nbougalis · seelabs ·
ximinez · scottschurr · HowardHinnant · miguelportilla · mellery451 · rec · bthomee ·
mathbunnyru · justmoon

…plus the wider v3 crawl — among them WietseWind, RichardAH, RareData, BobWay, Bronek,
Silkjaer, MarkusTeufelberger
and all others in the public tracker. Every "not a bug" a
maintainer once wrote by hand is, decades of their judgment compressed, exactly what this
pipeline refuses to guess. Thank you.

Caution

Use it only for whitehat intent — not to damage other systems, never for harmful purposes.
Think twice; be a good person and do good things: help other people find bugs. Think always twice.

This is an uncensored version, meaning the safety filters placed by the Qwen maintainers were
removed (heretic base, refusal-smoke 8/8 before SFT) and the model was fine-tuned into a custom
bug-triage / bug-hunting mode. I am not responsible for any damages or harm you create by yourself.

Repo contents

File What
Qwen3.8-9B-Cyber-Exploit-Agent-v3-Q4_K_M.gguf / …Q5_K_M.gguf v3.3 build (adapter SHA256 c2e5196b…, see MANIFEST)
agent_v3.py / agent_v3.sh host agent: fetch allowlist, classifier C0–C8, writer CLASS_LOCK
INFERENCE_RUNBOOK.md serving, sampling, fetch contract, gate tables
inference_system.txt system prompt incl. rules 11–14 (fetch-or-UNPROVEN)
train_all_v3.jsonl SFT dataset, 334 unique rows (gate report included)
dataset_gate_report_v3.txt 16/16 dataset gate PASS
eval_lock_v4.json frozen 16-item lock eval (chmod a-w; never trained on)
ship_gate_v4.log v3.3 vs v3c gate table
agent_smoke_v2.md two-role pipeline acceptance: 6/6
BASE.txt base model + shard SHAs + pre-SFT refusal smoke 8/8

Honest limitations

  • VALID vs by-design/FP boundary is not stable from the weights alone at 9B; four SFT
    iterations and a DPO continue shifted but never dominated it. The agent pipeline is the
    working configuration (host classifies, model writes).
  • The agent alone is not 100% either. The working configuration is a triple:
    model (writer) + agent_v3.py (host classifier C0–C8) + the xrpl-drill skill
    (hunt bars, six ledger tests, N11–N18 families, maintainer FP catalog). The skill runs
    hand-in-hand with the model — without it, hunt coverage, claim discipline and FP
    recognition drop well below the measured 6/6; treat it as a required component, not an
    accessory.
  • Memory labs occasionally attract XRPL track tags from the model; the host strips them (C1).
  • On GGUF/LM Studio without the host, expect hedging on XRPL items — that is the documented
    behavior, not a defect you can prompt away.

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-08-19Upload README.md with huggingface_hubc1d2fd39.1 KB
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