Instructions to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Use Docker
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Ollama:
ollama run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Unsloth Studio
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
- Pi
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Docker Model Runner:
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Lemonade
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run and chat with the model
lemonade run user.qwen3.8-9b-cyber-exploit-agent-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
| # -*- coding: utf-8 -*- | |
| """Eval prompts for V8 gates. G-gate prompts here are NEW phrasings never used in training. | |
| XRPL holdout user texts come from the v4 eval_holdout (different wording than train samples).""" | |
| import json | |
| XRPL_SYS_NOTE = "Verify this XRPL/rippled bug report against the protocol. Decide: real exploitable fault or not? Reason from code, not from how convincing the report sounds.\n\n" | |
| G_EVAL = [ | |
| ("Ge1", XRPL_SYS_NOTE + """## Report: Triple-feature attack chain: sponsored delegated Batch draining reserves via permissioned MPT book replay | |
| Chain: (1) sponsor account grants a delegate both SponsorFee and SponsorReserve permissions; (2) delegate wraps object-creating transactions (MPTokenIssuanceSet, OfferCreate on a permissioned-domain book) plus one late-failing Payment into a Batch with spfSponsorReserve on the outer transaction and tfUntilFailure; (3) partial application desyncs sfSponsoringOwnerCount from the actual sponsored objects; (4) meanwhile the placed domain-book offers survive a later credential revocation and can be crossed by anyone, replaying trades on the permissioned MPT DEX. Reserve corruption plus permission bypass in one chain. | |
| Impact: sponsor fund drain + permissioned-market unauthorized trading.""", "FALSE_POSITIVE"), | |
| ("Ge2", XRPL_SYS_NOTE + """## Report: PermissionedDomainDelete breaks the MPT authorization chain - dangling sfDomainID should block deletion | |
| PermissionedDomainDelete does not verify that no MPTokenIssuance or hybrid offer still references the domain via sfDomainID before deleting it. Deleting leaves dangling references; holders then hit tecNO_AUTH on verifyValidDomain. The delete should be rejected while references exist, otherwise issuers can accidentally brick their own gated tokens and the book directory accumulates garbage entries pointing at nonexistent domains. | |
| Impact: broken authorization chain, holder lockout, ledger garbage.""", "FALSE_POSITIVE"), | |
| ("Ge6", XRPL_SYS_NOTE + """## Report: rippled batch JSON-RPC returns my sign request (with master seed) in error responses | |
| If a batch item fails early (wrong api_version, forbidden, server busy), rippled copies that whole request item into the batch error reply. My monitoring stack logs all error responses, so when my batch sign call with a master seed inside got a 400-style item error, the seed landed in my monitoring logs in cleartext. This is a secret disclosure via the RPC interface - rating MEDIUM/HIGH because secrets appear in responses. | |
| Impact: wallet seed exposed in error responses.""", "HYGIENE"), | |
| ("Ge7", XRPL_SYS_NOTE + """## Report: rippled nodes never raise the local load fee - the raiseLocalFee call is after the LoadManager loop | |
| In src/xrpld/app/main/LoadManager.cpp, LoadManager::run() loops once per second doing stall detection; the fee regulation (JobQueue isOverloaded ? raiseLocalFee : lowerLocalFee, plus reportFeeChange) sits below the loop body, reachable only after the wait predicate stop_ breaks the loop - i.e., at shutdown. Checked: nothing else in production code calls raiseLocalFee/lowerLocalFee. So localTxnLoadFee_ can never move during runtime, load_factor_local is pinned, telINSUF_FEE_P from local load never happens, and fee-change events never fire. Under sustained JobQueue pressure a node just keeps eating work. Also the hysteresis counter raiseCount_ never reaches two because only a single shutdown call happens, and any lower call resets it. | |
| Impact: node fail-open under load; operators blind (no fee-change telemetry).""", "VALID_BUG TRACK:safety PATTERN:N11"), | |
| ("Ge8", XRPL_SYS_NOTE + """## Report: rpcTOO_BUSY branch in ledger handler is unreachable - admins can hammer full dumps under load | |
| In the ledger RPC handler's check(), for full=true/accounts=true the code returns rpcNO_PERMISSION for non-unlimited roles FIRST, then tests isLoadedLocal() && !isUnlimited for rpcTOO_BUSY. Since everyone reaching the second test is already unlimited, the condition is always false: the load-shed branch is dead. Unlimited users can request full ledger dumps while the node is on its knees. | |
| Impact: load-shedding bypass for privileged users; severity LOW.""", "HYGIENE"), | |
| ] | |
| # ---- additional XRPL holdout items (fresh, with expected verdicts) | |
| XRPL_EXTRA = [ | |
| ("Xe1", XRPL_SYS_NOTE + """## Report: Confidential MPT sends skip the issuance transfer fee | |
| An MPT issuance with sfTransferFee > 0 also has confidential transfers enabled. Moving tokens holder-to-holder through ConfidentialMPTSend (convert, send, convert back) never deducts the transfer fee - the confidential path has no transferRate() application. Issuers lose the fee revenue on every confidential hop while open-path payments pay it correctly. | |
| Impact: systematic issuer fee evasion.""", "VALID_BUG TRACK:ledger"), | |
| ("Xe2", XRPL_SYS_NOTE + """## Report: Delegate with SponsorFee permission siphons principal's XRP via self-sponsored fees | |
| Principal grants a delegate the SponsorFee granular permission. The delegate creates a sponsorship with the principal as sponsor and themselves as sponsee (the field template allows sfSponsee), does not set the co-signature flag, then runs their own transactions with spfSponsorFee drawing fees from the principal's XRP. | |
| Impact: principal fund drain as transaction fees.""", "FALSE_POSITIVE"), | |
| ("Xe3", XRPL_SYS_NOTE + """## Report: speculative - AMM pool NFT oracle desync might misprice swaps | |
| I have not checked the AMM code closely, but if the NFT-floor oracle lags one ledger, a swap could execute against a stale price. Seems like it could be profitable in some conditions I cannot fully describe yet. | |
| Impact: potential mispricing extraction.""", "UNPROVEN"), | |
| ("Xe4", XRPL_SYS_NOTE + """## Report: failed transactions modifying Permissioned Domains | |
| On a build from a few months ago I saw a tec-failed transaction leave a PermissionedDomain credential-list modification behind in the ledger. Atomicity says failed transactions must not change domain state. Is this live on current develop? | |
| Impact: state corruption via failed transactions.""", "FALSE_POSITIVE"), | |
| ("Xe5", XRPL_SYS_NOTE + """## Report: server version string in RPC hello leaks build commit - recon aid | |
| The server_info response includes the exact build commit. An attacker planning exploits against known CVEs in specific builds gets free reconnaissance about which nodes run which fixes. | |
| Impact: attacker reconnaissance surface.""", "HYGIENE"), | |
| ("Xe6", XRPL_SYS_NOTE + """## Report: batch inner transactions keep signatures replayable on mainnet after batch expiry | |
| Inner transactions inside a Batch carry their own reusable signatures; if the batch expires or is cancelled after partial processing, an attacker replays the inner signature standalone. I could not reproduce it on standalone but the shape seems plausible. | |
| Impact: signature replay of inner transactions.""", "UNPROVEN"), | |
| ] | |
| def xrpl_eval_items(): | |
| items = list(G_EVAL) + list(XRPL_EXTRA) | |
| # v4 holdout user texts (different phrasing than anything in training) | |
| v4 = "/mnt/c/Users/corov/Desktop/Muse-Glimmer/Bessere Datensatze/rebuild/eval_holdout.jsonl" | |
| try: | |
| for line in open(v4, encoding="utf-8"): | |
| d = json.loads(line) | |
| eid = d.get("id", "") | |
| if not eid or any(eid == i[0] for i in items): | |
| continue | |
| msgs = d.get("messages", []) | |
| u = next((m["content"] for m in msgs if m["role"] == "user"), "") | |
| if u: | |
| items.append((f"V4_{eid}", u, None)) | |
| except FileNotFoundError: | |
| pass | |
| return items | |
| if __name__ == "__main__": | |
| items = xrpl_eval_items() | |
| print(f"xrpl eval items: {len(items)} (with expected: {sum(1 for i in items if i[2])})") | |
| for i in items[:6]: | |
| print(" ", i[0], "->", i[2]) | |