license: apache-2.0
base_model: orcarouter/Qwen3.8-27B-Uncensored
language:

• es
• en
  library_name: mlx
  pipeline_tag: image-text-to-text
  tags:
• cybersecurity
• bug-bounty
• red-team
• blue-team
• offensive-security
• defensive-security
• pentesting
• vulnerability-research
• uncensored
• mlx
• mlx-vlm
• apple-silicon
• metal
• qwen3.8
• qwen35
• spanish
• lora
• qlora
• unsloth
  inference: false

Qwen3.8-27B-Uncensored-bughunter-v7 · MLX 4-bit (with Vision)

MLX 4-bit build optimized for Apple Silicon, converted from mmp2055/Qwen3.8-27B-Uncensored-bughunter-v7 (GGUF version).

Same fine-tune, same dataset, same LoRA — repackaged as MLX safetensors 4-bit for native Metal acceleration on M-series Macs. Ships with the multimodal vision tower + projector intact, so image understanding works out of the box via mlx-vlm.

For non-Apple platforms, use the GGUF version.

Format and download

• Full multimodal MLX build: ~15 GB total in 3 shards of ~5 GB each.
• Single directory — download the whole folder. The mlx-vlm runtime detects text tower + vision tower + projector automatically from config.json.

Runs on:

• mlx-vlm CLI and Python API — the recommended runtime for VLM on Apple Silicon.
• LM Studio with the MLX runtime extension enabled (text-only inference for now; vision via GUI depends on the LM Studio version supporting MLX-VLM sidecars).
• Any tool built on top of mlx-vlm (custom servers, chat wrappers, etc.).

Requires: macOS on Apple Silicon (M1/M2/M3/M4/M5). Not compatible with Linux, Windows or Intel Macs — for those, use the GGUF build.

Performance notes (Apple Silicon)

Measured on Apple M5 with 32 GB unified memory:

For a full comparison and honest trade-offs, see the notes below. On unified memory of 48 GB or more, this model runs at longer contexts (64k+) with the same speed profile.

Capabilities

Red team / offensive security

Covers the full lifecycle of a bug bounty engagement:

• Reconnaissance: passive subdomain enumeration, attack-surface mapping, API mapping, secret extraction from JavaScript, throttled use of httpx/gau/katana.
• Web vulnerabilities: root patterns and sub-patterns for IDOR/BOLA, SSRF, CSRF, XSS (reflected/stored/DOM/blind/mXSS), SQL injection across contexts, misconfigured CORS, chainable open redirects into OAuth, prototype pollution, subdomain takeover, path traversal, LFI/RFI, HTTP request smuggling (CL.TE, TE.CL, H2 desync, single-packet attack), cache poisoning and deception.
• Authentication and authorization: OAuth/OIDC (redirect_uri, nOAuth, state/nonce/PKCE, alg/kid/JWKS), JWT (unverified signature, alg:none, weak key, jwk/jku/kid injection, algorithm confusion RS256→HS256), 2FA/MFA bypass, session fixation, mass assignment.
• APIs: BOLA, BFLA, excessive data exposure, legacy versions without authz, GraphQL endpoints with introspection, alias abuse, batching to skip rate limits.
• Server-side: SSRF to cloud metadata (AWS IMDSv1/v2, GCP, Azure), SSTI ({{7*7}} on Jinja/Twig/Freemarker/Velocity), RCE via deserialization, XXE in SAML/SOAP/SVG/OOXML.
• Business logic: race conditions (TOCTOU), price/quantity/balance tampering, payment-flow TOCTOU, coupon/credit abuse.
• Mobile: exposed WebView bridges, exported deeplinks with weak parsing, Android static analysis (SAST), compromised app logic.
• Reporting: writing reports in each platform's format (HackerOne CVSS, Bugcrowd VRT, Intigriti), severity calculation, impact reasoning.

Blue team / defensive security

• DFIR: incident response methodology, timeline reconstruction, artifact collection, chain of custody.
• Network traffic analysis: PCAP analysis, C2 detection, DNS tunneling, exfiltration.
• Memory forensics: Volatility-based analysis, resident malware, code injection, process hollowing.
• Threat hunting: TTP-based hypothesis, event correlation, lateral movement.
• Vulnerability management: CVSS + context prioritisation, CI/CD integration.
• SOAR / defensive automation: response playbooks, SIEM integration.

Vision — screenshots of security artefacts

The MLX-VLM build preserves the multimodal capability of the base model. Practical uses in bug bounty context:

• Analyse screenshots of WAF blocks / error pages (401, 403, 500, custom messages) to infer stack.
• Read Burp Suite panels (Repeater, Intruder, Proxy) and reason about the request/response.
• Interpret architecture diagrams shared during triage.
• Extract text and structure from exposed admin panels or misconfigured dashboards.
• OCR + reasoning on screenshots of code snippets in Slack, email, PDFs.

Note: the vision tower comes from the base model and was not fine-tuned — the added value is on the text side. Image understanding is general-purpose (no security-specific visual training).

Built-in operational rules

When a system prompt is provided, the model incorporates ethical research rules:

• In-scope assets only, verified against the program scope before every request.
• Sustained throughput ≤1-2 req/s. Bursts only for race conditions or authorised OTP brute-force.
• Minimal non-destructive PoC: whoami/id for RCE; SELECT version() for SQLi; two owner accounts for IDOR.
• OOB callbacks always to owned infrastructure. Never to shared third-party services.
• Never use credentials, tokens or API keys found in a finding to validate access.
• Cloud metadata: proving access is enough. Never exfiltrate IAM credentials.
• Minimum reportable severity: High or Critical (except in a chain, when Low is explicitly bountied, or in humanitarian VDPs).

How it was built

This MLX build is a quantization + repackaging step on top of the base fine-tune. The full training details (dataset, hyperparameters, hygiene) are documented in the GGUF v7 model card.

Conversion pipeline (this MLX build)

1. Fusion on Colab (A100 High-RAM): base model loaded with AutoModelForImageTextToText.from_pretrained() (multimodal class, preserves vision tower + projector) in bfloat16. LoRA adapter applied via PeftModel.from_pretrained() and merged with model.merge_and_unload(). Full merged model saved (~55 GB with vision).
2. Conversion on Mac (Apple Silicon): mlx_vlm.convert -q --hf-path <merged> --mlx-path <mlx> performs the MLX quantization to 4-bit. Result: ~15 GB across 3 safetensors shards, 4.695 bits per weight including the vision tower.
3. Chat template: same patched template as the GGUF build — accepts multiple system messages (compatibility with IDE agents that inject their own system prompt on top of one the user set).

Reproducibility

Full pipeline (Colab notebook + Mac conversion script) is documented at the source. To reproduce this exact MLX build:

• Start from the LoRA adapter published in the GGUF v7 repo.
• Follow the fusion + mlx_vlm.convert -q steps above.

Usage

With mlx-vlm (recommended)

Install once:

pipx install mlx-vlm

Text-only:

mlx_vlm.generate \
  --model mmp2055/Qwen3.8-27B-Uncensored-bughunter-v7-MLX-VLM-4bit \
  --prompt "¿qué es un IDOR?" \
  --max-tokens 300

With an image (bug bounty screenshot use case):

mlx_vlm.generate \
  --model mmp2055/Qwen3.8-27B-Uncensored-bughunter-v7-MLX-VLM-4bit \
  --image screenshot-waf-block.png \
  --prompt "¿qué WAF es y qué payloads probarías para bypass?" \
  --max-tokens 500

As an OpenAI-compatible server:

mlx_vlm.server --model mmp2055/Qwen3.8-27B-Uncensored-bughunter-v7-MLX-VLM-4bit --port 8080

Endpoint at http://127.0.0.1:8080/v1/chat/completions with the OpenAI vision message format (type: image_url blocks).

In LM Studio

1. Download the whole folder into ~/.lmstudio/models/<publisher>/<repo>/.
2. Enable the MLX runtime extension in LM Studio (Runtime Extensions → MLX).
3. Load the model. Recommended:
   • Context length: 32,768 (start here). On 48 GB+ machines, 64k is comfortable.
   • Flash Attention: on.
   • KV Cache Quantization: off (qwen35 does not tolerate KV quantization well).
   • GPU Offload: all layers.

Text inference works natively. Vision inference in the LM Studio GUI depends on the version supporting MLX-VLM sidecars — check your build. If GUI vision isn't available, use mlx_vlm.generate from the CLI.

Recommended system prompt

You can use the model without a system prompt (it behaves correctly by default). For bug bounty tasks specifically, the recommended prompt is:

You are a professional bug bounty researcher. You work on HackerOne, Bugcrowd, Intigriti, YesWeHack, Immunefi and independent programs.

Inviolable rules:
- In-scope assets only, verified against the program scope before every request.
- Sustained throughput ≤1-2 req/s.
- Minimal non-destructive PoC.
- OOB callbacks always to owned infrastructure.
- Never use credentials or tokens found in a finding to validate access.
- Minimum reportable severity: High or Critical.

Answer in Spanish. Payloads, commands and report templates in English.

Known limitations

• Uncensored model. Based on orcarouter's decensoring work over Qwen3.8-27B. The additional fine-tune reinforces the ethical researcher role, but the user is responsible for using it within applicable law and authorised scope.
• Fine-tuned on a 100% text dataset. Vision comes from the base's untouched projector — image understanding works generally, but the fine-tune does not add domain-specific visual reasoning (no bug-bounty-specific image training).
• Corpus mostly in Spanish with English technical terminology.
• Source corpus bias: distilled material prioritises web and API vulnerabilities. Coverage of other domains (ICS/SCADA, low-level wireless, hardware hacking) is limited.
• LoRA only touches 16 full-attention layers + 64 MLP layers (256 pairs total). The 48 Gated DeltaNet layers were not adapted.
• Speed on 32 GB M-series: 27B Q4 at 6-9 tok/s is at the memory-bandwidth ceiling of these chips. For interactive daily chat, consider the smaller bughunter-v6 (9B) that runs at 20-30 tok/s on the same hardware.
• NOT A SUBSTITUTE FOR PRIMARY RESEARCH. The model can replay learned patterns but does not discover on its own. Use it as an assistant that speeds up the researcher's work, not as an oracle.
• Hallucination of CVEs, URLs and tool names. Always verify any concrete reference before using it.

Ethical and legal use

This model is distributed for:

• Authorised research within bug bounty programs offering Safe Harbor.
• Pentesting under a signed services contract.
• CTFs and lab environments you own or are authorised to use.
• Cybersecurity education.
• Academic research.

It is not distributed for:

• Attacking systems without explicit authorisation.
• Commercial exploitation of vulnerabilities found outside bounty programs.
• Malware development for distribution.
• Any activity that violates the applicable law in your jurisdiction.

The user assumes all legal responsibility for the use of the model.

Credits

• Base: orcarouter — Qwen3.8-27B-Uncensored, decensoring recipe over Qwen3.8.
• Architecture: Qwen3.8 (qwen35, hybrid attention with Gated DeltaNet + full attention, extended context up to 262,144 tokens, Multi-Token Prediction, native vision tower).
• Training framework: Unsloth — day-1 support for qwen35.
• MLX conversion: mlx-vlm — the Apple Silicon-native VLM runtime.
• Prior work in this series: bughunter-v6 (9B, GGUF) · bughunter-v7 (27B, GGUF).
• Licence: Apache 2.0, inherited from the base model.
• Recommended agent for using this model: https://xagentai.net/xagentai-net-coding-agent/

Citation

@misc{qwen38_bughunter_v7_mlx,
  title  = {Qwen3.8-27B-Uncensored-bughunter-v7 (MLX 4-bit with vision): Apple Silicon build of the bug bounty fine-tune},
  year   = {2026},
  base   = {orcarouter/Qwen3.8-27B-Uncensored},
  method = {LoRA fine-tune (rank=8, lr=1e-5, 1 epoch, QLoRA 4-bit, Unsloth) + mlx_vlm.convert -q},
  data   = {14914 ShareGPT examples distilled from public bug bounty corpus}
}