JoasASantos / Offensive-Security-AI-Models

# Uncensored LLMs for Offensive Security
Curated list of open-weight uncensored models for authorized red team operations, penetration testing, and security research.
> All data sourced from HuggingFace model cards and official publications. Sep 2026.
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## Security Fine-tuned Models
### 1. DeepHat V2 (WhiteRabbitNeo)
| Spec | Value |
|------|-------|
| Base Model | Qwen2.5-Coder-7B |
| Parameters | 7B / 32B |
| Context Length | 131K |
| VRAM (Q4_K_M) | ~6 GB |
| Uncensoring Method | SFT on 1.7M offensive/defensive samples |
| Training Data | 1.7M security-specific samples (USENIX Security 2024 workshop) |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
**Download:** https://huggingface.co/WhiteRabbitNeo
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### 2. BugTraceAI-CORE-Apex (26B)
| Spec | Value |
|------|-------|
| Base Model | Gemma4-26B MoE |
| Parameters | 26B MoE |
| Context Length | 32K |
| VRAM (Q4_K_M) | ~16 GB |
| Uncensoring Method | SFT on HackerOne Hacktivity 2024-2025 |
| Training Data | HackerOne reports + WAF evasion dataset |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
**Download:** https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Apex-26b
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### 3. BugTraceAI-CORE-Ultra (27B)
| Spec | Value |
|------|-------|
| Base Model | Qwen3.6-27B (DavidAU fine-tuned variant) |
| Parameters | 27B dense |
| Context Length | 4K (recommended) |
| VRAM (Q6_K) | ~22-24 GB |
| Uncensoring Method | SFT via Unsloth on bug bounty + CVE data |
| Training Data | 2,541 examples from bug bounty disclosures, CVE writeups, and security research (2024-2026) |
| Specialization | Tooling model: generates Nuclei templates, CVE PoCs, exploit code, pentest scripts |
| Vision | No |
| Tool Calling | Yes |
| License | Apache 2.0 |
**Download:** https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6
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### 4. CYBER-FROST-3.8 (Blackfrost-AI)
| Spec | Value |
|------|-------|
| Base Model | Qwen/Qwen3.8-Flash-Next |
| Parameters | ~180B total (512 routed experts, 10 active per token) |
| Context Length | 262K |
| Architecture | Qwen4ExpForConditionalGeneration, 48 transformer blocks, hybrid linear + full attention |
| VRAM | Multi-GPU required (tested on 4x NVIDIA B300 SXM6) |
| Uncensoring Method | Security-domain fine-tuning on proprietary Blackfrost-AI corpus |
| Training Data | Proprietary security corpus: recon, web app security, vuln research, malware analysis, cloud security, threat intel |
| MTP | Yes (1 native MTP layer for speculative decoding) |
| Vision | No |
| Tool Calling | Yes |
| License | Qwen Community License 1.0 |
**Download:** https://huggingface.co/Blackfrost-AI/CYBER-FROST-3.8-BF16
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### 5. CyberPal 2.0 (20B)
| Spec | Value |
|------|-------|
| Base Model | gpt-oss-20b |
| Parameters | ~20B (21B in files) |
| Context Length | 8,192 |
| VRAM (BF16) | ~42 GB |
| Uncensoring Method | SFT on SecKnowledge 2.0 pipeline |
| Training Data | 403K examples via expert-in-the-loop schema steering, multi-step grounding, LLM quality checks |
| Spe