GitHub - JoasASantos/Offensive-Security-AI-Models: Uncensored AI models or those fine-tuned for cybersecurity tasks.

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. 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 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 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 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 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 Specialization Defensive: CTI, vuln analysis, detection/mitigation, SOC/IR, AppSec, compliance Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/cyber-pal-security/CyberPal2.0-20B 6. Cyber-Prime 1.1 (2.6B) Spec Value Base Model LiquidAI/LFM2-2.6B Parameters 2.6B (~3B actual) Context Length N/A (model card does not specify) VRAM (BF16) ~6 GB Tensor Type BF16 Uncensoring Method SFT + RL + reward-guided post-training on 75K cybersecurity rows Training Data NER repair (~6K), HTTP reasoning w/ CoT (~5K), email phishing (~5K), threat intel summarization (~2K), GHSA/KEV/ATT&CK Operating Modes Direct mode (classification) + Think mode (chain-of-thought) CyberBench Average 0.592 F1/Acc (up from 0.501 in v1.0) CyberBench Highlights NER 0.499, Phishing 0.890, HTTP Attack 0.628 Vision No Tool Calling No License LFM Open License v1.0 Download: https://huggingface.co/Akahsizrr/Cyber-Prime-1.1-2.6B 7. Cyber-Ornith-1.5-9B (DuoNeural / mradermacher) Spec Value Base Model ornith-ai/Ornith-1.5-9B (Qwen 3.5 architecture) Parameters 9B Context Length 128K (Qwen 3.5 default) VRAM (Q4_K_M) ~7 GB Uncensoring Method Obliteration (abliteration variant) Training Data NousResearch/hermes-function-calling-v1, OpenThoughts3-1.2M, openhands-synthetic-conversations Specialization Agentic cybersecurity: function-calling, tool-use, reasoning, CLI/terminal automation Format GGUF (IQ1_S to Q6_K available) Vision No Tool Calling Yes License Apache 2.0 Download: https://huggingface.co/mradermacher/Cyber-Ornith-1.5-9B-OBLITERATED-i1-GGUF 8. Dolphin3-Cyber-8B (RavichandranJ) Spec Value Base Model Dolphin3.0-Llama3.1-8B-abliterated Parameters 8.03B Context Length 2,048 (fine-tuned) / 131K (base) VRAM (Q4_K_M) ~6 GB Uncensoring Method LoRA rank-16 on abliterated Dolphin3 base Training Data Cybersecurity-specific: pentest, vuln analysis, exploit dev, incident response Architecture LlamaForCausalLM, 32 layers, GQA (32 heads, 8 KV heads) Performance 5 tok/s (CPU) to 55 tok/s (RTX 4060) Vision No Tool Calling No License Llama 3.1 Download: https://huggingface.co/RavichandranJ/Dolphin3-Cyber-8B-GGUF 9. Imperum-CybersecurityLLM v1.0 Spec Value Base Model Qwen/Qwen3.6-35B-A3B Parameters 34.66B total / ~3B active (MoE, 256 routed experts, 8 active per token) Context Length 16,384 (recommended 8,192 for resource-constrained) VRAM (Q4_K_M) ~22 GB Architecture Qwen3.5-MoE, 40 layers, hybrid linear + full attention Uncensoring Method SFT across 10+ security domains Training Data SOC/SIEM operations, detection engineering, DFIR, malware analysis, threat intel, vuln management, cloud/K8s/IAM, OT security, GRC, authorized pentesting Vision No Tool Calling Yes License Apache 2.0 Download: https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF 10. Lily-Cybersecurity-7B v0.2 (Segolily Labs) Spec Value Base Model Mistral-7B-Instruct-v0.2 Parameters 7B Context Length 8K VRAM (Q4_K_M) ~6 GB Uncensoring Method SFT on 22K cybersecurity pairs Training Data 22,000 hand-crafted cybersecurity data pairs across 28+ domains: pentesting, malware analysis, IR, cloud security Training Hardware Single A100, 24h, 5 epochs Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/segolilylabs/Lily-Cybersecurity-7B-v0.2 11. pentest-v2 (gewsefa) Spec Value Base Model Qwen3-8B Parameters 8B Context Length 32K VRAM (Q4_K_M) ~6 GB Uncensoring Method LoRA r=4, 2,804 curated samples Training Data GTFOBins, HackTricks, HackTheBox writeups GTFOBins Accuracy 100% (vs 25% base model zero-shot) Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/gewsefa/pentest-v2 12. Qwythos-9B (Empero AI) Spec Value Base Model Qwen3.5-9B Parameters 9B Context Length 1M (YaRN rope-scaling) VRAM (Q4_K_M) ~7 GB Uncensoring Method Post-training on 500M+ tokens of Claude Mythos / Claude Fable traces with CoT Benchmarks +34 MMLU, +30 GSM8K vs base (Empero evals) Native Function Calling Yes (Qwen3.5 spec) Chain-of-Thought Always-on <think> block Variants Base (SFT), Claude-Mythos-5-1M-GGUF (Q4_K_M to BF16) Vision Yes (inherited vision tower) Tool Calling Yes License Apache 2.0 Download (base): https://huggingface.co/emperorai/Qwythos-9B Download (GGUF): https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF 13. RavenX-CyberAgent (deadbydawn101) Spec Value Base Model Qwen/Qwen3.6-35B-A3B Parameters 36B total / 3B active (MoE) Context Length 262K (native), 32K tested VRAM (Q4_K_M) ~24 GB Uncensoring Method 12-round progressive SFT on 745K+ examples from 110 sources Training Data Pentest reports, bug bounty data, Claude Mythos reasoning, MITRE ATT&CK, blackhat content Specialization RATH protocol: Attack Surface, Exploit, Impact, Remediation, Document, Prevent Output Format CVSS scores, CWE identifiers, MITRE ATT&CK mappings Inference Speed 89 tok/s generation, 900 tok/s prompt processing Vision No Tool Calling Yes License Apache 2.0 Download: https://huggingface.co/deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF 14. REDCELL-26B-A4B (terrorswift) Spec Value Base Model Google Gemma 4 26B-A4B (Unsloth fine-tuned) Parameters 26B total / ~4B active (MoE) Context Length 262K VRAM (APEX-Mini) ~12 GB VRAM (Q8_0) ~26 GB Uncensoring Method 16-bit LoRA SFT on 6,500 custom instructions Training Data Cyber threat intelligence, investigative journalism, counter-disinformation, analytical methodology Specialization OSINT: threat actor attribution, IoC pivoting, geolocation analysis, Admiralty source credibility, vulnerability contextualization APEX Quantization Domain-weighted imatrix (~70% REDCELL corpus, ~30% general calibration) Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/terrorswift/REDCELL-26B-A4B-OSINT-Cyber-APEX-GGUF 15. VEXT Pentest-7B Spec Value Base Model Mistral-7B Parameters 7B Context Length 8K VRAM (Q4_K_M) ~6 GB Uncensoring Method QLoRA SFT + DPO on pentest traces Training Data Pentest methodology, tool usage, reporting Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/vextechnologies/VEXT-Pentest-7B 16. security-slm-unsloth-1.5b Spec Value Base Model Qwen2.5-1.5B Parameters 1.5B Context Length 32K VRAM (Q4_K_M) ~2 GB Uncensoring Method Unsloth SFT on security Q&A Training Data Security knowledge base, CTF-style Vision No Tool Calling No License Apache 2.0 Download: https://huggingface.co/AbdullahMujtaba/security-slm-unsloth-1.5b General Abliterated Models 17. Qwen3.8-27B-Uncensored-OrcaRouter (chimingw GGUF) Spec Value Base Model Qwen3.8-27B Parameters 27B dense Context Length 262K VRAM (Q4_K_M) ~18 GB Uncensoring Method Abliteration (131 matrices, Arditi et al. 2024) Intelligence Index 52 (Artificial Analysis) Vision Yes Tool Calling Yes License Apache 2.0 HF Downloads 230K+ HF Likes 257+ Download (GGUF): https://huggingface.co/chimingw/Qwen3.8-27B-Uncensored-OrcaRouter-GGUF Download (base): https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored 18. GLM-5.3-Flash-Uncensored-FP8 (OrcaRouter) Spec Value Base Model GLM-5.3-Flash Parameters 320B total / 18B active (288 routed experts, MoE) Context Length 1M VRAM (FP8) ~80 GB+ (multi-GPU) Uncensoring Method Abliteration (layer 22/45, deeper refusal mechanism) Compliance Rate 82.8% (OrcaRouter testing) MTP Yes (Multi-Token Prediction preserved) Vision Yes + Video Tool Calling Yes License MIT Download: https://huggingface.co/orcarouter/GLM-5.3-Flash-Uncensored-FP8 19. GLM-5.3-CYBERSECURITY-FP8 (dealignai)