JoasASantos/Offensive-Security-AI-Models
Uncensored AI models or those fine-tuned for cybersecurity tasks. - JoasASantos/Offensive-Security-AI-Models
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README
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.
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)
Spec Value
Base Model zai-org/GLM-5.3 (via JANGQ-AI/GLM-5.3-FP8)
Parameters 753B total (glm_moe_dsa architecture)
Context Length ~131K (practical on 8x H200 w/ TP8)
VRAM (FP8) 8x H200 GPUs with tensor parallelism
Architecture 78 layers, text-only, routed FP8 experts
Uncensoring Method Direct weight modification for offensive-security, red-team, exploit-dev, RE, evasion, phishing, credential-attack, malware-analysis
Notes Not abliteration or LoRA; direct bf16 residual writer editing. Soft refusal on copyright reproduction retained
Vision No (text-only)
Tool Calling Yes
License MIT
Download: https://huggingface.co/dealignai/GLM-5.3-CYBERSECURITY-FP8
20. DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed (drowzeys)
Spec Value
Base Model DeepSeek-V4.1-Flash
Parameters MoE (size matches base)
Context Length Matches base DeepSeek-V4.1-Flash
Uncensoring Method Abliteration overlay on layers 10-35 attention projection (wo_b); layers 0-9, 36-39, expert layers, vision components unchanged
Format Modular overlay (not standalone checkpoint): FP8 (~1.1 GB) or EXL3 mul1 K=5 (~651 MB)
Deployment Apply on top of existing quantized base packs (native, EXL3, TR3-Hybrid)
GPU Util <= 0.85 recommended
Vision Yes (preserved)
Tool Calling Yes
License MIT
Download: https://huggingface.co/drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
21. huihui-ai/Qwen3.5-27B-abliterated
Spec Value
Base Model Qwen3.5-27B
Parameters 27B dense
Context Length 128K
VRAM (Q4_K_M) ~18 GB
Uncensoring Method Abliteration
Vision No
Tool Calling Yes
License Apache 2.0
Download: https://huggingface.co/huihui-ai/Qwen3.5-27B-abliterated
22. huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
Spec Value
Base Model Qwen2.5-Coder-32B-Instruct
Parameters 32B dense
Context Length 128K
VRAM (Q4_K_M) ~20 GB
Uncensoring Method Abliteration
Vision No
Tool Calling No
License Apache 2.0
Download: https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
23. Qwen3.8-27B-Cyber-agentic
Spec Value
Base Model Qwen3.8-27B
Parameters 27B dense
Context Length 262K
VRAM (Q4_K_M) ~18 GB
Uncensoring Method Abliteration + cyber agentic fine-tune
Vision Yes
Tool Calling Yes
License Apache 2.0
Download: https://huggingface.co/Qwen/Qwen3.8-27B (base, community abliterated variants available)
24. HIDra-30B-A3B (huihui-ai/Qwen3-Coder-30B-A3B-abliterated)
Spec Value
Base Model Qwen3-Coder-30B-A3B
Parameters 30B total / 3B active (MoE)
Context Length 128K
VRAM (Q4_K_M) ~20 GB
Uncensoring Method Abliteration
Vision No
Tool Calling Yes
License Apache 2.0
Download: https://huggingface.co/huihui-ai/Qwen3-Coder-30B-A3B-abliterated
25. qwen25_UNCENSORED_03-C
Spec Value
Base Model Qwen2.5-based
Parameters ~7B
Context Length 32K
VRAM (Q4_K_M) ~6 GB
Uncensoring Method Progressive fine-tuning (multi-stage)
Vision No
Tool Calling No
License Apache 2.0
Download: https://huggingface.co/models?search=qwen25_UNCENSORED
Legacy / Classic Models 26. Dolphin-Llama3-8B (Cognitive Computations)
Spec Value
Base Model Llama 3 8B
Parameters 8B
Context Length 8K
VRAM (Q4_K_M) ~6 GB
Uncensoring Method Data filtering (Dolphin method, Eric Hartford)
Training Data Dolphin dataset (alignment/refusal responses removed)
Vision No
Tool Calling No
License Llama 3 Community
Download: https://huggingface.co/cognitivecomputations/dolphin-2.9.3-llama-3-8b
27. Wizard-Vicuna-13B-Uncensored (QuixiAI)
Spec Value
Base Model LLaMA-13B
Parameters 13B
Context Length 2K
VRAM (Q4_K_M) ~10 GB
Uncensoring Method Data filtering (wizard_vicuna_70k_unfiltered)
MMLU 47.92 (Open LLM Leaderboard)
HellaSwag 81.95 (Open LLM Leaderboard)
TruthfulQA 51.69 (Open LLM Leaderboard)
Vision No
Tool Calling No
License Other
HF Likes 323
Download: https://huggingface.co/QuixiAI/Wizard-Vicuna-13B-Uncensored
Cloud Providers & Deployment Platforms Managed Inference (API Access)
Provider Description Uncensored Models Pricing API
OrcaRouter AI gateway with adaptive routing across 200+ models. Zero token markup, OpenAI-compatible endpoint. Own abliterated models (Qwen3.8, GLM-5.3). Yes, hosts own abliterated variants $0 token markup, BYOK or pay-as-you-go OpenAI-compatible
Featherless AI Serverless LLM hosting, HuggingFace's largest inference provider (6,700+ models). Supports uncensored/abliterated models natively. Yes, 40K+ models including uncensored $25/mo (32K ctx) or $50 credits/mo (256K ctx) OpenAI-compatible
Together AI Production inference platform, supports open models including uncensored variants. Select open models Pay-per-token OpenAI-compatible
GPU Cloud (Self-Hosted)
Provider Description Best For GPU Options
RunPod GPU cloud with serverless and pod options, Docker-based. Quick deploy with Ollama/vLLM templates. Self-hosting any model, no content restrictions A100, H100, H200, RTX 4090
Vast.ai GPU marketplace, cheapest cloud GPUs. Peer-to-peer rental model. Budget self-hosting Consumer to datacenter GPUs
Lambda On-demand GPU cloud for AI. Enterprise-grade infrastructure. Production workloads A100, H100, H200
Local Deployment
Stack Description GPU Required
Ollama One-command local LLM deployment. Easiest setup for GGUF models. Consumer GPU (6-24 GB)
llama.cpp C/C++ inference engine for GGUF. CPU+GPU hybrid, maximum hardware flexibility. Flexible (CPU-only possible)
vLLM High-throughput inference engine. PagedAttention for efficient memory. Datacenter GPU
SGLang Structured output + agentic workflow engine. RadixAttention for multi-turn. Datacenter GPU
LM Studio GUI-based local LLM runner. Drag-and-drop GGUF loading. Consumer GPU
Quick Reference
# Model Params Context VRAM Method Vision Tools License
1 DeepHat V2 7B/32B 131K ~6 GB SFT 1.7M samples No Yes Apache 2.0
2 BugTrace Apex 26B MoE 32K ~16 GB SFT HackerOne No Yes Apache 2.0
3 BugTraceAI Ultra 27B 4K ~22 GB SFT Unsloth No Yes Apache 2.0
4 CYBER-FROST ~180B MoE 262K Multi-GPU Security FT No Yes Qwen CL
5 CyberPal 2.0 20B 8K ~42 GB SFT 403K No No Apache 2.0
6 Cyber-Prime 1.1 2.6B N/A ~6 GB SFT+RL 75K No No LFM Open
7 Cyber-Ornith 9B 128K ~7 GB Obliteration No Yes Apache 2.0
8 Dolphin3-Cyber 8B 2K/131K ~6 GB LoRA on Dolphin3 No No Llama 3.1
9 Imperum 34B/3B MoE 16K ~22 GB SFT 10+ domains No Yes Apache 2.0
10 Lily-Cyber 7B 8K ~6 GB SFT 22K pairs No No Apache 2.0
11 pentest-v2 8B 32K ~6 GB LoRA 2.8K No No Apache 2.0
12 Qwythos-9B 9B 1M ~7 GB Post-train 500M tok Yes Yes Apache 2.0
13 RavenX-CyberAgent 36B/3B MoE 262K ~24 GB SFT 745K, 12 rounds No Yes Apache 2.0
14 REDCELL-26B 26B/4B MoE 262K ~12 GB LoRA 6.5K OSINT No No Apache 2.0
15 VEXT Pentest-7B 7B 8K ~6 GB QLoRA SFT+DPO No No Apache 2.0
16 security-slm 1.5B 32K ~2 GB Unsloth SFT No No Apache 2.0
17 Qwen3.8-27B 27B 262K ~18 GB Abliteration 131 mat Yes Yes Apache 2.0
18 GLM-5.3-Flash 320B/18B 1M ~80 GB+ Abliteration Yes Yes MIT
19 GLM-5.3-CYBER 753B ~131K 8xH200 Weight modification No Yes MIT
20 DS-V4.1-Flash MoE base ~1.1 GB overlay Abliteration overlay Yes Yes MIT
21 Huihui-Qwen3.5 27B 128K ~18 GB Abliteration No Yes Apache 2.0
22 Qwen2.5-Coder-32B 32B 128K ~20 GB Abliteration No No Apache 2.0
23 Qwen3.8-Cyber 27B 262K ~18 GB Abliteration+cyber Yes Yes Apache 2.0
24 HIDra-30B-A3B 30B/3B 128K ~20 GB Abliteration No Yes Apache 2.0
25 qwen25_UNCENSORED ~7B 32K ~6 GB Progressive FT No No Apache 2.0
26 Dolphin-Llama3 8B 8K ~6 GB Data filtering No No Llama 3
27 Wizard-Vicuna-13B 13B 2K ~10 GB Data filtering No No Other
Glossary
Abliteration: Weight-level intervention (Arditi et al. 2024) that orthogonalizes the refusal direction out of the residual stream, removing alignment constraints without retraining Obliteration: Variant of abliteration with similar weight-intervention approach SFT: Supervised Fine-Tuning on domain-specific data QLoRA: Quantized Low-Rank Adaptation, memory-efficient fine-tuning DPO: Direct Preference Optimization MoE: Mixture of Experts, only a subset of parameters active per token MTP: Multi-Token Prediction, speculative decoding for faster inference GGUF: Quantized format for llama.cpp / Ollama deployment FP8: 8-bit floating point quantization BF16: Brain floating point 16-bit, standard training/inference format Q4_K_M: 4-bit quantization with k-quants (medium), good balance of quality/speed Q6_K: 6-bit quantization with k-quants, higher quality than Q4 RATH: RavenX Attack, Threat & Hunt protocol (6-step autonomous security assessment) APEX: Domain-weighted quantization using importance matrices from training corpus imatrix: Importance matrix quantization, preserves domain-critical weights during compression CyberBench: Benchmark suite for cybersecurity models (CyNER, APTNER, CyNews, SecMMLU, CyQuiz, Email Phishing, HTTP Attack Log)
Deployment Stacks
Stack Best For GPU Required
Ollama Local dev, quick testing Consumer GPU (6-24 GB)
llama.cpp GGUF models, CPU+GPU hybrid Flexible
vLLM Production serving, high throughput Datacenter GPU
SGLang Agentic workflows, structured output Datacenter GPU
Transformers Research, custom pipelines Any
LM Studio Desktop GUI, drag-and-drop Consumer GPU
Sources
HuggingFace model cards (all specifications) Open LLM Leaderboard