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
| 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
| 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
| 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
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 |
| 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 |
| 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 |
| # |
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 |
- 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)
| 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 |
- HuggingFace model cards (all specifications)
- Open LLM Leaderboard v1 (Wizard-Vicuna benchmarks)
- OrcaRouter release notes (abliteration details, compliance rates)
- WhiteRabbitNeo/Kindo publications (USENIX Security 2024)
- Empero AI model card (Qwythos benchmarks)
- Eric Hartford / Cognitive Computations (Dolphin methodology)
- TrustedSec LLM Attack Benchmark (4,800 runs vs OWASP Juice Shop)
- Blackfrost-AI model card (CYBER-FROST architecture)
- deadbydawn101 model card (RavenX RATH protocol, training data)
- terrorswift model card (REDCELL OSINT methodology)
- cyber-pal-security publication (SecKnowledge 2.0 pipeline)
- BugTraceAI model card (Ultra tooling model design)
- IMPERUM model card (Imperum SOC/DFIR focus)
- Featherless AI (featherless.ai)
- OrcaRouter (orcarouter.ai)
- Reddit r/LocalLLaMA, r/netsec community reports
Joas A. Santos | Red Team Leaders | Sep 2026
For authorized security research and education only.
