未审查及攻击性安全人工智能模型基准测试

未审查及攻击性安全人工智能模型基准测试
Uncensored and Offensive Security AI Models Benchmark

原始链接: https://github.com/JoasASantos/Offensive-Security-AI-Models

本报告收录了 27 个针对网络安全任务（如红队演练、渗透测试、开源情报分析及事件响应）进行优化的开源、无审查大语言模型（LLM）。截至 2026 年 9 月，这些模型采用了先进的去对齐技术——主要是“消融”（abliteration，即正交化拒绝向量）以及针对 CVE 报告、漏洞赏金披露和恶意软件分析报告等安全领域语料库的监督微调（SFT）。主要条目包括 RavenX-CyberAgent（RATH 协议）和 BugTraceAI-CORE-Ultra 等专业智能体，以及 Qwen3.8-27B 等通用去对齐模型。技术配置涵盖了适用于边缘设备的 1.5B 轻量化模型，到需要多 GPU 企业级基础设施支持的 750B+ 混合专家（MoE）架构模型。本列表提供了全面的部署指南，包括推荐的推理引擎（Ollama、vLLM、SGLang 和 llama.cpp）以及云服务提供商（RunPod、Vast.ai 和 OrcaRouter）。这些模型仅供授权的安全研究、红队行动及教育目的使用。通过剥离限制性的安全对齐，这些工具在兼容标准安全工作流和工具调用接口的同时，实现了更深层次的漏洞研究和自动化威胁猎杀。

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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.