Uncensored and Offensive Security AI Models Benchmark
A curated GitHub list of open-weight "uncensored" and security-fine-tuned LLMs intended for authorized red teaming, pentesting, and security research, covering models like WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, and several abliterated Qwen/GLM/DeepSeek variants. It explains the underlying techniques (abliteration per Arditi et al. 2024, QLoRA/DPO fine-tuning, GGUF/FP8 quantization) and links to Hugging Face model cards, benchmark suites (CyberBench, TrustedSec's LLM Attack Benchmark vs OWASP Juice Shop), and deployment platforms for running these models locally or via API.
What it is
A 27-entry model roster plus a glossary covering the specific mechanics behind "uncensoring" techniques — most notably abliteration, the method from Arditi et al. 2024 that orthogonalizes the refusal direction out of a model's residual stream, removing alignment guardrails without any retraining. The page distinguishes this from related terms the summary doesn't surface: "obliteration" (a similar weight-intervention variant), APEX/imatrix (importance-matrix-weighted quantization that preserves domain-critical weights during compression), and RATH (RavenX's named 6-step autonomous security-assessment protocol used by one of the listed models).
How it works
Abliteration works at the weight level: it identifies the direction in the residual stream responsible for refusals and projects it out, so the model loses its safety constraints while keeping its original weights otherwise intact — no fine-tuning pass required. This is distinct from the SFT/QLoRA/DPO-based models on the list, which are retrained on security-specific data rather than surgically stripped of refusal behavior. Quantization formats (GGUF, FP8, Q4_K_M, Q6_K) are a separate, orthogonal axis — they compress model size/speed and are applied on top of either abliterated or fine-tuned base models for local deployment via llama.cpp/Ollama.
Preconditions / Affected
Relevant to anyone evaluating or deploying these models for red-team tooling: MoE architectures (several listed models, e.g. REDCELL-26B-A4B) only activate a subset of parameters per token, affecting hardware sizing; models tagged BF16 vs FP8 vs GGUF/Q4_K_M/Q6_K require different VRAM and have different quality/speed tradeoffs for local inference.
Caveats
The page is a vendor/community roster, not an independent evaluation — benchmark sourcing (CyberBench, TrustedSec's 4,800-run comparison against OWASP Juice Shop, Open LLM Leaderboard v1) is cited only by name per-model via external model cards and release notes, with no scores reproduced on the page itself. Several entries (e.g. "qwen25_UNCENSORED_03-C") link to a search query rather than a specific model card, and one entry (#23) links to the unmodified base Qwen3.8-27B with only a note that "community abliterated variants" exist — meaning not every roster item is independently verifiable from the page alone.
Organisations named here
- Arditi et al. 2024
- WhiteRabbitNeo
Technologies named here
- DeepHat V2
- OWASP Juice Shop
- BugTraceAI-CORE-Apex
- BugTraceAI-CORE-Ultra
- CYBER-FROST-3.8
- CyberPal2.0-20B
- Cyber-Prime 1.1
- Cyber-Ornith-1.5-9B
- Dolphin3-Cyber-8B
- Imperum-CybersecurityLLM v1.0
- Lily-Cybersecurity-7B v0.2
- pentest-v2
- Qwythos-9B
- RavenX-CyberAgent
- REDCELL-26B-A4B
- VEXT-Pentest-7B
- security-slm-unsloth-1.5b
- Qwen3.8-27B-Uncensored-OrcaRouter
- GLM-5.3-Flash-Uncensored-FP8
- GLM-5.3-CYBERSECURITY-FP8
- DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed
- Qwen3.5-27B-abliterated
- Qwen2.5-Coder-32B-Instruct-abliterated
- Qwen3.8-27B-Cyber-agentic
- HIDra-30B-A3B
- qwen25_UNCENSORED_03-C
- Dolphin-Llama3-8B
- Wizard-Vicuna-13B-Uncensored
- Featherless AI
- OrcaRouter
- TrustedSec LLM Attack Benchmark
- CyberBench
- Hugging Face
- Qwen
- DeepSeek
- GLM
- LLaMa
- Ollama
Techniques named here
- Abliteration
- Obliteration
- SFT
- QLoRA
- DPO
- RATH
- APEX
- imatrix
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