# A pentesting model, with the refusals taken out ## TL;DR - CyberStrike's base card describes training for tool use with 300 examples. - Huihui applied an edit that strips a refusal-linked pattern from the fine-tuned model. - Huihui's CyberStrike derivative card reports neither refusal measurements nor a post-edit tool-use rerun. ## Basically, the facts **A fine-tune that admits it didn’t add knowledge** Basically, CyberStrike's base card describes targeted training for tool use, not a capability upgrade. **Then huihui-ai took the refusals out** Basically, Huihui's CyberStrike release applies an edit that strips a refusal-linked pattern from the fine-tuned model. **What abliterating a security model changes** Basically, Resecurity's July 2026 analysis described CyberStrike-OffSec as a dual-use model. **What the card does and doesn’t claim** Basically, Huihui's CyberStrike card has no measured refusal rate or post-edit tool-call evaluation. **The paper trail so far** Basically, the CyberStrike article recorded an easy-to-run file after a user asked for that format. **How we treat it** Basically, Huihui's CyberStrike publisher warns that the model has significantly reduced safety filtering. **The idea, in plain words** Basically, CyberStrike's base card reports 18 correct tool calls out of 24 after targeted training. _Published 10 August 2026 · Updated 6 September 2026 · 6 min read · Canonical: https://abliterated.cloud/blog/huihui-cyberstrike-offsec-35b-abliterated/_ Published 10 August 2026. Exact artifact: `huihui-ai/Huihui-CyberStrike-OffSec-35B-abliterated`, revision `01521758ee85df1ed4edaf494c48e20704b80204`. CyberStrike-OffSec-35B is a fine-tune of Qwen3.6-35B-A3B built by Orhan Yıldırım (oyildirim) to emit structured tool calls for the CyberStrike offensive-security harness. Its card calls the work “a small, targeted alignment, not a capability upgrade”: a 300-example dataset that fixed the tool-call collapse of the previous model, which had hallucinated whole engagements. On 10 August 2026, huihui-ai abliterated the fine-tune — a refusal-direction weight edit per Arditi et al., implemented as a crude proof of concept with remove-refusals-with-transformers — and restored the Qwen3.6 MTP module: 35,951,822,704 parameters versus the fine-tune’s 35,107,181,936. The base’s A/B tool-call evaluation (18/24 genuine structured calls versus the previous model’s 0/24) is an upstream claim; the abliterated card publishes no refusal-rate measurement and no post-edit rerun. Third-party coverage (Resecurity, 30 July 2026) treats the OffSec model as dual-use: local GGUF execution without telemetry, weights that “lower the skill floor for adversaries.” ## The idea, in plain words **How a model learns to call tools instead of guessing** — Base models often hallucinate function calls. Supervised fine-tuning (SFT) fixes that with examples: here, a ~300-example dataset taught the model to emit structured tool calls, and the base card measured the difference — 18 of 24 correct tool calls after, 0 of 24 before. That's what 'tool-calling' means in practice: the model doesn't know tools, it was shown when and how to call them. Primary sources: - [Exact model card](https://huggingface.co/huihui-ai/Huihui-CyberStrike-OffSec-35B-abliterated) - [Pinned artifact](https://huggingface.co/huihui-ai/Huihui-CyberStrike-OffSec-35B-abliterated/tree/01521758ee85df1ed4edaf494c48e20704b80204) - [CyberStrike-OffSec-35B card](https://huggingface.co/oyildirim/CyberStrike-OffSec-35B) and [EVALUATION.md](https://huggingface.co/oyildirim/CyberStrike-OffSec-35B/blob/main/EVALUATION.md) - [Resecurity: When AI Becomes the Attacker](https://www.resecurity.com/blog/article/when-ai-becomes-the-attacker-understanding-autonomous-offensive-security-agents) - [Official Qwen3.6-35B-A3B card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) - [Refusal-direction paper](https://arxiv.org/abs/2406.11717) and [implementation linked by huihui-ai](https://github.com/Sumandora/remove-refusals-with-transformers) - [HF discussion: “GGUF of this model is needed”](https://huggingface.co/huihui-ai/Huihui-CyberStrike-OffSec-35B-abliterated/discussions/1) and [GGUF repo](https://huggingface.co/huihui-ai/Huihui-CyberStrike-OffSec-35B-abliterated-GGUF) - [Creator posts on LinkedIn](https://www.linkedin.com/posts/orhan-yildirim_cybersecurity-penetrationtesting-opensource-activity-7440165985984204800-ow_a) - [r/AgenticCybersecurity thread](https://www.reddit.com/r/AgenticCybersecurity/comments/1vi8hji/oyildirimcyberstrikeoffsec35b_hugging_face_i_dont/) and [r/Pentesting thread](https://www.reddit.com/r/Pentesting/comments/1qu4zbq/i_built_a_pentesting_platform_that_lets_al/) — linked, not quoted; Reddit blocked access at research time The publisher warns that safety filtering is significantly reduced. “Abliterated” means refusal-reduced — not authorized, not legal, and not harmless. ## Run this model on your terms Want this model running for you, on a private cloud GPU or your own machine? [Request access on Signal](https://signal.me/#p/+13103408213) or [see how it works](https://abliterated.cloud/#how). > Model research, dated at publication. Model licenses, publisher benchmarks and hosting estimates are specific to each article, not a live availability or price list. Reported zero-refusal results are test-specific, not a universal guarantee.