@0x0SojalSec: fully uncensored Cybersecurity-specialized model tuned on exploits, pentesting and Run locally on MacBook, delivers exp…

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Summary

A fully uncensored cybersecurity-specialized AI model fine-tuned on exploits and pentesting data, designed to run locally on consumer hardware with multiple quantization options, offering expert offensive and defensive insights.

fully uncensored Cybersecurity-specialized model tuned on exploits, pentesting and Run locally on MacBook, delivers expert-level offensive & defensive insights. - Zero refusals - Exploit writing - vuln analysis - Fully uncensored - OWASP, and MITRE. - fine-tuned deep on real security data - 11 quants available from tiny Q2_K to full precision, Runs great on consumer hardware. Runs 100% offline on your laptop no refusals. Perfect for CTFs, bug bounties, blue/red teaming.
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Cached at: 07/12/26, 02:58 PM

fully uncensored Cybersecurity-specialized model tuned on exploits, pentesting and Run locally on MacBook, delivers expert-level offensive & defensive insights.

  • Zero refusals
  • Exploit writing
  • vuln analysis
  • Fully uncensored
  • OWASP, and MITRE.
  • fine-tuned deep on real security data
  • 11 quants available from tiny Q2_K to full precision, Runs great on consumer hardware.

Runs 100% offline on your laptop no refusals. Perfect for CTFs, bug bounties, blue/red teaming.

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A community member built RangeBench, an MIT-licensed cybersecurity benchmark where models get a shell in an isolated Docker box to capture flags across pwn, web, crypto, rev, forensics, and multi-stage ranges. Local Qwen3.8 27B scored 28.1% first-try and 0% on pwn, while cheap API models like MiMo 2.6 Flash and GPT-6 Luna reached 73.7% and 90.9%, highlighting the real-world risk of increasingly capable hacking models.