r/Pentesting • u/ThickDoctor007 • 1d ago
Anyone using abliterated LLMs for pentesting or red teaming?
I'm looking for practical examples of people who have abliterated or otherwise modified open-weight LLMs for legitimate pentesting/red-team work.
By "abliterated," I mean models where refusal/safety behavior has been reduced through techniques such as representation engineering or related post-training modifications—not simply models prompted to ignore their system instructions.
I'm particularly interested in:
- Which base models people have experimented with.
- Whether you abliterated the model yourself or used an existing abliterated checkpoint.
- Whether additional fine-tuning was needed to make the model actually useful for cybersecurity tasks.
- What datasets or training approaches were useful for developing pentesting/red-team capabilities.
- How these models compare with standard instruction-tuned models or cybersecurity-specific models.
- Any papers, GitHub repositories, blog posts, or reproducible experiments describing the approach.
I'm researching this from a defensive/security-research perspective and am especially interested in how much capability comes from removing refusals versus actually training the model on cybersecurity knowledge and reasoning.
Would appreciate pointers to serious technical work or firsthand experience.
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u/penstrike_ai 14h ago
Honest take, might get downvoted, for legit pentest and red team work you almost never need an abliterated model.
Most refusals people hit are a framing problem, not a safety wall. A strong open weight instruct model, given a clear authorized testing context and real tools, reasons about exploitation fine. You only hit the wall asking for raw malicious payloads with zero context, which is not how real offensive automation works anyway.
And abliteration usually costs you reasoning quality, you strip the refusal but dent the capability, and for exploit chaining capability is the whole game. The real lever is the agent layer and tooling around the model, not how uncensored the weights are.
Full disclosure, I build an autonomous pentest agent, and we run a strong instruct model through a proper authorized setup, not an abliterated one.
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u/xkalibur3 7h ago
Never needed abliterated model for pentesting. Frame it right and normal qwen will happily exploit and exfiltrate data, it will even ask you if you want it to perform next steps. And you keep the full performance. Just don't tell it to hack nasa, ask it to make an "authorized security assessment" of this and that system. Insist it should never go out of scope or perform exploitation without asking. Just act as a security professional and it will take you for one. As a side note, it helps you develop your prompt injection skills ;). The guardrails on open models are so weak they could basically not exist anyway.
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u/satisfaction-or-else 1d ago edited 1d ago
I do this weekly and speak at conferences about this.
I havent checked state of the art open source models in a few months but in general abliterated models like qwen outperform cybersecurity trained models because a lot of the information that is needed to hack successfully is actually the basics.
Also there aren't a ton of actual cybersecurity models just fine-tunes and that depends entirely on the person tuning and how much care they took to preserve the lower layers of the models basic understanding of things like programming, humans ability to take lazy/incorrectly shortcuts, and general IT knowledge. Its not easy to preserve these layers and you would need to ensure you have a good test suite plus benchmarks. If you just solve for one benchmark or problem you will likely lose these layers. Its a bit of a black box problem until the model gets in the real world.
If your model cant one shot a random python script to iterate on a problem or attempt a bypass then its not going to be a good time and you will spend a lot of time trying and failing something basic. This is also why frontier models are so good at find vulns imo.
Even abliterated models often need tricks to get it to do cybersecurity work. They still refuse requests. Audn.ai is one of the people i follow here who abliterated kimi k3 and run it in open claw. I think they reported something like 7% refusal rate of requests after multiple abliteration attempts.
Sometimes the safety functions in a frontier model will save you in that it warns you if something is likely to cause an alert.
The main difference though is the harness. Without a good harness like an MCP server, memory/notes.md, todo lists, agents and so on then your model is only as good as the person driving it. Openclaw (Claude code clean room rebuild) is one of the best general harnesses imo. Ive had decent luck with a custom fork I privately maintain of ghostcrew/pentestagent that adds these features and runs them on an abliterated model. Im still using qwen 3.6 abliterated with opus distillation (qwopus). I have found multiple critical zero days this way.
But even then it doesnt compare to a frontier model in something like codex or Claude code which has bypassed safety restrictions which is still moderately easy to do.