LLMs-Finetuning-Safety
Demonstrates safety risks in fine-tuning GPT-3.5 Turbo with adversarial examples
GraphCanon updated Oct 5, 2026 · GitHub synced Oct 5, 2026
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Decision brief
LLMs-Finetuning-Safety demonstrates the safety risks associated with fine-tuning GPT-3.5 Turbo using few adversarially designed examples.
Good fit when
- When evaluating the risk of compromised safety in language models after fine-tuning them on small, carefully crafted datasets.
- If performing research focused on how even low-cost ($0.20) fine-tunings through OpenAI's APIs can undermine pre-trained model safeguards.
Avoid when
- When generalizing safety risks to other large language models that have different underlying architectures or safeguard mechanisms than GPT-3.5 Turbo.
- If intending to use this tool as a method of fine-tuning any model for enhancing its performance on specific tasks, given it is designed for illustrating risk rather than improving capabilities.
- Pricing:
- freemium - Open-source under the MIT license; free to use and modify. OpenAI API usage cost applies, but this repository demonstrates effects at less than $0.20.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (954d since push)
- As of Oct 5, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 5, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install LLMs-Finetuning-SafetySimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Research on how fine-tuning a language model can inadvertently undermine its safety measures using few adversarially designed examples.
Capability facts
- Languages
- python
Source: github.language · Oct 5, 2026
Categories
Tags
README
<h1 align='center' style="text align:center; font weight:bold; font size:2.0em;letter spacing:2.0px;" Fine tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! </h1 <p align='center' style="text align:center;font size:1.25em;" <a href="https://unis...
For agents
This page has a .md twin and JSON over the API.