arXiv (All)
open access
Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an assumption that new harmful behavior is learned through fine-tuning rather than elicited by jailbreaking the model. Yet, pretrained LLMs already encode
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content#1118416
- Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates#1116390
- Towards Effective Black-Box Adversarial Attacks on Deep Code Models via Structural and Identifier Perturbations#1062134
- Breaking Adversarial Transferability in Fine-Tuned Speech Recognition#1233148
- ASCENT: First-Order Optimal Fine-Tuning with Recalibration for Safety--Utility Co-Enhancement#1217796
Record · ID 1232744
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive