One Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries
arXiv:2605.14605v2 Announce Type: replace-cross Abstract: Model providers increasingly release open weights or allow users to fine-tune foundation models through APIs. Although these models are safety-aligned before release, their safeguards can often be removed by fine-tuning on harmful data. Recent defenses aim to make models robust to such malicious fine-tuning, but they are largely evaluated only against fixed attacks that do not account for the defense. We show that these robustness claims are incomplete. Surveying 15 recent defenses, we identify several defense mechanisms and show that t
The proliferation of open source and API-driven foundation models has created new attack surfaces, making the security of fine-tuning mechanisms a critical and immediate concern.
This highlights fundamental vulnerabilities in AI model security, suggesting that current defense strategies against malicious modifications are insufficient and can be bypassed.
The assumption that fine-tuned models retain foundational safety alignments is challenged, necessitating a re-evaluation of model release strategies and security protocols.
- · AI Red Teams
- · Cybersecurity providers specializing in AI
- · Model auditing platforms
- · Open-weight AI model providers
- · API-driven foundation model platforms
- · Users relying on existing safety measures
Increased scrutiny and demand for more robust AI safety and security mechanisms for fine-tuning.
Potential for new regulations or industry standards governing the release and modification of foundation models.
A shift towards more 'black box' or restricted access models, or more complex trust frameworks for AI deployment.
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Read at arXiv cs.LG