Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance
arXiv:2606.15531v1 Announce Type: new Abstract: Fine-tuning aligned language models on benign tasks (e.g. math tutoring) systematically breaks safety guardrails, even when training data contains no harmful content. While mechanistic approaches have shed light on where alignment resides in model weights, they do not by provide a general formal framework for deriving guarantees about when fine-tuning degrades it -- leaving the field without principled tools for predicting or preventing alignment collapse. We develop a local geometric framework through geometric analysis of parameter-space trajec
The rapid deployment of large language models makes understanding and mitigating their failure modes, particularly 'alignment collapse' during fine-tuning, a critical and immediate research priority.
This research provides a foundational framework for predicting and preventing alignment collapse in fine-tuned AI models, a major barrier for safe and reliable AI deployment, especially for sensitive applications.
The ability to systematically break safety guardrails via benign fine-tuning, and the development of a framework to understand this, means future AI development can incorporate more principled safety mechanisms.
- · AI safety researchers
- · Organizations deploying large language models
- · AI ethics and governance bodies
- · Malicious actors exploiting AI vulnerabilities
- · Organizations with inadequate AI safety protocols
Increased robustness and trustworthiness of AI systems as methods for preventing alignment collapse are adopted.
Reduced risk of AI models developing unintended harmful behaviors, allowing for broader deployment in sensitive sectors.
Potential for new regulatory frameworks and industry standards centered around 'alignment guarantees' for AI systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG