SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
arXiv:2605.28030v1 Announce Type: new Abstract: Fine-tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine-tuning attacks in which adversarial data removes safeguards and induces unsafe behaviors. We propose SPARD, a defense framework that integrates Safety-Projected Alternating optimization with Relevance-Diversity aware data selection. SPARD employs SPAG, which optimizes alternatively between utility updates and explicit safety projections with a set of safe data to enforce safety constraints. To curate safe data, we introduce a Rel
As AI models become more ubiquitous and powerful, the need to secure them against adversarial attacks and ensure their safety alignment is critical for broad adoption and trust.
The proliferation of harmful fine-tuning attacks threatens the reliability and ethical deployment of large language models, necessitating robust defense mechanisms to maintain model integrity and public safety.
The development of SPARD introduces a method for proactively defending AI models against harmful fine-tuning, potentially raising the bar for AI safety and trust in deployed systems, and making adversarial attacks more difficult and costly.
- · AI developers focused on safety
- · Organizations deploying LLMs in critical applications
- · AI security researchers
- · Adversarial AI attackers
- · Organizations with lax AI security postures
- · Harmful content creators leveraging compromised LLMs
AI models protected by SPARD will exhibit greater safety alignment and robustness against adversarial manipulation, strengthening their real-world utility.
Increased trust in AI systems could accelerate adoption across sensitive sectors, but attackers will evolve new methods, leading to an ongoing AI security arms race.
The necessity for such sophisticated defenses might spur regulatory bodies to mandate specific safety protocols for AI deployment, shaping future AI development standards.
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Read at arXiv cs.LG