INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 7/17/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment arXiv:2607.15081v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on domain-specific datasets has become a standard paradigm for adapting LLMs to specialized applications. However, recent work has shown that even fine-tuning on benign task-specific data can substantially weaken the safety capabilities of LLMs. While existing efforts have made progress in iden…
https://arxiv.org/abs/2607.15081
sha256:00f9f448b8dc88ead68bbde64d72adef780cf97dd7f7caf11642e14b0a5ef7cd
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