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Landseer: Exploring the Machine Learning Defense Landscape

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arXiv:2605. 27148v1 Announce Type: new Abstract: Machine learning systems face diverse threats that undermine robustness, privacy, and fairness.

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Authors: Ayushi Sharma, Rosemary Agbozo, Santiago Torres-Arias, Zahra Ghodsi
Released: 2026-05-27 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Landseer: Exploring the Machine Learning Defense Landscape overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: under, eval, position. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.27148

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