AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes ReGap, a data-free attack that recovers private associations using task structure, filters them by answer-token likelihood, and updates the target model via low-rank adaptation, which requires neither target answers nor auxiliary genuine private supervision.
SAVER is introduced, a transition-centered framework in which Substrate locates reusable influence, Adaptation captures how it changes, Violation identifies compromised safety attributes, Exposure marks where failures become observable, and Response assesses containment, repair, or revocation.
This work introduces PROSE (Provenance through Relational Organization of Semantic Expression), replacing fixed query sets with a target semantical domain and brittle response keys with semantic structures internalized as domain-conditioned response behavior.
SkillShift is presented, a constrained black-box framework for covert policy steering without explicit target command injection or task hijacking that combines semantically plausible policy edits with hierarchical validation, failure-guided optimization, and strategy compression to preserve effectiveness, output validi...
This study unveils the capability of attackers to generate adversarial policies even when restricted to partial observations of the victims in multi-agent competitive environments, and proposes a novel black-box attack (SUB-PLAY) that incorporates the concept of constructing multiple subgames to mitigate the impact of...
This work forms this problem as backdoor generalization under training--inference trigger shift and introduces Lilith, a black-box anchor-to-family framework that achieves high family-wise attack success with limited utility degradation and a small trigger generalization gap.
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