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MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks

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arXiv:2605. 18919v1 Announce Type: new Abstract: Evolutionary algorithms for adversarial attacks leverage population-based search to discover perturbations without gradient information, but suffer from inefficient crossover operations that destroy adversarial properties through discrete interpolation.

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Authors: Hyo Seo Kim, Gang Luo, Can Chen, Binghui Wang, Yue Duan, Ren Wang
Released: 2026-05-20 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks 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 "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, rate, without. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.18919

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