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Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
A new research survey synthesizes transferable attack mechanisms in AI systems, establishing a unified taxonomy for cross-model security risks.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 19 October 2026
- Collected by OneBench
- 20 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2311.11796v3 Announce Type: replace-cross Abstract: Transferable attacks allow adversarial manipulations or learned attack mechanisms constructed in one setting to compromise other AI systems, potentially reducing…
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OneBench interpretation
Institutional assessment
So what
Adversarial attacks developed against open or surrogate models can successfully exploit black-box proprietary systems deployed in production.
Do what
Review model risk and AI security frameworks against cross-model transferable attack vectors.