No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
Breaking secure aggregation: Label leakage from aggregated gradients in federated learning,
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LADSG is a unified defense framework that reduces success rates of passive, active, and direct label inference attacks in VFL by 30-60% via label anonymization, gradient substitution, and norm-based filtering.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
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LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning
LADSG is a unified defense framework that reduces success rates of passive, active, and direct label inference attacks in VFL by 30-60% via label anonymization, gradient substitution, and norm-based filtering.