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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Echelon enables auditable aggregate-only adaptation of language models across privacy boundaries by training locally and sharing only boundary-level aggregates, achieving competitive performance in 1B LoRA experiments.
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.
citing papers explorer
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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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Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries
Echelon enables auditable aggregate-only adaptation of language models across privacy boundaries by training locally and sharing only boundary-level aggregates, achieving competitive performance in 1B LoRA experiments.
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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.