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%.
Attenmia: Llm membership inference attack through attention signals
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Attention layers in tabular foundation models enable effective membership inference attacks via pattern concentration, addressed by an inference-time k-anonymity defense on high-risk queries that cuts leakage by ~50% with minimal utility loss.
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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Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries
Attention layers in tabular foundation models enable effective membership inference attacks via pattern concentration, addressed by an inference-time k-anonymity defense on high-risk queries that cuts leakage by ~50% with minimal utility loss.