SIGIL cryptographically seals the audit-runtime gap for LLM skills via an on-chain registry with four publication types, DAO vetting, and a runtime verification loader that enforces integrity and permissions.
MindGuard: Intrin- sic decision inspection for securing LLM agents against metadata poisoning
7 Pith papers cite this work. Polarity classification is still indexing.
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MCP-DPT creates a defense-placement taxonomy that organizes MCP threats and defenses across six architectural layers, revealing mostly tool-centric protections and gaps at orchestration, transport, and supply-chain layers.
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%.
Descriptor-level manipulation in the Model Context Protocol can drive LLMs to unsafe tool selections in up to 36% of cases; a layered defense of integrity checks, auxiliary-LLM vetting, and runtime guardrails reduces this to 15% and raises blocking to 74%.
Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.
Tool Attention cuts tool-related tokens by 95% and raises context utilization from 24% to 91% in a 120-tool simulation via dynamic gating and lazy loading.
CASCADE is a cascaded hybrid detector that combines fast regex/entropy filtering, BGE embeddings with local LLM fallback, and output pattern checks to achieve 95.85% precision and 6.06% false-positive rate against prompt injection and related attacks in MCP-based systems.
citing papers explorer
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Sealing the Audit-Runtime Gap for LLM Skills
SIGIL cryptographically seals the audit-runtime gap for LLM skills via an on-chain registry with four publication types, DAO vetting, and a runtime verification loader that enforces integrity and permissions.
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MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security
MCP-DPT creates a defense-placement taxonomy that organizes MCP threats and defenses across six architectural layers, revealing mostly tool-centric protections and gaps at orchestration, transport, and supply-chain layers.
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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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Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation
Descriptor-level manipulation in the Model Context Protocol can drive LLMs to unsafe tool selections in up to 36% of cases; a layered defense of integrity checks, auxiliary-LLM vetting, and runtime guardrails reduces this to 15% and raises blocking to 74%.
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Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols
Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.
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Tool Attention Is All You Need: Dynamic Tool Gating and Lazy Schema Loading for Eliminating the MCP/Tools Tax in Scalable Agentic Workflows
Tool Attention cuts tool-related tokens by 95% and raises context utilization from 24% to 91% in a 120-tool simulation via dynamic gating and lazy loading.
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CASCADE: A Cascaded Hybrid Defense Architecture for Prompt Injection Detection in MCP-Based Systems
CASCADE is a cascaded hybrid detector that combines fast regex/entropy filtering, BGE embeddings with local LLM fallback, and output pattern checks to achieve 95.85% precision and 6.06% false-positive rate against prompt injection and related attacks in MCP-based systems.