AgentBound is the first declarative access control framework for Model Context Protocol servers that generates policies from source code at 80.9% accuracy and blocks most threats in malicious servers with negligible overhead.
A new era in llm security: Exploring security con- cerns in real-world llm-based systems
8 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Trust-RAG Compass framework and TRC Bench benchmark to assess RAG trustworthiness across factuality, robustness, fairness, transparency, accountability, and privacy, with evaluations showing performance gaps between LLMs.
Multi-agent LLM frameworks can spread compromises across agent boundaries via insecure memory inheritance during subagent spawning.
LVLM-based agents exhibit trust boundary confusion with visual injections and a multi-agent defense separating perception from decision-making reduces misleading responses while preserving correct ones.
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
π-RAG uses π digits for transcendental addressing and projects queries onto Canonical Intent Centroids mapped via cryptographic salt to produce oblivious π-keys that point to data without exposing embeddings.
Flowr is an agentic AI framework that decomposes retail supply chain workflows into coordinated LLM-based agents with human-in-the-loop oversight to automate operations in large supermarket chains.
citing papers explorer
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AgentBound: Securing Execution Boundaries of AI Agents
AgentBound is the first declarative access control framework for Model Context Protocol servers that generates policies from source code at 80.9% accuracy and blocks most threats in malicious servers with negligible overhead.
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Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Introduces Trust-RAG Compass framework and TRC Bench benchmark to assess RAG trustworthiness across factuality, robustness, fairness, transparency, accountability, and privacy, with evaluations showing performance gaps between LLMs.
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When Child Inherits: Modeling and Exploiting Subagent Spawn in Multi-Agent Networks
Multi-agent LLM frameworks can spread compromises across agent boundaries via insecure memory inheritance during subagent spawning.
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If you're waiting for a sign... that might not be it! Mitigating Trust Boundary Confusion from Visual Injections on Vision-Language Agentic Systems
LVLM-based agents exhibit trust boundary confusion with visual injections and a multi-agent defense separating perception from decision-making reduces misleading responses while preserving correct ones.
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Progent: Securing AI Agents with Privilege Control
Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
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Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
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$\pi$-RAG: Oblivious Retrieval via Semantic Quantization and Transcendental Addressing for Large Language Models
π-RAG uses π digits for transcendental addressing and projects queries onto Canonical Intent Centroids mapped via cryptographic salt to produce oblivious π-keys that point to data without exposing embeddings.
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Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains
Flowr is an agentic AI framework that decomposes retail supply chain workflows into coordinated LLM-based agents with human-in-the-loop oversight to automate operations in large supermarket chains.