TL;DR
A formal framework for security in agentic AI is developed, built around an ideal predicate ϕ that decides, for a given ( prompt, context, result ) triple, whether the agent’s behaviour is acceptable.
A Systematization of Knowledge (SoK) that reframes jailbreak security around the full agentic execution pipeline, and develops unified taxonomies of attacks and defenses spanning user interaction, planning and reasoning, memory, tool use, and inter-agent communication, introduces a security--utility--efficiency evaluat...
This position paper argues that EBRB could serve as a core safety and reasoning governor within LLM-based agentic pipelines, yielding hybrid systems to better align agentic AI systems with emerging regulatory and ethical requirements for Trustworthy AI (TAI) in high-risk settings.
This survey systematically analyze threat surfaces across intra-execution loops and interaction planes, formulate a multi-layered zero-trust defense-in-depth architecture integrating Dual-LLM isolation, Capability-Based Access Control, kernel eBPF probes, and sandboxed runtimes, and map technical controls to internatio...
This paper evaluated Niyam-AI on 2,000 real-world agent scenarios from Agent-SafetyBench and compared it against three existing safety approaches: NeMo Guardrails, Meta's Llama Prompt Guard 2, and OpenAI's GPT-OSS-Safeguard.
The right unit of safety in agentic AI is the trajectory-with-checkable-evidence, not the model, and this work formalizes an Agent Trajectory Schema and Evidence Chain, state a compositional gating proposition based on standard monitor composition, and outline a research agenda.
Artificial intelligence (AI) agents are rapidly evolving into autonomous systems capable of reasoning, acting, and continual evolving. Their increasing autonomy enables powerful real-world applications but also introduces security risks throughout the entire agent execution cycle. However, the security challenges arisi...
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