No major agentic AI framework complies with six safety containment principles; a memory poisoning attack on LangChain causes persistent targeted errors up to 88.9% wrongful denials and 3.5x increase under complex policies, fixed by two sub-millisecond validators.
Targeting the core: A simple and effective method to attack rag-based agents via direct llm manipulation
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GT-MCP coordinates three LLM agents via a trust function and rollback to bound contextual drift and block adversarial injections in multi-turn interactions.
REALISTA generates semantically coherent adversarial prompts via latent-space optimization over input-dependent editing directions, achieving stronger hallucination elicitation than prior realistic attacks on open-source and reasoning LLMs.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
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Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
GT-MCP coordinates three LLM agents via a trust function and rollback to bound contextual drift and block adversarial injections in multi-turn interactions.