When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents
Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks. Traditional LLM Agents typically obtain information through webpages, documents, databases, or external tools and generate corresponding invocation sequences according to user goals; when this technology is further integrated with robotic systems, large language models begin to undertake funct
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- PossiblePossibly related (embedding) · 64%Large Language Models (LLMs): Transforming Enterprise AI Development - nerdbot →
- PossiblePossibly related (embedding) · 63%Large Language Models (LLMs): Transforming Enterprise AI Development - nerdbot.com →
- PossiblePossibly related (embedding) · 63%Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers →
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “When State Becomes an Attack Surface: State-Semantic Injecti” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “When State Becomes an Attack Surface: State-Semantic Injecti” ≈ “Unity-Technologies/ml-agents””
- FuzzySimilar name plus overlapping authors · 67%TauricResearch/TradingAgents →
“Title similarity 0.73; shared authors: xiao”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”