Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world pri...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models
Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world pri...
Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files, and querying...
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Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 29
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 0
Practical Impact Score 20
Novelty Interest Score 70
Consequence Score 34
Curiosity Score 16
Shareability Score 45
Topics
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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