Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same treatment. We formalize agent reconnaissance by modeling the process and identify...
VQV Signal
SECURITY
SOURCE-BACKED
GENERAL
Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents
Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same treatment. We formalize agent reconnaissance by modeling the process and identify...
Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same treatment. We formalize agent reconnaissance by modeling the process and identifying the knowledge assets it seeks to extract: what they...
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Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 22
Category SECURITY
Reader Depth GENERAL
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 0
Novelty Interest Score 70
Consequence Score 18
Curiosity Score 16
Shareability Score 41
Topics
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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