Strategic agentic scambaiting modeled to reduce scammer profitability
Original title: The Promise of Strategic Agentic Scambaiting
Model shows scambaiting agents make scammer profits negative above prevalence thresholds while defender mimicry gains the arms-race advantage.
Scammers increasingly use AI agentic systems for scalable conversational fraud. Defensive scambaiting agents impersonate victims to extract threat intelligence like mule accounts and wallets. Formal model of agent interactions captures decision spaces, engagement dynamics, resource consumption and belief adaptation. Scambaiting prevalence drives scammer profitability negative under baseline parameters.
- Scammer profitability turns negative once baiting-agent prevalence exceeds thresholds
- Defender mimicry gains outpace equivalent scammer detection improvements
- Model analyzes resource consumption and disruption outcomes
- Scambaiting remains effective in agent-to-agent environments
From the original article
1 min read at arxiv.orgScammers have increasingly adopted AI-driven capabilities, evolving from automated message generation to fully agentic conversational systems that scale operations and maximize the exploitation of fraudulent resources before detection and takedown.
By Abhishek Satyam · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.