Researcher Built Automated Bot to Engage Internet Trolls
The project used a local LLM to redirect online harassment, aiming to shield vulnerable users from serial attackers.
Updated on Oct. 5, 2026 in Cybersecurity
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Security researcher Marcus Hutchins developed and ran a script designed to automatically reply to serial social media harassers. The bot used a local large language model to generate responses intended to keep trolls occupied.
Why it matters
The project serves as a defensive experiment to divert aggressive behavior away from individuals less equipped to manage online abuse. By automating engagement, the tool forces serial harassers to interact with a system rather than real targets.
The system relied on a local LLM to generate calm, confident replies, keeping trolls engaged for extended periods. This experiment lasted 3 days before the creator manually halted the bot to observe troll reactions.
The players
Marcus Hutchins
A security researcher well-known for his role in neutralizing the 2017 WannaCry global cyberattack.
The details
The software identifies serial harassers by monitoring and adding them to a watchlist after they leave abusive comments. These comments are then fed into a local LLM—a machine learning model that runs on a user's own hardware rather than a cloud server—which generates automated, steady-toned replies. The mechanism is designed to aggravate trolls and guarantee that they continue their engagement, thereby preventing them from bothering other users.
Timeline
May 2017: Marcus Hutchins stopped the WannaCry malware attack.
2020: A Wired article was published detailing the life of Hutchins.
Late September 2026: The troll bot experiment was conducted for 3 days.
Early October 2026: The project reveal was posted to Threads.
The Tech Race
The story follows a trajectory established by the 2017 WannaCry cyberattack, marking a shift from large-scale malware defense to individualized social engineering countermeasures. This experiment highlights a new front in the race between defensive automation and toxic online behavior.
The bot code has not been released, meaning this tool is currently unavailable for general use or public implementation. While the project proves that automated diversion is technically possible, future adoption remains limited by platform terms of service.
The takeaway
This experiment demonstrates the potential for local AI to act as a buffer against targeted digital abuse. Watch for future research papers or developer discussions regarding the efficacy of automated engagement tools in mitigating social media harassment.
Further reading
For more on evolving threat landscapes and defensive tools, visit Cybersecurity.
Source note: This article includes information reported by Cybernews.
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