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Experiment: Porting a PLC Exploit With AI Takes Hours and Hundreds of Dollars

Feeds.Feedburner •Eduard Kovacs • September 1, 2026

Researchers at Forescout’s Vedere Labs used Anthropic’s Claude to port a working remote code execution (RCE) exploit from one WAGO programmable logic controller (PLC) to another. They succeeded but only after extensive researcher oversight, several hours of dedicated work, and hundreds of dollars in API costs.

The starting point was a previously developed exploit for the WAGO 750-852 PLC, based on CVE-2021-31886 , a pre-authentication buffer overflow in the Nucleus FTP server that allows an unauthenticated attacker to execute arbitrary ARM shellcode on the targeted PLC.

For their experiment, Forescout researchers set out to adapt that exploit to a related but distinct model, the WAGO 750-831, and to see whether the AI could then push the result further into a full command-and-control implant. The experiment was conducted in the wake of the recent attacks targeting PLCs in the water sector.

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The researchers used Claude Code, giving it access to a terminal, reference files, the reverse-engineering tool Ghidra, and the physical target device. The AI confirmed the vulnerability through a mix of live probing and static firmware analysis before generating a payload that crashed the PLC.

The crash confirmed the flaw was present, but this was the easy part of the exercise. Turning it into controlled code execution took far longer, and early attempts sent the AI chasing incorrect leads, requiring researchers to redirect its analysis and provide additional technical context.

Progress stalled until the team switched from Claude Sonnet 4.6 to Claude Opus 4.6 and instructed the model to ask for help whenever it was uncertain a firmware detail. The breakthrough came when the AI figured out why the injected code kept getting erased before it could run, then adjusted its approach to keep the payload intact.

Once that obstacle was cleared, Claude produced two separate working payloads within 12 minutes. The researchers note that this pattern — a difficult, time-consuming path to initial execution followed by rapid iteration afterward — is what makes the cost of scaling this kind of work worth watching.

A follow-up attempt to build on the RCE went less smoothly. In a new session aimed at developing a command-and-control implant, Claude tested increasingly complex payloads, and one of those payloads wrote to a region mapped to the PLC’s flash memory, permanently bricking the device.

As for the costs associated with the experiment, Forescout noted that the final stage of RCE development alone consumed over $500 in API usage across a session lasting more than eight hours.

“One could argue that the [researcher guiding the AI] could have achieved the initial RCE port without AI in less time and at lower cost while also keeping the PLC alive,” Forescout noted , adding, “That is true right now, but the more important question is what happens as the amount of expert intervention required continues to fall. A human researcher can parallelize this work, but AI has the potential to reduce the marginal cost of doing so across many related targets at once.”

Related : Hackers Using AI to Target Siemens PLCs in Critical US Sectors

Related : CISA Urges Water Sector to Protect OT After Coordinated Attacks on PLCs

Related : CISA: Over 100 Internet-Exposed Water Systems Targeted in July Cyberattacks

Eduard Kovacs (@EduardKovacs) is senior managing editor at SecurityWeek. He worked as a high school IT teacher before starting a career in journalism in 2011. Eduard holds a bachelor’s degree in industrial informatics and a master’s degree in computer techniques applied in electrical engineering.

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AI-Powered Exploitation of PLCs Raises Concerns in Cybersecurity Researchers at Forescout’s Vedere Labs successfully used AI to port a remote code execution (RCE) exploit from the WAGO 750-852 PLC to the WAGO 750-831 PLC, targeting CVE-2021-31886. The experiment required significant human oversight, taking over eight hours and costing approximately $535 in API usage. The AI confirmed the vulnerability through live probing and static analysis, but initial attempts to create a functional exploit faced challenges, leading to a switch in AI models. Once operational, the AI produced multiple payloads quickly, but an attempt to enhance the exploit resulted in bricking the PLC. The findings highlight the potential for AI to automate parts of the exploit development process, raising alarms about the accessibility of such attacks against critical infrastructure. The experiment follows recent attacks on PLCs in the water sector, emphasizing the urgency of addressing these vulnerabilities. 17 sources 52

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Experiment: Porting a PLC Exploit With AI Takes Hours and Hundreds of Dollars

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