arXiv:2602.17975v2 Announce Type: replace Abstract: This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations. We demonstrate this capability on an instance of the CANOS-PF graph neural network model, as implemented by the PF$\Delta$ benchmark library, operating on a 14-bus test grid. Generated adversarial points yield errors as large as 3.7 per-unit in reactive power and 0.08 per-unit in voltage magnitude. When minimizing the perturbation from a
The increasing reliance on AI models for critical infrastructure monitoring and control, especially power grids, makes understanding their vulnerabilities a present concern.
This research highlights the significant security risks associated with deploying AI in critical energy infrastructure, demonstrating how subtle adversarial attacks can lead to substantial errors in power flow predictions.
The focus of ensuring grid stability now extends to securing the AI models predicting power flow against sophisticated data perturbations, introducing new cybersecurity challenges for energy systems.
- · Cybersecurity firms specializing in AI/ML security
- · Researchers in adversarial AI and critical infrastructure protection
- · Regulatory bodies developing AI safety standards for utilities
- · Power grid operators with unhardened AI systems
- · Developers of unsecure AI models for critical infrastructure
- · Energy consumers in areas with vulnerable grids
Increased investment in adversarial machine learning research specifically targeting critical infrastructure AI.
New regulatory mandates for AI model robustness testing and security audits before deployment in energy grids.
The development of 'AI-proof' or 'adversarial immune' power grid control systems as a market differentiator.
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