# Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers

arXiv: https://arxiv.org/abs/2502.05727 · HTML: https://mlsanity.com/paper/2502.05727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 12 of 12)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems](https://mlsanity.com/paper/2007.01002/) (2020)
- [End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch](https://mlsanity.com/paper/2304.11726/) (2023)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [Learning to Solve Optimization Problems with Hard Linear Constraints](https://mlsanity.com/paper/2208.10611/) (2022)
- [Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks](https://mlsanity.com/paper/1904.06606/) (2019)
- [DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility](https://mlsanity.com/paper/2009.03147/) (2020)
- [BrainWash: A Poisoning Attack to Forget in Continual Learning](https://mlsanity.com/paper/2311.11995/) (2023)
- [Teaching Networks to Solve Optimization Problems](https://mlsanity.com/paper/2202.04104/) (2022)
- [Data Poisoning: An Overlooked Threat to Power Grid Resilience](https://mlsanity.com/paper/2407.14684/) (2024)

## Abstract (arXiv)

The increased integration of clean yet stochastic energy resources and the growing number of extreme weather events are narrowing the decision-making window of power grid operators. This time constraint is fueling a plethora of research on Machine Learning-, or ML-, based optimization proxies. While finding a fast solution is appealing, the inherent vulnerabilities of the learning-based methods are hindering their adoption. One of these vulnerabilities is data poisoning attacks, which adds perturbations to ML training data, leading to incorrect decisions. The impact of poisoning attacks on learning-based power system optimizers have not been thoroughly studied, which creates a critical vulnerability. In this paper, we examine the impact of data poisoning attacks on ML-based optimization proxies that are used to solve the DC Optimal Power Flow problem. Specifically, we compare the resilience of three different methods-a penalty-based method, a post-repair approach, and a direct mapping approach-against the adverse effects of poisoning attacks. We will use the optimality and feasibility of these proxies as performance metrics. The insights of this work will establish a foundation for enhancing the resilience of neural power system optimizers.