Data Poisoning: An Overlooked Threat to Power Grid Resilience
On the map: LLM safety and security → Backdoor and data poisoning attacks
Abstract (arXiv)
As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security. Builds on 12 of its 29 references that are in the map. First posted on arXiv 19 Jul 2024.
Builds on
12 of 29 references.
References · 12
- 2013Intriguing properties of neural networkscited by 5,114
- 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
- 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
- 2020Contextualized Perturbation for Textual Adversarial Attackcited by 136
- 2021Meta Gradient Adversarial Attackcited by 55
- 2021Optical Adversarial Attackcited by 43
- 2021Can You Hear It? Backdoor Attacks via Ultrasonic Triggerscited by 41
- 2021A Review of Adversarial Attack and Defense for Classification Methodscited by 29
- 2019Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attackscited by 18
- 2023BrainWash: A Poisoning Attack to Forget in Continual Learningcited by 9
- 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 6
- 2021Adversarially Robust Learning for Security-Constrained Optimal Power Flowcited by 5
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Citing papers per year: 2025: 1.
1 paper.