Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards

The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exposes new vulnerabilities to prompt-based adversarial attacks. This paper evaluates the risk of jailbreaking LLMs, i.e., circumventing safety alignments to produce outputs violating regulatory standards, assuming threats from authorized users, such as operators, who craft malicious prompts to elicit non-compliant guidance. Three state-of-the-art LLMs (OpenAI's GPT-4o mini, Google's Gemini 2.0 Flash-Lite, and Anthropic's Claude 3.5 Haiku) were tested against Baseline, BitBypass, and DeepInception jailbreaking methods across scenarios derived from nine NERC Reliability Standards (EOP, TOP, and CIP). In the initial broad experiment, the overall Attack Success Rate (ASR) was 33.1%, with DeepInception proving most effective at 63.17% ASR. Claude 3.5 Haiku exhibited complete resistance (0% ASR), while Gemini 2.0 Flash-Lite was most vulnerable (55.04% ASR) and GPT-4o mini moderately susceptible (44.34% ASR). A follow-up experiment refining malicious wording in Baseline and BitBypass attacks yielded a 30.6% ASR, confirming that subtle prompt adjustments can enhance simpler methods' efficacy.

Photonic Quantum Computing For Polymer Classification

We present a hybrid classical-quantum approach to the binary classification of polymer structures. Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps. The hybrid approach combines one of the three methods, Gaussian Kernel Method, Quantum-Enhanced Random Kitchen Sinks or Variational Quantum Classifier, implemented by linear quantum photonic circuits (LQPCs), with a classical deep neural network (DNN) feature extractor. The latter extracts from the classical data information about samples chemical structure. It also reduces the data dimensions yielding compact 2-dimensional data vectors that are then fed to the LQPCs. We adopt the photonic-based data-embedding scheme, proposed by Gan et al. [EPJ Quantum Technol. 9, 16 (2022)] to embed the classical 2-dimensional data vectors into the higher-dimensional Fock space. This hybrid classical-quantum strategy permits to obtain accurate noisy intermediate-scale quantum-compatible classifiers by leveraging Fock states with only a few photons. The models obtained using either of the three hybrid methods successfully classified the VIS and NIR polymers. Their accuracy is comparable as measured by their scores ranging from 0.86 to 0.88. These findings demonstrate that our hybrid approach that uses photonic quantum computing captures chemistry and structure-property correlation patterns in real polymer data. They also open up perspectives of employing quantum computing to complex chemical structures when a larger number of logical qubits is available.