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Middle Tennessee State University

Academic institutionnorthamerica · us
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Research library7linked papers
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Selected work

Representative Papers

An Empirical Study of Output-to-Input Loops for Black-Box Backdoor Detection in Fine-Tuned Open-Weight LLMs

Aug 11, 2026

This work addresses the challenge of detecting backdoors in fine-tuned large language models under black-box settings—particularly when training data, clean reference models, or known trigger tokens are unavailable. The authors propose a “self-feeding” detection method that iteratively feeds a model’s own output back as its next input, progressively steering generated text toward the distribution of the fine-tuning data and thereby revealing latent backdoor behaviors. This approach is the first to systematically exploit an output-to-input feedback mechanism for efficient, internal-information-free triggering. Evaluated across six open-source models (3B–15B parameters) and eleven backdoor attack variants, the method achieves a model-level detection precision of 92.0%, maintains 100% precision within just four inference steps, and reduces query counts by 60%, demonstrating its effectiveness even under low single-prompt recall conditions.

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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Aug 11, 2026

This study addresses the critical security gap in large language model (LLM)-based autonomous agents endowed with real-world operational capabilities, where reasoning components remain highly vulnerable to attacks that can trigger unauthorized access, irreversible state changes, or cascading failures. Conducting a systematic literature review of 85 studies from 2023–2025 following PRISMA 2020 guidelines, this work reveals a pronounced imbalance between attack and defense research (3.9:1) and introduces the first four-layer vulnerability taxonomy for agent LLMs—encompassing perception, cognition, action, and interaction—identifying 13 vulnerability types and seven open challenges centered on isolation. Notably, perception-layer vulnerabilities account for 66% of reported issues, whereas high-risk action-layer threats such as tool misuse and sandbox escape constitute only 4.7%, highlighting a severe misalignment between research focus and actual risk. The study further identifies cross-layer vulnerability propagation due to architectural coupling as the root cause of systemic security weaknesses.

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Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning

Jun 12, 2026

This study investigates the use of physiological signals—specifically electrodermal activity, heart rate, and skin temperature collected during examinations—to predict students’ academic performance. We systematically evaluate a range of machine learning and deep learning models, including logistic regression, random forest, support vector machines (SVM), LSTM, GRU, and Transformer architectures. Notably, this work presents the first application of the Transformer model to numerical physiological data, demonstrating predictive performance on par with LSTM and GRU. Intriguingly, under certain conditions, the comparatively simple random forest model outperforms more complex deep learning approaches. Our findings establish a quantifiable link between physiological stress responses and academic outcomes, while also offering new evidence supporting the efficacy of lightweight models in educational neuroscience applications.

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Recent publications

Latest Papers

An Empirical Study of Output-to-Input Loops for Black-Box Backdoor Detection in Fine-Tuned Open-Weight LLMs

Aug 11, 2026

This work addresses the challenge of detecting backdoors in fine-tuned large language models under black-box settings—particularly when training data, clean reference models, or known trigger tokens are unavailable. The authors propose a “self-feeding” detection method that iteratively feeds a model’s own output back as its next input, progressively steering generated text toward the distribution of the fine-tuning data and thereby revealing latent backdoor behaviors. This approach is the first to systematically exploit an output-to-input feedback mechanism for efficient, internal-information-free triggering. Evaluated across six open-source models (3B–15B parameters) and eleven backdoor attack variants, the method achieves a model-level detection precision of 92.0%, maintains 100% precision within just four inference steps, and reduces query counts by 60%, demonstrating its effectiveness even under low single-prompt recall conditions.

0 citationsRead paper

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Aug 11, 2026

This study addresses the critical security gap in large language model (LLM)-based autonomous agents endowed with real-world operational capabilities, where reasoning components remain highly vulnerable to attacks that can trigger unauthorized access, irreversible state changes, or cascading failures. Conducting a systematic literature review of 85 studies from 2023–2025 following PRISMA 2020 guidelines, this work reveals a pronounced imbalance between attack and defense research (3.9:1) and introduces the first four-layer vulnerability taxonomy for agent LLMs—encompassing perception, cognition, action, and interaction—identifying 13 vulnerability types and seven open challenges centered on isolation. Notably, perception-layer vulnerabilities account for 66% of reported issues, whereas high-risk action-layer threats such as tool misuse and sandbox escape constitute only 4.7%, highlighting a severe misalignment between research focus and actual risk. The study further identifies cross-layer vulnerability propagation due to architectural coupling as the root cause of systemic security weaknesses.

0 citationsRead paper

Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning

Jun 12, 2026

This study investigates the use of physiological signals—specifically electrodermal activity, heart rate, and skin temperature collected during examinations—to predict students’ academic performance. We systematically evaluate a range of machine learning and deep learning models, including logistic regression, random forest, support vector machines (SVM), LSTM, GRU, and Transformer architectures. Notably, this work presents the first application of the Transformer model to numerical physiological data, demonstrating predictive performance on par with LSTM and GRU. Intriguingly, under certain conditions, the comparatively simple random forest model outperforms more complex deep learning approaches. Our findings establish a quantifiable link between physiological stress responses and academic outcomes, while also offering new evidence supporting the efficacy of lightweight models in educational neuroscience applications.

0 citationsRead paper