Kang, Daniel

NoScope: optimizing neural network queries over video at scale

Kang, D., Emmons, J., Abuzaid, F., Bailis, P., & Zaharia, M. (2017) · Proceedings of the VLDB Endowment

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 367

MLPerf Training Benchmark

Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 206

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Zhan, Q., Liang, Z., Ying, Z., & Kang, D. (2024) · Findings of the Association for Computational Linguistics: ACL 2024

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 147

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Zhan, Q., Liang, Z., Ying, Z., & Kang, D. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 143

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 114

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2024) · 2024 IEEE Security and Privacy Workshops (SPW)

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 114

Identifying and Mitigating the Security Risks of Generative AI

Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security

Adversarial Robustness in Machine LearningInformation and Cyber Security

Cited by 57

LLM Agents can Autonomously Exploit One-day Vulnerabilities

Fang, R., Bindu, R., Gupta, A., & Kang, D. (2024) · arXiv

Web Application Security VulnerabilitiesAdversarial Robustness in Machine Learning

Cited by 26

LLM Agents can Autonomously Hack Websites

Fang, R., Bindu, R., Gupta, A., Zhan, Q., & Kang, D. (2024) · arXiv

Web Application Security VulnerabilitiesAdversarial Robustness in Machine Learning

Cited by 16

Establishing Best Practices for Building Rigorous Agentic Benchmarks

Zhu, Y., Jin, T., Pruksachatkun, Y., Zhang, A., Liu, S., Cui, S., Kapoor, S., Longpre, S., Meng, K., Weiss, R., et al. (2025) · arXiv

Artificial Intelligence ApplicationsAI-based Problem Solving and Planning

Cited by 2

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation

Kapoor, S., Stroebl, B., Kirgis, P., Nadgir, N., Siegel, Z. S., Wei, B., Xue, T., Chen, Z., Chen, F., Utpala, S., et al. (2025) · arXiv

Artificial Intelligence ApplicationsAI-based Problem Solving and Planning

Cited by 0

Establishing Best Practices in Building Rigorous Agentic Benchmarks

Zhu, Y., Jin, T., Pruksachatkun, Y., Zhang, A., Liu, S., Cui, S., Kapoor, S., Longpre, S., Meng, K., Weiss, R., et al. (2025) · Advances in Neural Information Processing Systems 38

Artificial Intelligence ApplicationsAI-based Problem Solving and Planning

Cited by 0