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Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security
Abstract
It is our pleasure to welcome you to the 16th ACM Workshop on Artificial Intelligence and Security - AISec 2023. AISec, having been annually co-located with CCS for 16 consecutive years, is the premier meeting place for researchers interested in the intersection of security, privacy, AI, and machine learning. Its role as a venue has been to merge practical security problems with advances in AI and machine learning. In doing so, researchers have also been developing theories and analytics unique to this domain and have explored diverse topics such as learning in gametheoretic adversarial environments, privacy-preserving learning, and applications to malware, spam, and intrusion detection. AISec 2022 received 64 submissions, of which 21 (35%) were selected for publication and presentation as full papers. Submissions arrived from researchers in many different countries, and from a wide variety of institutions, both academic and corporate.
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“Potential consequences include exfiltrating user data, executing arbitrary code, and more [18, 23, 33, 42].”
From AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents (2024) · cites this paper as [18]
In the citing paper
“A successful attack can allow an external attacker to take actions (and call tools) on behalf of the user.”
From AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents (2024) · cites this paper as [18]
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A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges
Sasi, T., Lashkari, A. H., Lu, R., Xiong, P., & Iqbal, S. (2023) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2023.12.001 · Cited by 196
Preserving data privacy in machine learning systems
Mestari, S. Z. E., Lenzini, G., & Demirci, H. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103605 · Cited by 195
A survey on membership inference attacks and defenses in machine learning
Niu, J., Liu, P., Zhu, X., Shen, K., Wang, Y., Chi, H., Shen, Y., Jiang, X., Ma, J., & Zhang, Y. (2024) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2024.02.001 · Cited by 51
Red teaming large language models: A comprehensive review and critical analysis
Jabbar, M. S., Al-Azani, S., Alotaibi, A., & Ahmed, M. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104239 · Cited by 31
M2FD: Mobile malware federated detection under concept drift
Augello, A., Paola, A. D., & Re, G. L. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104361 · Cited by 20
Metadata
- Publisher
- ACM
- Published
- 2023-11-26
- OpenAlex
- W4388887352
- Cited by
- 18
- References
- 0
- Citations by year
- 2025: 12 · 2024: 4 · 2023: 2
- Type
- paratext
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- machine learning security, privacy-preserving machine learning, adversarial learning, game-theoretic security, malware detection, spam detection, intrusion detection
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- CoChat. (2026). Citation check: Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security. https://app.cochat.ai/papers/10.1145/3605764
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- Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security. (2023). https://doi.org/10.1145/3605764
