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AISS'' 2026: International Conference on Artificial Intelligence Safety and Security

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Submission Date:
2026-10-15 Due in 13 days
Notification Date:
2026-10-31
Conference Date:
2026-12-13
Location:
Hefei, China
Years:
1
Viewed: 2   Tracked: 0   Attend: 0

Call For Papers

AISS'' 2026 (International Conference on Artificial Intelligence Safety and Security) is an academic conference held in Hefei, China on 2026-12-13. The paper submission deadline is 2026-10-15. Acceptance notifications are sent on 2026-10-31.

Scope and Topics We solicit original, high-quality research papers and practical experience reports on all aspects of AI safety and security. Topics of interest include, but are not limited to: Foundations of Trustworthy and Safe AI Robustness, generalization, and reliability of AI models under distribution shift Uncertainty quantification, calibration, and out-of-distribution detection Interpretability, explainability, and mechanistic understanding of model behavior Alignment, value specification, and controllable AI system design Safety of Foundation Models and Agentic AI Safety evaluation, red teaming, and adversarial testing of large language and multimodal models Jailbreaking, prompt injection, and defense for tool-using and agentic systems Hallucination detection, factual grounding, and truthfulness in generative models Safety of long-horizon autonomous agents, multi-agent systems, and AI workflows Security of AI Systems and AI Infrastructure Attacks on the AI lifecycle: data poisoning, backdoors, model extraction, and membership inference Security of training and inference infrastructure, accelerators, and AI accelerators supply chain Model and data supply-chain security, provenance, and integrity attestation Side-channel, hardware, and cross-tenant leakage in shared AI infrastructure Privacy-Preserving and Confidential AI Differential privacy, federated learning security, and privacy-preserving training Confidential computing, trusted execution environments, and secure enclaves for AI Machine unlearning, data rights, and training-data attribution Privacy risks of multimodal and embodied systems collecting real-world data Safety and Security of Embodied and Cyber-Physical AI Safety assurance and fail-safe behavior for robots, autonomous vehicles, and embodied agents Security of networked robots, teleoperation, and vehicle-to-everything systems Robustness of world models and embodied policies to sensing, actuation, and network attacks Formal verification, runtime monitoring, and safety envelopes for physical AI systems AI Safety Evaluation, Benchmarking, and Assurance Benchmarks, metrics, and testbeds for AI safety, security, and robustness Assurance cases, safety cases, and audit methodologies for AI systems Incident reporting, failure taxonomies, and post-deployment monitoring Reproducible evaluation of defenses and responsible disclosure practices Governance, Standards, and Compliance AI governance frameworks, risk management, and regulatory compliance International and national standards, certification, and conformity assessment Liability, accountability, and human oversight of autonomous AI systems Safety and security practices in industrial deployment and public-sector adoption AI for Security and Defense Machine learning for intrusion, malware, fraud, and anomaly detection AI-assisted vulnerability discovery, threat intelligence, and security operations Autonomous cyber defense and AI-driven incident response Adversarial machine learning: attack generation and certified defenses
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