The impact of designing for security of AI is critical for humanity in the AI era. With humans increasingly becoming dependent upon AI, there is a need for neural networks that work reliably, inspite of Adversarial attacks. The vision for Safe and secure AI for popular use is achievable. To achieve safety of AI, this paper explores strategies and a novel deep learning architecture. To guard AI from adversaries, paper explores combination of 3 strategies:
1. Introduce randomness at inference time to hide the representation learning from adversaries.
2. Detect presence of adversaries by analyzing the sequence of inferences.
3. Exploit visual similarity.
To realize these strategies, this paper designs a novel architecture, Dynamic Neural Defense, DND. This defense has 3 deep learning architectural features:
1. By hiding the way a neural network learns from exploratory attacks using a random computation graph, DND evades attack.
2. By analyzing input sequence to cloud AI inference engine with LSTM, DND detects attack sequence.
3. By inferring with visual similar inputs generated by VAE, any AI defended by DND approach does not succumb to hackers.
Thus, a roadmap to develop reliable, safe and secure AI is presented.
Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets
Neurint, LLC（Neurint有限责任公司）；U.S. Army Cyber Corps（美国陆军网络部队）；Northwestern State University of Louisiana（路易斯安那西北州立大学）；Automata（Automata公司）；AGH University of Krakow（克拉科夫AGH大学）
GUARD-SLM: Token Activation-Based Defense Against Jailbreak Attacks for Small Language Models
Knight Foundation School of Computing and Information Sciences（奈特基金会计算与信息科学学院）；Security, Optimization, and Learning for InterDependent networks laboratory (solid lab)（安全、优化与相互依赖网络学习实验室）；Florida International University（佛罗里达国际大学）
SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety
School of Cyber Science and Technology, Beihang University, Beijing, China（北京航空航天大学网络安全学院）；Institute of Artificial Intelligence, Beihang University, Beijing, China（北京航空航天大学人工智能研究院）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；AI Security Lab, Beijing, China（360人工智能安全实验室）
To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack
Monash University（墨尔本大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；National University of Singapore（新加坡国立大学）
