A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWares

In this paper we argue that a jailbroken GenAI model can cause substantial harm to GenAI-powered applications and facilitate PromptWare, a new type of attack that flips the GenAI model's behavior from serving an application to attacking it. PromptWare exploits user inputs to jailbreak a GenAI model to force/perform malicious activity within the context of a GenAI-powered application. First, we introduce a naive implementation of PromptWare that behaves as malware that targets Plan & Execute architectures (a.k.a., ReAct, function calling). We show that attackers could force a desired execution flow by creating a user input that produces desired outputs given that the logic of the GenAI-powered application is known to attackers. We demonstrate the application of a DoS attack that triggers the execution of a GenAI-powered assistant to enter an infinite loop that wastes money and computational resources on redundant API calls to a GenAI engine, preventing the application from providing service to a user. Next, we introduce a more sophisticated implementation of PromptWare that we name Advanced PromptWare Threat (APwT) that targets GenAI-powered applications whose logic is unknown to attackers. We show that attackers could create user input that exploits the GenAI engine's advanced AI capabilities to launch a kill chain in inference time consisting of six steps intended to escalate privileges, analyze the application's context, identify valuable assets, reason possible malicious activities, decide on one of them, and execute it. We demonstrate the application of APwT against a GenAI-powered e-commerce chatbot and show that it can trigger the modification of SQL tables, potentially leading to unauthorized discounts on the items sold to the user.

On-demand Mobility Services for Urban Resilience: A Review Towards Human-Machine Collaborative Future

Mobility-on-demand (MOD) services have the potential to significantly improve the adaptiveness and recovery of urban logistics and transportation infrastructure, in the wake of disruptive events. This paper presents a survey on the usage of MOD services for resilience improvement (MOD-R) and finds a noticeable increase within recent years on this topic across four main areas: resilient MOD services, novel usage of MOD-R services for improving supply chain resilience, empirical impact evaluation, and supporting technologies. MOD-R services have been utilized for anomaly detection, essential supply delivery, evacuation and rescue, on-site medical care, power grid stabilization, transit service substitution during downtime, and infrastructure and equipment repair. The review reveals integrating electrification, automation, and advanced communication technologies offers significant synergistic benefits. The review also suggests the importance of harnessing the collective capabilities of humans and intelligent machines to effectively implement versatile, multi-functional MOD-R services during crises.

Max-Min Fair Energy-Efficient Beam Design for Quantized ISAC LEO Satellite Systems: A Rate-Splitting Approach

Low earth orbit (LEO) satellite systems with sensing functionality is envisioned to facilitate global-coverage service and emerging applications in 6G. Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication. To effectively control the interference, rate-splitting multiple access (RSMA) scheme is employed as the interference management strategy in the system design. On the other hand, to address the limited power supply at the LEO satellites, we consider low-resolution quantization digital-to-analog converters (DACs) at the transmitter to reduce power consumption, which grows exponentially with the number of quantization bits. Additionally, optimizing the total energy efficiency (EE) of the system is a common practice to save the power. However, this metric lacks fairness among users. To ensure this fairness and further enhance EE, we investigate the max-min fairness EE of the RSMA-assisted integrated sensing and communications (ISAC)-LEO satellite system. In this system, the satellite transmits a quantized dual-functional signal serving downlink users while detecting a target. Specifically, we optimize the precoders for maximizing the minimal EE among all users, considering the power consumption of each radio frequency (RF) chain under communication and sensing constraints. To tackle this optimization problem, we proposed an iterative algorithm based on successive convex approximation (SCA) and Dinkelbach's method. Numerical results illustrate that the proposed design outperforms the strategies that aim to maximize the total EE of the system and conventional space-division multiple access (SDMA) in terms of max-min fairness EE and the communication-sensing trade-off.

Cost-Efficient Computation Offloading and Service Chain Caching in LEO Satellite Networks

The ever-increasing demand for ubiquitous, continuous, and high-quality services poses a great challenge to the traditional terrestrial network. To mitigate this problem, the mobile-edge-computing-enhanced low earth orbit (LEO) satellite network, which provides both communication connectivity and on-board processing services, has emerged as an effective method. The main issue in LEO satellites includes finding the optimal locations to host network functions (NFs) and then making offloading decisions. In this article, we jointly consider the problem of service chain caching and computation offloading to minimize the overall cost, which consists of task latency and energy consumption. In particular, the collaboration among satellites, the network resource limitations, and the specific operation order of NFs in service chains are taken into account. Then, the problem is formulated and linearized as an integer linear programming model. Moreover, to accelerate the solution, we provide a greedy algorithm with cubic time complexity. Numerical investigations demonstrate the effectiveness of the proposed scheme, which can reduce the overall cost by around 20% compared to the nominal case where NFs are served in data centers.

Throughput Analysis of IEEE 802.11bn Coordinated Spatial Reuse

Multi-Access Point Coordination (MAPC) is becoming the cornerstone of the IEEE 802.11bn amendment, alias Wi-Fi 8. Among the MAPC features, Coordinated Spatial Reuse (C-SR) stands as one of the most appealing due to its capability to orchestrate simultaneous access point transmissions at a low implementation complexity. In this paper, we contribute to the understanding of C-SR by introducing an analytical model based on Continuous Time Markov Chains (CTMCs) to characterize its throughput and spatial efficiency. Applying the proposed model to several network topologies, we show that C-SR opportunistically enables parallel high-quality transmissions and yields an average throughput gain of up to 59% in comparison to the legacy 802.11 Distributed Coordination Function (DCF) and up to 42% when compared to the 802.11ax Overlapping Basic Service Set Packet Detect (OBSS/PD) mechanism.

Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach

Digital Twin (DT) is a prominent technology to utilise and deploy within the healthcare sector. Yet, the main challenges facing such applications are: Strict health data-sharing policies, high-performance network requirements, and possible infrastructure resource limitations. In this paper, we address all the challenges by provisioning adaptive Virtual Network Functions (VNFs) to enforce security policies associated with different data-sharing scenarios. We define a Cloud-Native Network orchestrator on top of a multi-node cluster mesh infrastructure for flexible and dynamic container scheduling. The proposed framework considers the intended data-sharing use case, the policies associated, and infrastructure configurations, then provision Service Function Chaining (SFC) and provides routing configurations accordingly with little to no human intervention. Moreover, what is \textit{optimal} when deploying SFC is dependent on the use case itself, and we tune the hyperparameters to prioritise resource utilisation or latency in an effort to comply with the performance requirements. As a result, we provide an adaptive network orchestration for digital health twin use cases, that is policy-aware, requirements-aware, and resource-aware.

SFC Deployment in Space-Air-Ground Integrated Networks Based on Matching Game

The space-air-ground integrated network (SAGIN) is dynamic and flexible, which can support transmitting data in environments lacking ground communication facilities. However, the nodes of SAGIN are heterogeneous and it is intractable to share the resources to provide multiple services. Therefore, in this paper, we consider using network function virtualization technology to handle the problem of agile resource allocation. In particular, the service function chains (SFCs) are constructed to deploy multiple virtual network functions of different tasks. To depict the dynamic model of SAGIN, we propose the reconfigurable time extension graph. Then, an optimization problem is formulated to maximize the number of completed tasks, i.e., the successful deployed SFC. It is a mixed integer linear programming problem, which is hard to solve in limited time complexity. Hence, we transform it as a many-to-one two-sided matching game problem. Then, we design a Gale-Shapley based algorithm. Finally, via abundant simulations, it is verified that the designed algorithm can effectively deploy SFCs with efficient resource utilization.

Advanced Scaling Methods for VNF deployment with Reinforcement Learning

Network function virtualization (NFV) and software-defined network (SDN) have become emerging network paradigms, allowing virtualized network function (VNF) deployment at a low cost. Even though VNF deployment can be flexible, it is still challenging to optimize VNF deployment due to its high complexity. Several studies have approached the task as dynamic programming, e.g., integer linear programming (ILP). However, optimizing VNF deployment for highly complex networks remains a challenge. Alternatively, reinforcement learning (RL) based approaches have been proposed to optimize this task, especially to employ a scaling action-based method which can deploy VNFs within less computational time. However, the model architecture can be improved further to generalize to the different networking settings. In this paper, we propose an enhanced model which can be adapted to more general network settings. We adopt the improved GNN architecture and a few techniques to obtain a better node representation for the VNF deployment task. Furthermore, we apply a recently proposed RL method, phasic policy gradient (PPG), to leverage the shared representation of the service function chain (SFC) generation model from the value function. We evaluate the proposed method in various scenarios, achieving a better QoS with minimum resource utilization compared to the previous methods. Finally, as a qualitative evaluation, we analyze our proposed encoder's representation for the nodes, which shows a more disentangled representation.

Understanding accountability in algorithmic supply chains

Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by 'many hands'. Increasingly, however, algorithmic systems are also produced, deployed, and used within a supply chain comprising multiple actors tied together by flows of data between them. In such cases, it is the working together of an algorithmic supply chain of different actors who contribute to the production, deployment, use, and functionality that drives systems and produces particular outcomes. We argue that algorithmic accountability discussions must consider supply chains and the difficult implications they raise for the governance and accountability of algorithmic systems. In doing so, we explore algorithmic supply chains, locating them in their broader technical and political economic context and identifying some key features that should be understood in future work on algorithmic governance and accountability (particularly regarding general purpose AI services). To highlight ways forward and areas warranting attention, we further discuss some implications raised by supply chains: challenges for allocating accountability stemming from distributed responsibility for systems between actors, limited visibility due to the accountability horizon, service models of use and liability, and cross-border supply chains and regulatory arbitrage

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where an incoming task can be partitioned into two parts, one for local execution and the other for remote execution. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where we design a framework enabling interaction between two agents. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms in terms of cumulative and average episodic rewards and it overweighs a number of baseline algorithms with respect to execution delay, energy consumption, and usage charge.