The Model Context Protocol (MCP) has emerged as a standard for connecting large language models (LLMs) with external tools. However, this MCP ecosystem introduces new security risks across hosts, servers, and registries. In this paper, we present the first cross-entity security study of MCP under a two-stage attack surface. At the registry-level, weak vetting and ownership checks allow adversarial or hijacked servers to enter hosts. After integration, attacker-controlled tool metadata can shape LLM reasoning and induce attacker-intended operations, which hosts execute without independent verification. Code-level vulnerabilities (e.g., code injection) are not required but can amplify attacker-controlled parameters into exploitation. We analyze 67,057 servers across six public registries and identify widespread conditions enabling server hijacking and invocation manipulation. We further implement MCPInspect, a pre-integration analysis tool that detects misleading tool metadata and exploitable code vulnerabilities, identifying 833 vulnerable servers and 18 with suspicious descriptions.

Continuous Integration (CI) has been widely adopted for automated software building and testing. In CI workflows, various third-party services can be integrated to enhance functionalities. During the integration process, secrets such as tokens and credentials are often used for authentication and authorization. Unfortunately, secret leakage regularly occurs, potentially causing serious consequences. Existing secret scanners detect secrets by matching tokens and credentials through regular expressions, which suffer from low recall rates. In this paper, we identify several issues in regular expression-based secret detectors and propose SecretScout, a new method for scanning hard-coded secrets by detecting their names. SecretScout contains carefully designed detectors to extract candidates. It considers special flags where secrets might exist, and further applies filters to reduce false positives. SecretScout can detect secrets in both structured files (e.g., CI configuration files) and unstructured files (e.g., log files). To demonstrate the effectiveness, we evaluate SecretScout and different versions of GitLeaks and TruffleHog using labeled configuration files. The results show that SecretScout achieves$7.3 \times$and$100 \times$higher recall rates compared to the default versions of GitLeaks and TruffleHog, respectively. We also conduct a measurement study on open-source projects' configuration files, and demonstrate that many true secrets might be leaked.

Mainstream Continuous Integration (CI) platforms have provided the plugin functionality to accelerate the development of CI pipelines. Unfortunately, CI plugins, which are essentially reusable code snippets, also expose new attack surfaces as plugins might be developed by less trusted users. In this paper, we present an in-depth study to understand potential security risks in existing CI plugins. We conduct a comprehensive analysis of plugin implementations on four mainstream CI platforms (GitHub Actions, GitLab CI, CircleCI, and Azure Pipelines), and investigate several weak links in existing plugin distributions and isolation mechanisms. We investigate seven attack vectors that can enable attackers to hijack plugins and distribute malicious code without plugins users being aware, and further exploit hijacked plugins to manipulate the workflow execution. Additionally, we find that plugin dependency (a plugin references other plugins) might further amplify the attack impact of our disclosed attacks. To evaluate the potential impact, we conduct a large-scale measurement on GitHub and GitLab, covering a total of 1,328,912 repositories using the aforementioned CI platforms. Our measurement results show that a large number of repositories and existing plugins, including many widely used ones, are potentially vulnerable to the proposed attacks. We have duly reported the identified vulnerabilities and received positive responses.

Continuous Integration (CI) platforms have widely adopted caching to speed up CI task executions by storing and reusing dependent packages. Unfortunately, CI cache also exposes new attack surfaces when cache objects are shared across trust boundaries. In this paper, we systematically investigate potential security threats of CI cache features in seven mainstream CI platforms (CIPs). We find that existing CIPs have isolation issues in their cache sharing and inheritance strategies, potentially raising cache poisoning and data leakage problems. By exploiting these vulnerable mechanisms, we further uncover four attack vectors enabling attackers to stealthily inject malicious code into the cache or steal sensitive data. Even worse, many CIPs provide vulnerable official cache templates that will mistakenly store and expose sensitive data in the cache by default. To understand the potential impact of our disclosed threats, we develop an analysis tool and conduct a large-scale measurement on open-source repositories. Our measurement results show that many popular repositories are potentially affected by these attacks. We also identify 78 repositories that expose their high-value secrets in cache objects and are at risk of secret leakage. We have duly reported identified vulnerabilities to corresponding stakeholders and received positive responses.

Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues.

Recent studies have revealed much detailed information about the translation lookaside buffers (TLBs) of modern CPUs, but we find that many properties of such components in modern GPUs still remain unknown or unclear. To fill this knowledge gap, we develop a new GPU TLB reverse-engineering method and apply it to a variety of consumer- and server-grade GPUs in Turing and Ampere generations. Aside from learning significantly more comprehensive and accurate GPU TLB properties, we discover a design flaw of NVIDIA Multi-Instance GPU (MIG) feature. MIG claims full partitioning of the entire GPU memory system for secure GPU sharing in cloud computing. However, we surprisingly find that MIG does not partition the last-level TLB, which is shared by all the compute units in a GPU. Exploiting this design flaw and learned TLB properties, we are able to construct a covert channel for data exfiltration across MIG-enforced isolation. To the best of our knowledge, this is the first attack on MIG. We evaluate the proposed attack on a commercial cloud platform, and we successfully achieve reliable data exfiltration from a victim tenant at a speed of up to 31 kbps with a very high accuracy around 99.8%. Even when the victim is using the GPU for deep neural network training, the transmission can still reach more than 25 kbps with a more than 99.5% accuracy. We propose and implement a mitigation approach that can effectively thwart data exfiltration through this covert channel. Additionally, we present a preliminary study on exploiting the access patterns of the last-level TLB to infer the identity of applications running in other MIG-created GPU instances.

Software-Defined Networking (SDN) enables the deployment of diversified networking applications by providing global visibility and open programmability on a centralized controller. As SDN enters its second decade, several well-developed open source controllers have been widely adopted in industry, and various commercial SDN applications are built to meet the surging demand of network innovation. This complex ecosystem inevitably introduces new security threats, as malicious applications can significantly disrupt network operations. In this paper, we introduce a new vulnerability in existing SDN controllers that enable adversaries to create a backdoor and further deploy malicious applications to disrupt network service via a series of topology poisoning attacks. The root cause of this vulnerability is that SDN systems simply process received Packet-In messages without checking the integrity, and thus can be misguided by manipulated messages. We discover that five popular SDN controllers (i.e., Floodlight, ONOS, OpenDaylight, POX and Ryu) are potentially vulnerable to the disclosed attack, and further propose six new attacks exploiting this vulnerability to disrupt SDN services from different layers. We evaluate the effectiveness of these attacks with experiments in real SDN testbeds, and discuss feasible countermeasures.

Continuous Integration (CI) is a widely-adopted software development practice for automated code integration. A typical CI workflow involves multiple independent stakeholders, including code hosting platforms (CHPs), CI platforms (CPs), and third party services. While CI can significantly improve development efficiency, unfortunately, it also exposes new attack surfaces. As the code executed by a CI task may come from a less-trusted user, improperly configured CI with weak isolation mechanisms might enable attackers to inject malicious code into victim software by triggering a CI task. Also, one insecure stakeholder can potentially affect the whole process. In this paper, we systematically study potential security threats in CI workflows with multiple stakeholders and major CP components considered. We design and develop an analysis tool, CInspector, to investigate potential vulnerabilities in seven popular CPs, when integrated with three mainstream CHPs. We find that all CPs have the risk of token leakage caused by improper resource sharing and isolation, and many of them utilize over-privileged tokens with improper validity periods. We further reveal four novel attack vectors that allow attackers to escalate their privileges and stealthy inject malicious code by executing a piece of code in a CI task. To understand the potential impact, we conduct a large-scale measurement on the three mainstream CHPs, scrutinizing over 1.69 million repositories. Our quantitative analysis demonstrates that some very popular repositories and large organizations are affected by these attacks. We have duly reported the identified vulnerabilities to CPs and received positive responses.

Package registries host reusable code assets, allowing developers to share and reuse packages easily, thus accelerating the software development process. Current software registry ecosystems involve multiple independent stakeholders for package management. Unfortunately, abnormal behavior and information inconsistency inevitably exist, enabling adversaries to conduct malicious activities with minimal effort covertly. In this paper, we investigate potential security vulnerabilities in six popular software registry ecosystems. Through a systematic analysis of the official registries, corresponding registry mirrors and registry clients, we identify twelve potential attack vectors, with six of them disclosed for the first time, that can be exploited to distribute malicious code stealthily. Based on these security issues, we build an analysis framework, RScouter, to continuously monitor and uncover vulnerabilities in registry ecosystems. We then utilize RScouter to conduct a measurement study spanning one year over six registries and seventeen popular mirrors, scrutinizing over 4 million packages across 53 million package versions. Our quantitative analysis demonstrates that multiple threats exist in every ecosystem, and some have been exploited by attackers. We have duly reported the identified vulnerabilities to related stakeholders and received positive responses.

Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual’s unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95 percent on three public datasets, a sheer 15 percent gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99 and 90 percent success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.

Cloud gaming has become an emerging computing paradigm in recent years, allowing computer games to offload complex graphics and logic computation to the cloud. To deliver a smooth and high-quality gaming experience, cloud gaming services have invested abundant computing resources in the cloud, including adequate CPUs, top-tier GPUs, and high-bandwidth Internet connections. Unfortunately, the abundant computing resources offered by cloud gaming are vulnerable to misuse and exploitation for malicious purposes. In this paper, we present an in-depth study on security vulnerabilities in cloud gaming services. Specifically, we reveal that adversaries can purposely inject malicious programs/URLs into the cloud gaming services via game mods. Using the provided features such as in-game subroutines, game launch options, and built-in browsers, adversaries are able to execute the injected malicious programs/URLs in cloud gaming services. To demonstrate that such vulnerabilities pose a serious threat, we conduct four proof-of-concept attacks on cloud gaming services. Two of them are to abuse the CPUs and GPUs in cloud gaming services to mine cryptocurrencies with attractive profits and train machine learning models at a trivial cost. The other two are to exploit the high-bandwidth connections provided by cloud gaming for malicious Command & Control and censorship circumvention. Finally, we present several countermeasures for cloud gaming services to protect their valuable assets from malicious exploitation.

Containers enable a computing system to host multiple isolated applications, making more cost-efficient use of the available computing resources. However, exploiting shared computing resources, adversaries can launch various real-world attacks (e.g., denial-of-service attacks) inside containers. In this paper, we present TORPEDO, a fuzzing-based approach to detecting out-of-band workloads: such workloads could largely interfere the performance of colocated container instances on the same host, gaining extra unfair advantages on the system resources without being charged appropriately. TORPEDO mutates inputs of OS syscalls and simultaneously monitors the resource consumption of multiple container instances. It uses resource-guided heuristics to find inputs that maximize the difference in resource consumption between container instances and resource limits. We evaluate TORPEDO on widely-used containerization platforms and demonstrate that it can verify adversarial workloads that are manually discovered by existing research. More importantly, TORPEDO identifies several zero-day vulnerabilities that are not known to the public.

Universal Serial Bus (USB) ports are a ubiquitous feature in computer systems and offer a cheap and efficient way to provide power and data connectivity between a host and peripheral devices. Even with the rise of cloud and off-site computing, USB has played a major role in enabling data transfer between devices. Its usage is especially prevalent in high-security environments where systems are ‘air-gapped’ and not connected to the Internet. However, recent research has demonstrated that USB is not nearly as secure as once thought, with different attacks showing that modified firmware on USB mass storage devices can compromise a host system. While many defenses have been proposed, they require user interaction, advanced hardware support (incompatible with legacy devices), or utilize device identifiers that can be subverted by an attacker. In this paper, we present Time-Print, a novel timing-based fingerprinting method, for identifying USB mass storage devices. We create a fingerprint by timing a series of read operations from different locations on a drive, as the timing variations are unique enough to identify individual USB devices. Time-Print is low overhead, completely software-based, and does not require any extra or specialized hardware. To validate the efficacy of Time-Print, we examine more than 40 USB flash drives and conduct experiments in multiple authentication scenarios. The experimental results show that Time-Print can (1) identify known/unknown brand/model USB devices with greater than 99.5% accuracy, (2) identify seen/unseen devices of the same brand/model with 95% accuracy, and (3) classify USB devices from the same brand/model with an average accuracy of 98.7%.

As power and cooling cost has become a major factor in the total cost of ownership (TCO) of large-scale data centers, it is important to investigate how data centers run their cooling systems in practice. The data centers of Amazon Web Services (AWS) have been continuously expanding worldwide, and their restrictive security policies keep many management aspects of data centers private. In this paper, we make an attempt to explore the cooling systems of AWS data centers without privileged accesses. We first demonstrate PVT (process, voltage, and temperature) variations in AWS FPGAs (Field Programmable Gate Arrays) using time-digital converters (TDC). We further leverage the DRAM temperature side channel and improve the usage of the TDC to measure the temperature change accurately. We conduct a measurement on the daily temperatures of AWS data centers worldwide and find that temperature changes of some data centers are closely related to local weathers. Thus, we deduce they adopt free cooling techniques. This measurement study motivates us to re-think the vulnerability of data centers to power/thermal attacks.

Live migration of Virtual machines (VMs) has become a regular tool for edge and cloud operators to facilitate system maintenance, fault tolerance, and load balancing, with little impact on running instances. However, the potential security risks of live migration of VMs are still obscure. In this article, we expose a new vulnerability in the existing VM live migration approaches, especially thepost-copyapproach. The entire live migration mechanism relies upon reliable TCP connectivity for the transfer of the VM state. We demonstrate that, if the host server is vulnerable to off-path TCP attacks, the loss of TCP reliability leads to VM live migration failure. We demonstrate that, by intentionally aborting the TCP connection, attackers can cause unrecoverable memory inconsistency forpost-copy, leading to a significant increase in downtime and performance degradation of the running VM. Additionally, we present detailed techniques to reset the migration connection under heavy networking traffic. We also propose effective defenses to secure the VM live migration. Our experimental results demonstrate that memory inconsistencies could be devastating to some applications, and it only takes a few minutes to reset a heavy migration connection.

Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale and powerful servers generate a great amount of heat, resulting in significant cooling costs. A trend in modern data centers is to raise the temperature and maintain all servers in a relatively hot environment. While this can save on cooling costs given benign workloads running in servers, the hot environment increases the risk of a cooling failure. In this article, we unveil a new vulnerability of existing data centers with aggressive cooling energy saving policies. Such a vulnerability might be exploited to launch thermal attacks that could severely worsen the thermal conditions in a data center. Specifically, we conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can (1) largely increase the temperature of victim servers degrading their performance and reliability, (2) negatively impact on thermal conditions of neighboring servers causing local hotspots, (3) raise the cooling cost, and (4) even lead to cooling failures. Finally, we propose and evaluate effective server and data center level defenses to enhance thermal stabilities.

Advanced RISC Machines (ARM) processors have recently gained market share in both cloud computing and desktop applications. Meanwhile, ARM devices have shifted to a more peripheral based design, wherein designers attach a number of coprocessors and accelerators to the System-on-a-Chip (SoC). By adopting a System-Level Cache, which acts as a shared cache between the CPU-cores and peripherals, ARM attempts to alleviate the memory bottleneck issues that exist between data sources and accelerators. This paper investigates emerging security threats introduced by this new System-Level Cache. Specifically, we demonstrate that the System-Level Cache can still be exploited to create a cache occupancy channel to accurately fingerprint websites. We redesign and optimize the attack for various browsers based on the ARM cache design, which can significantly reduce the attack duration while increasing accuracy. Moreover, we introduce a novel GPU contention channel in mobile devices, which can achieve similar accuracy to the cache occupancy channel. We conduct a thorough evaluation by examining these attacks across multiple devices, including iOS, Android, and MacOS with the new M1 MacBook Air. The experimental results demonstrate that (1) the System-Level Cache based website fingerprinting technique can achieve promising accuracy in both open (up to 90%) and closed (up to 95%) world scenarios, and (2) our GPU contention channel is more effective than the CPU cache channel on Android devices.

RAPL (Running Average Power Limit) is a hardware feature introduced by Intel to facilitate power management. Even though RAPL and its supporting software interfaces can benefit power management significantly, they are unfortunately designed without taking certain security issues into careful consideration. In this paper, we demonstrate that information leaked through RAPL-induced side channels can be exploited to mount realistic attacks. Specifically, we have constructed a new RAPL-based covert channel using a single AVX instruction, which can exfiltrate data across different boundaries (e.g., those established by containers in software or even CPUs in hardware); and, we have investigated the first RAPL-based website fingerprinting technique that can identify visited webpages with a high accuracy (up to 99% in the case of the regular network using a browser like Chrome or Safari, and up to 81% in the case of the anonymity network using Tor). These two studies form a preliminary examination into RAPL-imposed security implications. In addition, we discuss some possible countermeasures.

When attackers compromise a computer system and obtain root control over the victim system, retaining that control and avoiding detection become their top priority. To achieve this goal, various rootkits have been developed. However, existing rootkits are still easy to detect as long as defenders can gain control at a lower level, such as the operating system level, the hypervisor level, or the hardware level. In this paper, we present a new type of rootkit called CloudSkulk, which is a nested virtual machine (VM) based rootkit. While nested virtualization has attracted sufficient attention from the security and cloud community, to the best of our knowledge, we are the first to reveal and demonstrate how nested virtualization can be used by attackers to develop rootkits. We then, from defenders' perspective, present a novel approach to detecting CloudSkulk rootkits at the host level. Our experimental results show that the proposed approach is effective in detecting CloudSkulk rootkits.

Single Sign-On (SSO) has been widely adopted for online authentication due to its favorable usability and security. However, it also introduces a single point of failure since all service providers fully trust the identity of a user created by the SSO identity provider. In this paper, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. The vulnerability exists because current SSO systems highly rely on a user’s email address to bind an account with a real identity, but ignore the fact that email addresses might be reused by other users. We reveal that under the SSO authentication, such inconsistency allows an adversary controlling a reused email address to take over associated online accounts without knowing any credentials like passwords. Specifically, we first conduct a measurement study on the account management policies for multiple cloud email providers, showing the feasibility of acquiring previously used email accounts. We further perform a systematic study on 100 popular websites using the Google business email service with our own domain address and demonstrate that most online accounts can be compromised by exploiting this inconsistency vulnerability. To shed light on email reuse in the wild, we analyze the commonly used naming conventions that lead to a wide existence of potential email address collisions, and conduct a case study on the account policies of U.S. universities. Finally, we propose several useful practices for end-users, service providers, and identity providers to protect against this identity-account inconsistency threat.

Container technology provides a lightweight operating system level virtual hosting environment. Its emergence profoundly changes the development and deployment paradigms of multi-tier distributed applications. However, due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, some security concerns still exist for multiple containers sharing an operating system kernel on a multi-tenancy container-based cloud service. In this paper, we first present the information leakage channels we discovered that are accessible within containers. Such channels expose a spectrum of system-wide host information to containers without proper resource partitioning. By exploiting such leaked host information, it becomes much easier for malicious adversaries (acting as tenants in a container cloud) to launch attacks that might impact the reliability of cloud services. We demonstrate that the information leakage channels could be exploited to infer private data, detect and verify co-residence, build covert channels, and launch more advanced cloud-based attacks. We discuss the root causes of the containers' information leakage and propose a two-stage defense approach. As demonstrated in the evaluation, our defense is effective and incurs trivial performance overhead.

With the rapid growth of visual content, deep learning to hash is gaining popularity in the image retrieval community recently. Although it greatly facilitates search efficiency, privacy is also at risks when images on the web are retrieved at a large scale and exploited as a rich mine of personal information. An adversary can extract private images by querying similar images from the targeted category for any usable model. Existing methods based on image processing preserve privacy at a sacrifice of perceptual quality. In this paper, we propose a new mechanism based on adversarial examples to "stash'' private images in the deep hash space while maintaining perceptual similarity. We first find that a simple approach of hamming distance maximization is not robust against brute-force adversaries. Then we develop a new loss function by maximizing the hamming distance to not only the original category, but also the centers from all the classes, partitioned into clusters of various sizes. The extensive experiment shows that the proposed defense can harden the attacker's efforts by 2-7 orders of magnitude, without significant increase of computational overhead and perceptual degradation. We also demonstrate 30-60% transferability in hash space with a black-box setting. The code is available at: https://github.com/sugarruy/hashstash

Linux Control Groups, i.e., cgroups, are the key building blocks to enable operating-system-level containerization. The cgroups mechanism partitions processes into hierarchical groups and applies different controllers to manage system resources, including CPU, memory, block I/O, etc. Newly spawned child processes automatically copy cgroups attributes from their parents to enforce resource control. Unfortunately, inherited cgroups confinement via process creation does not always guarantee consistent and fair resource accounting. In this paper, we devise a set of exploiting strategies to generate out-of-band</>workloads via de-associating processes from their original process groups. The system resources consumed by such workloads will not be charged to the appropriate cgroups. To further demonstrate the feasibility, we present five case studies within Docker containers to demonstrate how to break the resource rein of cgroups in realistic scenarios. Even worse, by exploiting those cgroups' insufficiencies in a multi-tenant container environment, an adversarial container is able to greatly amplify the amount of consumed resources, significantly slow-down other containers on the same host, and gain extra unfair advantages on the system resources. We conduct extensive experiments on both a local testbed and an Amazon EC2 cloud dedicated server. The experimental results demonstrate that a container can consume system resources (e.g., CPU) as much as $200\times$ of its limit, and reduce both computing and I/O performance of particular workloads in other co-resident containers by 95%.

Pre-trained deep learning models can be deployed on mobile devices to conduct inference. However, they are usually not updated thereafter. In this paper, we take a step further to incorporate training deep neural networks on battery-powered mobile devices and overcome the difficulties from the lack of labeled data. We design and implement a new framework to enlarge sample space via data paring and learn a deep metric under the privacy, memory and computational constraints. A case study of deep behavioral authentication is conducted. Our experiments demonstrate accuracy over 95% on three public datasets, a sheer 15% gain from traditional multi-class classification with less data and robustness against brute-force attacks with 99% success. We demonstrate the training performance on various smartphone models, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. We also profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos so can be scheduled intermittently on mobile devices.

Software-defined networks (SDNs) are novel networking architectures that decouple the network control and forwarding functions from the data plane. Unlike traditional networking, the control logic of SDNs is implemented in a logically centralized controller which provides a global network view and open programming interface to the applications. While SDNs have become a hot topic among both academia and industry in recent years, little attention has been paid on the security aspect. In this paper, we introduce a novel attack, namely, packet injection attack, in SDNs. By maliciously injecting manipulated packets into SDNs, attackers can affect the services and networking applications in the control plane, and largely consume the resources in the data plane. The consequences could be the disruption of applications built on the top of the topology manager service and rest API, as well as a huge consumption of network resources, such as the bandwidth of the OpenFlow channel. To defend against the packet injection attack, we present PacketChecker, a lightweight extension module on SDN controllers to effectively detect and mitigate the flooding of falsified packets. We implement a prototype of PacketChecker in floodlight controller and conduct experiments to evaluate the efficiency of the defense mechanism. The evaluation shows that the PacketChecker module can effectively mitigate the attack with a minor overhead to the SDN controller.

Virtual machine live migration has been studied for more than a decade, and this technique has been implemented in various commercial hypervisors. However, currently in the cloud environment, virtual machine migration is initiated by system administrators. Cloud customers have no say on this: They can not initiate a migration, and they do not even know whether or not their virtual machines have been migrated. In this paper, we propose the COMS framework, which is short for "Customer Oriented Migration Service". COMS gives more control to cloud customers so that migration becomes a service option and customers are more aware of the migration process. We have implemented a suite of modules in our COMS framework. Our evaluation results show that these modules could either bring performance benefit to cloud customers, or mitigate security threats in the cloud environment.

Container technology provides a lightweight operating system level virtual hosting environment. Its emergence profoundly changes the development and deployment paradigms of multi-tier distributed applications. However, due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, some security concerns still exist for multiple containers sharing an operating system kernel on a multi-tenancy container cloud service. In this paper, we first present the information leakage channels we discovered that are accessible within the containers. Such channels expose a spectrum of system-wide host information to the containers without proper resource partitioning. By exploiting such leaked host information, it becomes much easier for malicious adversaries (acting as tenants in the container clouds) to launch advanced attacks that might impact the reliability of cloud services. Additionally, we discuss the root causes of the containers' information leakages and propose a two-stage defense approach. As demonstrated in the evaluation, our solution is effective and incurs trivial performance overhead.

As the most commonly used bots in first-person shooter (FPS) online games, aimbots are notoriously difficult to detect because they are completely passive and resemble excellent honest players in many aspects. In this paper, we conduct the first field measurement study to understand the status quo of aimbots and how they play in the wild. For data collection purpose, we devise a novel and generic technique called baittarget to accurately capture existing aimbots from the two most popular FPS games. Our measurement reveals that cheaters who use aimbots cannot play as skillful as excellent honest players in all aspects even though aimbots can help them to achieve very high shooting performance. To characterize the unskillful and blatant nature of cheaters, we identify seven features, of which six are novel, and these features cannot be easily mimicked by aimbots. Leveraging this set of features, we propose an accurate and robust server-side aimbot detector called AimDetect. The core of AimDetect is a cascaded classifier that detects the inconsistency between performance and skillfulness of aimbots. We evaluate the efficacy and generality of AimDetect using the real game traces. Our results show that AimDetect can capture almost all of the aimbots with very few false positives and minor overhead.

As the limited battery lifetime remains a major factor restricting the applicability of a smartphone, significant research efforts have been devoted to understand the energy consumption in smartphones. Existing energy modeling methods can account energy drain in a fine-grained manner and provide well designed human-battery interfaces for users to characterize energy usage of every app in smartphones. However, in this paper, we demonstrate that there are still pitfalls in current Android energy modeling approaches, leaving collateral energy consumption unaccounted. The existence of collateral energy consumption becomes a serious energy bug. In particular, those energy bugs could be exploited to launch a new class of energy attacks, which deplete battery life and sidestep the supervision of current energy accounting. To unveil collateral energy bugs, we propose E-Android to accurately profile energy consumption of a smartphone in a comprehensive manner. E-Android monitors collateral energy related events and maintains energy consumption maps for relevant apps. We evaluate the effectiveness of E-Android under six different collateral energy attacks and two normal scenarios, and compare the results with those of Android. While Android fails to disclose collateral energy bugs, E-Android can accurately profile energy consumption and reveal the existence of energy bugs with minor overhead.

Smartphone users can conveniently install a set of apps that provide Location Based Service (LBS) from markets. These LBS-based apps facilitate users in many application scenarios, but they raise concerns on the breach of privacy related to location access. Smartphone users can hardly perceive location access, especially when it happens in background. In comparison to location access in foreground, location access in background could result in more serious privacy breach because it can continuously know a user's locations. In this paper, we study the problem of location access in background, and especially perform the first measurement of this background action on the Google app market. Our investigation demonstrates that many popular apps conduct location access in background within short intervals. This enables these apps to collect a user's location trace, from which the important personal information, Points of Interest (PoIs), can be recognized. We further extract a user's movement pattern from the PoIs, and utilize it to measure the extent of privacy breach. The measurement results also show that using the combination of movement pattern related metrics and the other PoI related metrics can help detect the privacy breach in an earlier manner than using either one of them alone.

The availability of smartphones is still severely restricted by the limited battery lifetime. To help users understand the energy consumption, major mobile platforms support fine-grained energy profiling for each app. In this paper, we present a new threat, called energy collateral attacks, which can abuse and mislead all existing energy modeling approaches. In particular, energy collateral attacks are able to divulge battery stealthily through interprocess communication, wakelock, and screen. To defend against those at- tacks, we propose E-Android to accurately profile the energy consumption in a comprehensive manner. E-Android monitors energy collateral related events and maintains energy consumption for relevant apps. We utilize E-Android to measure the energy consumption under the attack of six energy malware and two normal scenarios. While Android fails to disclose all these energy-malware-based attacks, E- Android can accurately profile energy consumption and re- veal the existence of energy malware.

No abstract available.

It is well-known that Android mobile advertising networks may abuse their host applications' permission to collect private information. Since the advertising library and host app are running in the same process, the current Android permission mechanism cannot prevent an ad network from collecting private data that is out of an ad network's permission range. In this paper, we propose PmDroid to protect the data that is not under the scope of the ad network's permission set. PmDroid can block the data from being sent to advertising servers at the occurrence of permission violation in ad networks. Moreover, we utilize PmDroid to assess how serious the permission violation problem is in the ad networks. We first implement 53 sample apps using a single ad network library. We grant all permissions of Android 4.3 to these apps and record the data sent to the Internet. Then, we further analyze 430 published market apps. In total, there are 76 ad networks identified in our experiments. We compare the permission of data received by these ad networks with their official documents. Our experimental results indicate that the permission violation is a real problem in existing ad network markets.