We evaluate a bundle of specifications from the Self-Sovereign Identity (SSI) paradigm to construct an authentication protocol for the Web. We demonstrate how relevant standards such as W3C Verifiable Credentials (VC), W3C Decentralised Identifiers (DIDs), and components of the Hyperledger Aries Framework are to be assembled methodologically into a protocol. We make those assumptions from standard trust models explicit that underlie the derived protocol, and verify security and privacy properties, notably secrecy, authentication, and unlinkability. This enables us to formally justify the additional precision that we urge these specifications to consider, to ensure that implementors of SSI-based systems do not neglect security-critical controls.
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To detect unknown attack traffic, anomaly-based network intrusion detection systems (NIDSs) are widely used in Internet infrastructure. However, the security communities realize some limitations when they put most existing proposals into practice. The challenges are mainly concerned with (i) fine-grained emerging attack detection and (ii) incremental updates/adaptations. To tackle these problems, we propose to decouple the need for model capabilities by transforming known/new class identification issues into multiple independent one-class learning tasks. Based on the above core ideas, we develop Trident, a universal framework for fine-grained unknown encrypted traffic detection. It consists of three main modules, i.e., tSieve, tScissors, and tMagnifier are used for profiling traffic, determining outlier thresholds, and clustering respectively, each of which supports custom configuration. Using four popular datasets of network traces, we show that Trident significantly outperforms 16 state-of-the-art (SOTA) methods. Furthermore, a series of experiments (concept drift, overhead/parameter evaluation) demonstrate the stability, scalability, and practicality of Trident.
File upload is a critical feature incorporated by a myriad of web applications in an effort to enable users to share and manage their files conveniently. It has been used in many useful services such as file-sharing and social media. While file upload is an essential component of web applications, the lack of rigorous checks on the file name, type, and content of the uploaded files can result in security issues, often referred to as Unrestricted File Upload (UFU). In this study, we analyze the (in)security of popular file upload libraries and real-world applications in the Node.js ecosystem. To automate our analysis, we propose and implement NodeSEC- a tool designed to analyze file upload insecurities in Node.js applications and libraries. NodeSEC generates unique payloads and thoroughly evaluates the application's file upload security against 13 distinct UFU-type attacks. Utilizing NodeSEC, we analyze the most popular file upload libraries and real-world applications in the Node.js ecosystem. Our analysis results reveal that some real-world web applications are vulnerable to UFU attacks and disclose serious security bugs in file upload libraries. As of this writing, we received 19 CVEs and two US-CERT cases for the security issues that we reported. Our findings provide strong evidence that dynamic features of Node.js applications introduce security shortcomings and that web developers should be cautious when implementing file upload features in their applications. Finally, combining our responsible disclosure experience and root cause analysis, we identified the main causes of significant security weaknesses in file uploads in Node.js.
Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.
Investigating how websites use sensitive user data is an active research area. However, research based on automated measurements has been limited to those websites that do not require user authentication. To overcome this limitation, we developed a crawler that automates website registrations and newsletter subscriptions and detects both security and privacy threats at scale. We demonstrate our crawler's capabilities by running it on 660k websites. We use this to identify security and privacy threats and to contextualize them within EU laws, namely the General Data Protection Regulation and ePrivacy Directive. Our methods detect private data collection over insecure HTTP connections and websites sending emails with user-provided passwords. We are also the first to apply machine learning to web forms, assessing violations of marketing consent collection requirements. Overall, we find that 37.2% of websites send marketing emails without proper user consent. This is mostly caused by websites failing both to verify and store consent adequately. Additionally, 1.8% of websites share users' email addresses with third parties without a transparent disclosure.
Browser extensions offer a variety of valuable features and functionalities. They also pose a significant security risk if not properly designed or reviewed. Prior works have shown that browser extensions can access and manipulate data fields, including sensitive data such as passwords, credit card numbers, and Social Security numbers. In this paper, we present an empirical study of the security risks posed by browser extensions. Specifically, we first build a proof-of-concept extension that can steal sensitive user information. We find that the extension passes the Chrome Webstore review process. We then perform a measurement study on the top 10K website login pages to check if the extension access to password fields via JS. We find that none of the password fields are actively protected, and can be accessed using JS. Moreover, we found that 1K websites store passwords in plaintext in their page source, including popular websites like Google.com and Cloudflare.com. We also analyzed over 160K Chrome Web Store extensions for malicious behavior, finding that 28K have permission to access sensitive fields and 190 store password fields in variables. To analyze the behavioral workflow of the potentially malicious extensions, we propose an LLM-driven framework, Extension Reviewer. Finally, we discuss two countermeasures to address these risks: a bolt-on JavaScript package for immediate adoption by website developers allowing them to protect sensitive input fields, and a browser-level solution that alerts users when an extension accesses sensitive input fields. Our research highlights the urgent need for improved security measures to protect sensitive user information online.
Given a collection of vectors \boldsymbolx ^(1), \dots,\boldsymbolx ^(n) \in \0,1\ ^d, the selection problem asks to report the index of an "approximately largest'' entry in \boldsymbolx =\sum_j=1 ^n \boldsymbolx ^(j) . Selection abstracts a host of problems, for example: Recommendation of a popular item based on user feedback; releasing statistics on the most popular web sites; hyperparameter tuning and feature selection in machine learning. We study selection under differential privacy, where a released index guarantees privacy for individual vectors. Though selection can be solved with an excellent utility guarantee in the central model of differential privacy, the distributed setting where no single entity is trusted to aggregate the data lacks solutions. Specifically, strong privacy guarantees with high utility are offered in high trust settings, but not in low trust settings. For example, in the popular shuffle model of distributed differential privacy, there are strong lower bounds suggesting that the utility of the central model cannot be obtained. In this paper we design a protocol for differentially private selection in a trust setting similar to the shuffle model---with the crucial difference that our protocol tolerates corrupted servers while maintaining privacy. Our protocol uses techniques from secure multi-party computation (MPC) to implement a protocol that: (i) has utility on par with the best mechanisms in the central model, (ii) scales to large, distributed collections of high-dimensional vectors, and (iii) uses k\geq 3 servers that collaborate to compute the result, where the differential privacy guarantee holds assuming an honest majority. Since general-purpose MPC techniques are not sufficiently scalable, we propose a novel application of integer secret sharing, and evaluate the utility and efficiency of our protocol both theoretically and empirically. Our protocol improves on previous work by Champion, shelat and Ullman (CCS '19) by significantly reducing the communication costs, demonstrating that large-scale differentially private selection with information-theoretical guarantees is feasible in a distributed setting.
Unlike fungible tokens (e.g., cryptocurrency), a Non-Fungible Token (NFT) is unique and indivisible. As such, they can be used to authenticate ownership of digital assets (e.g., a photo) in a decentralized fashion. Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. We collect over 242M transfer logs and over 97M marketplace transactions until Aug 1st, 2023, by far the largest NFT dataset, to the best of our knowledge. We characterize the on-chain behavior of NFTs and their trading across five major marketplaces. We find that, although the NFT ecosystem is growing rapidly, it is driven by a relatively small set of dominant centralized players, with suspicious trade activities, e.g., over 23% of the monetary volume is generated by malicious wash trading and the ecosystem has experienced over 157K cases of NFT arbitrage, with a total sum of over \25M profit. Our observations motivate the need for more research efforts in the NFT security analysis.
With the advent of Bitcoin, a cryptographically-enabled peer-to-peer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, process automation and secure data sharing across otherwise isolated data silos. Furthermore, as the recognition of the data value began to sink in, data assets has become an essential part of the development of enterprises and countries. Blockchain technology is regarded as the foundation of digital economy and provides an effective approach for data ownership, pricing and transactions, which are the core issues of data asset management. However, the potential implications of Blockchain technologies go far beyond their application as the technological backbone for cryptocurrencies. Web3.0, using blockchain as underlying technology, allow for various novel application scenarios, which are built upon distributed consensus and thus are hard to block or censor while providing public verifiability of peer-to-peer transactions without a trusted central party. Web3.0 are expected to become the main front for a plethora of highly expressive applications. To more thoroughly explore the potential of blockchain and web3.0 and promote their progress, SDBD'24 will provide a forum for the most recent blockchain and web3.0 research, innovations, and applications, bridging the gap between theory and practice in the design.
Cybersecurity remains a grand societal challenge. Large and constantly changing attack surfaces are non-trivial to protect against malicious actors. Entities like the United States and the European Union have recently emphasized the value of Artificial Intelligence (AI) for advancing cybersecurity. For example, the National Science Foundation has called for AI systems that can enhance cyber threat intelligence, detect new and evolving threats, and analyze massive troves of cybersecurity data. The 4th Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (co-located with ACM KDD) sought to make significant and novel contributions within these relevant topics. Submissions were reviewed by highly qualified AI for cybersecurity researchers and practitioners spanning academia and private industry firms.
In recent years, there have been exciting and accelerated developments in AI with novel developments in foundation models, deep learning, new AI applications across numerous verticals, and more. In addition, the pace of adoption of these innovations driven by both academic and industry research labs has sped up with both big tech companies and startups looking to deliver value-differentiated products and services. With Generative AI (GenAI) garnering significant attention, the second edition of the I2S workshop focuses on two aspects: First, bringing together AI thought leaders from academia, big tech, and startups to discuss the opportunities, use-case themes, challenges, and risks of GenAI in various business verticals; and Second, bringing together startup founders to share experiences and lessons learned in commercializing GenAI innovations into successful enterprises highlighting challenges through the entire commercial journey - from productization to acquiring customers, building a team, and securing funding.
Automated systems must adapt to evolving environments, yet many struggle with drift phenomena affecting healthcare, finance, and cybersecurity domains. The DELTA workshop addresses this by distinguishing between data and concept drift, aiming to create a practical, human-centric framework for managing drift. The workshop seeks innovative drift detection, prediction, and analysis solutions by uniting researchers and practitioners. DELTA fosters collaboration to advance the understanding and management of drift in dynamic data landscapes by featuring keynotes, paper presentations, interactive sessions, and discussions.
Over the last decades, a rapidly growing volume of spatiotemporal data has been collected from smartphones and GPS, terrestrial, seaborne, airborne, and spaceborne sensors, as well as computational simulations. Meanwhile, advances in deep learning technologies, especially the recent breakthroughs of generative AI and foundation models such as Large Language Models (LLMs) and Large Vision Models (LVMs), have achieved tremendous success in natural language processing and computer vision applications. There is growing anticipation of the same level of accomplishment of AI on spatiotemporal data in tackling grand societal challenges, such as national water resource management, monitoring coastal hazards, energy and food security, as well as mitigation and adaptation to climate change. When deep learning, especially emerging foundation models, intersects spatiotemporal data in scientific domains, it opens up new opportunities and challenges. The workshop aims to bring together academic researchers in both AI and scientific domains, government program managers, leaders from non-profit organizations, as well as industry executives to brainstorm and debate on the emerging opportunities and novel challenges of deep learning (foundation models) for spatiotemporal data inspired by real-world scientific applications.
Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)
PDF ↗With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has become crucial. It is essential to evaluate and monitor AI systems not only for accuracy and quality-related metrics but also for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement. In this survey article accompanying our tutorial, we highlight a wide range of harms associated with generative AI systems, and survey state of the art approaches (along with open challenges) to address these harms.
Findable. Accessible. Interoperable. Reusable. Since their introduction in 2016, the FAIR data principles have defined the standards by which scientific researchers share data. However, modern research in data editing and management consistently shows that while the FAIR data principles are widely accepted in theory, they can be much more difficult to understand and implement in practice. In this tutorial, we explore some of the simple, realistic steps scientists can take to FAIRly release open data. We also explore areas where the current FAIR guidelines fall short and offer practical suggestions for making open data FAIR(ER): more Equitable and Realistic. This first involves ways to make datasets themselves more equitably accessible for researchers with disabilities. While equitably accessible data design has some research overlap with paper, presentation, and website design, we suggest several unique distinctions specific to datasets. The "Realistic'' aspect of FAIR(ER) data facilitates a path to translate open data (and research on that data) back to true applications. Driven by national security applications pipelines, we call out important considerations for balancing data editing against data realism.
AI projects often face the challenge of limited access to meaningful amounts of training data. In traditional approaches, collecting data in a central location can be problematic, especially in industry settings with sensitive and distributed data. However, there is a solution -"moving the computation to the data" through Federated Learning. Federated Learning, a distributed machine learning approach, offers a promising solution by enabling model training across devices. It is a data minimization approach where direct access to data is not required. Furthermore, federated learning can be combined with techniques like differential privacy, secure aggregation, homomorphic encryption, and others, to further enhance privacy protection. In this hands-on tutorial, we delve into the realm of privacy-preserving machine learning using federated learning, leveraging the Flower framework which is specifically designed to simplify the process of building federated learning systems, as our primary tool. Moreover, we present the foundations of federated learning, explore how different techniques can enhance its privacy aspects, how it is being used in real-world settings today and a series of practical, hands-on code examples that showcase how you can federate any AI project with Flower, an open-source framework for all-this federated.
Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
PDF ↗Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.
The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage numerous existing high-performing models instead of building machine learning models from scratch. This paradigm envisions that: Any developer worldwide can submit their well-trained models spontaneously into a learnware dock system (formerly known as learnware market). The system uniformly generates a specification for each model to form a learnware and accommodates it. As the key component, a specification should represent the capabilities of the model while preserving developer's original data. Based on the specifications, the learnware dock system can identify and assemble existing learnwares for users to solve new machine learning tasks. Recently, based on reduced kernel mean embedding (RKME) specification, a series of studies have shown the effectiveness of the learnware paradigm theoretically and empirically. However, the realization of a learnware dock system is still missing and remains a big challenge. This paper proposes Beimingwu, the first open-source learnware dock system, providing foundational support for future research. The system provides implementations and extensibility for the entire process of learnware paradigm, including the submitting, usability testing, organization, identification, deployment, and reuse of learnwares. Utilizing Beimingwu, the model development for new user tasks can be significantly streamlined, thanks to integrated architecture and engine design, specifying unified learnware structure and scalable APIs, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. The system facilitates the future research implementations in learnware-related algorithms and systems, and lays the ground for hosting a vast array of learnwares and establishing a learnware ecosystem. The system is fully open-source and we expect the research community to benefit from the system. The system and research toolkit have been released on GitLink and GitHub.
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
PDF ↗Accurate solar power forecasting is crucial to integrate photovoltaic plants into the electric grid, schedule and secure the power grid safety. This problem becomes more demanding for those newly installed solar plants which lack sufficient operational data. Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities. In this paper, we propose a multi-modality fusion framework to integrate historical power data, numerical weather prediction, and satellite images, significantly improving forecast performance. We introduce a vector quantized framework that aligns modalities with varying information densities, striking a balance between integrating sufficient information and averting model overfitting. Our framework demonstrates strong zero-shot forecasting capability, which is especially useful for those newly installed plants. Moreover, we collect and release a multi-modal solar power (MMSP) dataset from real-world plants to further promote the research of multi-modal solar forecasting algorithms. Our extensive experiments show that our model not only operates with robustness but also boosts accuracy in both zero-shot forecasting and scenarios rich with training data, surpassing leading models. We have incorporated it into our eForecaster platform and deployed it for more than 300 solar plants with a total capacity of over 15GW. Our code and dataset are accessible at https://github.com/DAMO-DI-ML/FusionSF.git.