搜索结果：Trusted

Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operating System (Trusted OS) is the primary component. It enables the TEE to use security-based design techniques, such as data encryption and identity authentication. Once a Trusted OS has been exploited, the TEE can no longer ensure security. However, Trusted OSes for IoT devices have received little security analysis, which is challenging from several perspectives: (1) Trusted OSes are closed-source and have an unfavorable environment for sending test cases and collecting feedback. (2) Trusted OSes have complex data structures and require a stateful workflow, which limits existing vulnerability detection tools. To address the challenges, we present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices as well as tracking state and code coverage non-invasively. SyzTrust utilizes composite feedback to guide the fuzzer to effectively explore more states as

We introduces a category-theoretic framework for modelling trust as applied to trusted computation systems and remote attestation. By formalizing elements, claims, results, and decisions as objects within a category, and the processes of attestation, verification, and decision-making as morphisms, the framework provides a rigorous approach to understanding trust establishment and provides a well-defined semantics for terms such as `trustworthiness' and 'justification'/forensics. The trust decision space is formalized using a Heyting Algebra, allowing nuanced trust levels that extend beyond binary trusted/untrusted states. We then present additional structures and in particular utilise exponentiation in a category theoretic sense to define compositions of attestation operations and provide the basis of a measurement for the expressibility of an attestation environment. We present a number of worked examples including boot-run-shutdown sequences, Evil Maid attacks and the specification of an attestation environment based upon this model. We then address challenges in modelling dynamic and larger systems made of multiple compositions.

We identify a fundamental incompatibility between the goals of accuracy, trust, and human-level reasoning in artificial intelligence (AI) systems, for strict mathematical definitions of these notions. We define accuracy of a system as the property that it never makes any false claims when it has the ability to abstain from making a prediction on any input, and trust as the assumption that the system is accurate. We define human-level reasoning as the property of an AI system always matching or exceeding human capability. Our core finding is that -- for our formal definitions of these notions -- an accurate and trusted AI system cannot be a human-level reasoning system: for such an accurate, trusted system there are task instances which are easily and provably solvable by a human but not by the system. Our proofs draw parallels to Gödel's incompleteness theorems and Turing's proof of the undecidability of the halting problem, and can be regarded as interpretations of Gödel's and Turing's results. Key to our proof is the formalization of the notion of trust, which allows us to separate the intrinsic property of a system (being accurate) from its epistemic status (being trusted).

AI agents powered by large language models are increasingly deployed as cloud services that autonomously access sensitive data, invoke external tools, and interact with other agents. However, these agents run within a complex multi-party ecosystem, where untrusted components can lead to data leakage, tampering, or unintended behavior. Existing Confidential Virtual Machines (CVMs) provide only per binary protection and offer no guarantees for cross-principal trust, accelerator-level isolation, or supervised agent behavior. We present Omega, a system that enables trusted AI agents by enforcing end-to-end isolation, establishing verifiable trust across all contributing principals, and supervising every external interaction with accountable provenance. Omega builds on Confidential VMs and Confidential GPUs to create a Trusted Agent Platform that hosts many agents within a single CVM using nested isolation. It also provides efficient multi-agent orchestration with cross-principal trust establishment via differential attestation, and a policy specification and enforcement framework that governs data access, tool usage, and inter-agent communication for data protection and regulatory comp

With the advent of delegated quantum computing as a service, verifying quantum computations is becoming a question of great importance. Existing information theoretically Secure Delegated Quantum Computing (SDQC) protocols require the client to possess the ability to perform either trusted state preparations or measurements. Whether it is possible to verify universal quantum computations with information-theoretic security without trusted preparations or measurements was an open question so far. In this paper, we settle this question in the affirmative by presenting a modular, composable, and efficient way to turn known verification schemes into protocols that rely only on trusted gates. Our first contribution is an extremely lightweight reduction of the problem of quantum verification for BQP to the trusted application of single-qubit rotations around the Z axis and bit flips. The second construction presented in this work shows that it is generally possible to information-theoretically verify arbitrary quantum computations with quantum output without trusted preparations or measurements. However, this second protocol requires the verifier to perform multi-qubit gates on a registe

We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite Trusted Execution Environments (TEEs) providing multiple security functionalities, timestamps from the Operating System are not covered. Nevertheless, some services require time for validating permissions or ordering events. To address that need, we introduce Triad, a trusted timestamp dispatcher of time readings. The solution provides trusted timestamps enforced by mutually supportive enclave-based clock servers that create a continuous trusted timeline. We leverage enclave properties such as forced exits and CPU-based counters to mitigate attacks on the server's timestamp counters. Triad produces trusted, confidential, monotonically-increasing timestamps with bounded error and desirable, non-trivial properties. Our implementation relies on Intel SGX and SCONE, allowing transparent usage. We evaluate Triad's error and behavior in multiple dimensions.

A lease is an important primitive for building distributed protocols, and it is ubiquitously employed in distributed systems. However, the scope of the classic lease abstraction is restricted to the trusted computing infrastructure. Unfortunately, this important primitive cannot be employed in the untrusted computing infrastructure because the trusted execution environments (TEEs) do not provide a trusted time source. In the untrusted environment, an adversary can easily manipulate the system clock to violate the correctness properties of lease-based systems. We tackle this problem by introducing trusted lease -- a lease that maintains its correctness properties even in the presence of a clock-manipulating attacker. To achieve these properties, we follow a "trust but verify" approach for an untrusted timer, and transform it into a trusted timing primitive by leveraging two hardware-assisted ISA extensions (Intel TSX and SGX) available in commodity CPUs. We provide a design and implementation of trusted lease in a system called T-Lease -- the first trusted lease system that achieves high security, performance, and precision. For the application developers, T-Lease exposes an easy-to

AI systems for software development are rapidly gaining prominence, yet significant challenges remain in ensuring their safety. To address this, Amazon launched the Trusted AI track of the Amazon Nova AI Challenge, a global competition among 10 university teams to drive advances in secure AI. In the challenge, five teams focus on developing automated red teaming bots, while the other five create safe AI assistants. This challenge provides teams with a unique platform to evaluate automated red-teaming and safety alignment methods through head-to-head adversarial tournaments where red teams have multi-turn conversations with the competing AI coding assistants to test their safety alignment. Along with this, the challenge provides teams with a feed of high quality annotated data to fuel iterative improvement. Throughout the challenge, teams developed state-of-the-art techniques, introducing novel approaches in reasoning-based safety alignment, robust model guardrails, multi-turn jail-breaking, and efficient probing of large language models (LLMs). To support these efforts, the Amazon Nova AI Challenge team made substantial scientific and engineering investments, including building a c

Due to the scalability and portability, the low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high dynamic mobility, and vulnerable to security threats, which may degrade the routing performance for data transmission. Hence, how to ensure the routing stability and security of LAINs is a challenge. In this paper, we focus on the routing process in LAINs with multiple UAV clusters and propose the blockchain-enabled zero-trust architecture to manage the joining and exiting of UAVs. Furthermore, we formulate the routing problem to minimize the end-to-end (E2E) delay, which is an integer linear programming and intractable to solve. Therefore, considering the distribution of LAINs, we reformulate the routing problem into a decentralized partially observable Markov decision process. With the proposed soft hierarchical experience replay buffer, the multi-agent double deep Q-network based adaptive routing algorithm is designed. Finally, simulations are conducted and numerical results show that the total E2E delay of the propos

The majority of mobile devices today are based on Arm architecture that supports the hosting of trusted applications in Trusted Execution Environment (TEE). RISC-V is a relatively new open-source instruction set architecture that was engineered to fit many uses. In one potential RISC-V usage scenario, mobile devices could be based on RISC-V hardware. We consider the implications of porting the mobile security stack on top of a RISC-V system on a chip, identify the gaps in the open-source Keystone framework for building custom TEEs, and propose a security architecture that, among other things, supports the GlobalPlatform TEE API specification for trusted applications. In addition to Keystone enclaves the architecture includes a Trusted Hart -- a normal core that runs a trusted operating system and is dedicated for security functions, like control of the device's keystore and the management of secure peripherals. The proposed security architecture for RISC-V platform is verified experimentally using the HiFive Unleashed RISC-V development board.

Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, claim contextualization, and bias in prompt formulation. Applying the protocol to PaLM-2, we find that as we scale up the model size, the model performance on FactCheckQA improves from near-random to up to 80% balanced accuracy in aligning with trusted sources.

Hardware-based Trusted Execution Environments (TEEs) are widely deployed in mobile devices. Yet their use has been limited primarily to applications developed by the device vendors. Recent standardization of TEE interfaces by GlobalPlatform (GP) promises to partially address this problem by enabling GP-compliant trusted applications to run on TEEs from different vendors. Nevertheless ordinary developers wishing to develop trusted applications face significant challenges. Access to hardware TEE interfaces are difficult to obtain without support from vendors. Tools and software needed to develop and debug trusted applications may be expensive or non-existent. In this paper, we describe Open-TEE, a virtual, hardware-independent TEE implemented in software. Open-TEE conforms to GP specifications. It allows developers to develop and debug trusted applications with the same tools they use for developing software in general. Once a trusted application is fully debugged, it can be compiled for any actual hardware TEE. Through performance measurements and a user study we demonstrate that Open-TEE is efficient and easy to use. We have made Open- TEE freely available as open source.

Secure and trustworthy execution in heterogeneous SoCs is a major priority in the modern computing system. Security of SoCs mainly addresses two broad layers of trust issues: 1. Protection against hardware security threats(Side-channel, IP Privacy, Cloning, Fault Injection, and Denial of Service); and 2. Protection against malicious software attacks running on SoC processors. To resist malicious software-level attackers from gaining unauthorized access and compromising security, we propose a root of trust-based trusted execution mechanism \textbf{\textit{(named as \textbf{TrustToken}) }}. TrustToken builds a security block to provide a root of trust-based IP security: secure key generation and truly random source. \textbf{TrustToken} only allows trusted communication between the non-trusted third-party IP and the rest of the SoC world by providing essential security features, i.e., secure, isolated execution, and trusted user interaction. The proposed design achieves this by interconnecting the third-party IP interface to \textbf{TrustToken} Controller and checking IP authorization(Token) signals \texttt{`correctness'} at run-time. \textbf{TrustToken} architecture shows a very low

The growing availability of hardware-based trusted execution environments (TEEs) in commodity processors has recently advanced support (i.e., design, implementation and deployment frameworks) for network-based secure services. Examples of such TEEs include ARM TrustZone or Intel SGX, largely available in embedded, mobile and server-grade processors. TEEs shield services from compromised hosts, malicious users or powerful attackers. TEE-enabled devices are largely being deployed on the edge of the network, paving the way for large-scale deployments of trusted applications. These applications allow processing and disseminating sensitive data without having to trust cloud providers. However, uncovering network performance limitations of such trusted applications is difficult and currently lacking, despite the interest and reliance by developers and system deployers. iperfTZ is an open-source tool to uncover network performance bottlenecks rooted at the design and implementation of trusted applications for ARM TrustZone and underlying runtime systems. Our evaluation based on micro-benchmarks shows current trade-offs for trusted applications, both from a network as well as an energy per

Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way to change this situation, as it provides a measure of confidence in the segmentation results. In this paper, we propose a trusted brain tumor segmentation network which can generate robust segmentation results and reliable uncertainty estimations without excessive computational burden and modification of the backbone network. In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution. Meanwhile, the trusted segmentation framework learns the function that gathers reliable evidence from the feature leading to the final segmentation results. Overall, our unified trusted segmentation framework endows the model with reliability and robustness to out-of-distribution samples. To evaluate the effectiveness of our model in robustness and reliability, qualitative and quantitative experiments are conducted on the BraTS 2019 d

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conce

This paper describes the design, implementation, and evaluation of Otak, a system that allows two non-colluding cloud providers to run machine learning (ML) inference without knowing the inputs to inference. Prior work for this problem mostly relies on advanced cryptography such as two-party secure computation (2PC) protocols that provide rigorous guarantees but suffer from high resource overhead. Otak improves efficiency via a new 2PC protocol that (i) tailors recent primitives such as function and homomorphic secret sharing to ML inference, and (ii) uses trusted hardware in a limited capacity to bootstrap the protocol. At the same time, Otak reduces trust assumptions on trusted hardware by running a small code inside the hardware, restricting its use to a preprocessing step, and distributing trust over heterogeneous trusted hardware platforms from different vendors. An implementation and evaluation of Otak demonstrates that its CPU and network overhead converted to a dollar amount is 5.4$-$385$\times$ lower than state-of-the-art 2PC-based works. Besides, Otak's trusted computing base (code inside trusted hardware) is only 1,300 lines of code, which is 14.6$-$29.2$\times$ lower th

We address the role of the phase-insensitive trusted preparation and detection noise in the security of a continuous-variable quantum key distribution, considering the Gaussian protocols on the basis of coherent and squeezed states and studying them in the conditions of Gaussian lossy and noisy channels. The influence of such a noise on the security of Gaussian quantum cryptography can be crucial, even despite the fact that a noise is trusted, due to a strongly nonlinear behavior of the quantum entropies involved in the security analysis. We recapitulate the known effect of the preparation noise in both direct and reverse-reconciliation protocols, as well as the detection noise in the reverse-reconciliation scenario. As a new result, we show the negative role of the trusted detection noise in the direct-reconciliation scheme. We also describe the role of the trusted preparation or detection noise added at the reference side of the protocols in improving the robustness of the protocols to the channel noise, confirming the positive effect for the coherent-state reverse-reconciliation protocol. Finally, we address the combined effect of trusted noise added both in the source and the d

The fifth generation (5G) of cellular networks starts a paradigm shift from the traditional monolithic system design to a Service Based Architecture, that fits modern performance requirements and scales efficiently to new services. This paradigm will be the foundation of future cellular core networks beyond 5G. The new architecture splits network functionalities into smaller logical entities that can be disaggregated logically, physically, and geographically. This affords interoperability between the mobile network operators and commercial software and hardware vendors or cloud providers. By making use of commodity services and products, this system construct inherits the vulnerabilities in those underlying technologies, thereby increasing its attack surface and requiring a rigorous security analysis. In this work, we review the security implications introduced in B5G networks, and the security mechanisms that are supported by the 5G standard. We emphasize on the support of Zero Trust Architecture in 5G and its relevance in decentralized deployments. We revisit the definition of trust in modern enterprise network operations and identify important Zero Trust properties that are weak