KingsGuard: Enclave Data Protection Under Real-World TEE Vulnerabilities
- URL: http://arxiv.org/abs/2605.00613v2
- Date: Wed, 06 May 2026 06:09:08 GMT
- Title: KingsGuard: Enclave Data Protection Under Real-World TEE Vulnerabilities
- Abstract summary: KINGSGUARD is a novel TEE design that monitors and controls the propagation of sensitive data within an enclave.<n>Our implementation of KINGSGUARD on a RISC-V processor has a 10.8% hardware area overhead when synthesized on FPGA and a 5.69% performance overhead.
- Score: 3.136502393417242
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Trusted Execution Environments (TEEs) have emerged as a cornerstone for securing sensitive computations by providing isolated enclaves protected from untrusted software. However, their security guarantees are undermined by vulnerabilities in both the enclave code and the underlying hardware design, which can allow sensitive data to leak despite strong isolation guarantees. This paper presents KINGSGUARD, a novel TEE design that systematically monitors and controls the propagation of sensitive data within an enclave. By enforcing fine-grained data flow tracking and checks in hardware, our approach ensures that sensitive data does not leave the enclave boundary, thus bridging the gap between the idealized threat models of TEEs and their practical realizations. Additionally, to balance security with practical functionality, we introduce controlled declassification at enclave boundaries, allowing intentional release of data to the outside world. Our implementation of KINGSGUARD on a RISC-V processor has a 10.8% hardware area overhead when synthesized on FPGA and a 5.69% performance overhead.
Related papers
- More Granular, Less Trust: Enforcing Intra-Process Isolation with Arm CCA in an Untrusted Management Environment [15.118579654055603]
CCAegis is a system that extends the Arm Confidential Compute Architecture (CCA) to enforce intra-process isolation of sensitive data and operations.<n>We show that CCAegis effectively isolates sensitive data and operations, with performance overheads ranging from 1.01x to 1.43x compared to the original version across real-world cryptographic workloads.
arXiv Detail & Related papers (2026-08-20T21:42:16Z) - AmbSentry: Mitigating Sensing Eavesdropping in ISAC Systems by Harnessing Ambient IoT Devices [60.083979780396014]
Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks.<n>This paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers.
arXiv Detail & Related papers (2026-08-12T08:42:05Z) - RAIN: Secure and Robust Aggregation under Shuffle Model of Differential Privacy [46.52109845749167]
We present Robust Aggregation in Noise (RAIN), a framework that reconciles privacy, robustness, and verifiability under Shuffle-DP.<n>RAIN adopts sign-space aggregation to robustly measure update consistency and limit malicious influence under noise and anonymization.<n>We show that RAIN maintains strong privacy guarantees under Shuffle-DP and remains robust to poisoning attacks with negligible degradation in accuracy and convergence.
arXiv Detail & Related papers (2026-03-03T15:41:54Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment [45.772620696660034]
Safety Optimal Transport (SOT) is a novel framework that reframes safe fine-tuning from an instance-level filtering challenge to a distribution-level alignment task grounded in Optimal Transport (OT)<n>SOT prioritizes sample importance by actively pulling the downstream distribution towards a trusted safe anchor while simultaneously pushing it away from a general harmful reference.<n>Experiments across diverse model families and domains demonstrate that SOT significantly enhances model safety while maintaining competitive downstream performance.
arXiv Detail & Related papers (2026-01-12T04:48:02Z) - Securing Generative AI in Healthcare: A Zero-Trust Architecture Powered by Confidential Computing on Google Cloud [0.0]
Confidential Zero-Trust Framework (CZF) is a security paradigm that combines Zero-Trust Architecture for granular access control with the hardware-enforced data isolation of Confidential Computing.<n>CZF provides a defense-in-depth architecture where data remains encrypted while in-use within a hardware-based Trusted Execution Environment.
arXiv Detail & Related papers (2025-11-14T19:56:52Z) - Coverage-Guided Pre-Silicon Fuzzing of Open-Source Processors based on Leakage Contracts [1.2413165648298643]
Hardware-software leakage contracts have emerged as a formalism for specifying side-channel security guarantees of modern processors.<n>Current verification approaches struggle to scale to industrial-sized designs.<n>We introduce a novel and scalable approach: coverage-guided hardware-software contract fuzzing.
arXiv Detail & Related papers (2025-11-11T16:46:35Z) - SecureInfer: Heterogeneous TEE-GPU Architecture for Privacy-Critical Tensors for Large Language Model Deployment [9.666696979829359]
SecureInfer is a framework that offloads compute-intensive operations to untrusted accelerators.<n>We implement a prototype of SecureInfer using the LLaMA-2 model and evaluate it across performance and security metrics.
arXiv Detail & Related papers (2025-10-22T19:17:31Z) - CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
arXiv Detail & Related papers (2025-07-19T20:09:52Z) - Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses [42.136793654338106]
We introduce a new safety evaluation framework based on impermissible information leakage of model outputs.
We show that to ensure safety against inferential adversaries, defense mechanisms must ensure information censorship.
arXiv Detail & Related papers (2024-07-02T16:19:25Z) - Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution [8.92716309877259]
Federated Learning (FL) and Local Differential Privacy (LDP) have attracted much attention over the past few years.<n>They share the common limitation of being vulnerable to poisoning attacks.<n>We propose a system-level approach to remedy this issue based on a novel security notion of Proofs of Stateful Execution.
arXiv Detail & Related papers (2024-04-10T04:18:26Z) - HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs)
TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks.
We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
arXiv Detail & Related papers (2024-01-17T00:56:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.