arXiv subjects

Ahmad Atamli

Publications and source records attributed to Ahmad Atamli.

6 recordsLinked to original sources

arXiv2026

WarpGuard: Towards Control-Flow Attestation for Heterogeneous CPU-GPU Execution

Heterogeneous CPU-GPU workloads are increasingly used in safety-critical embedded systems, yet no existing approach provides joint attestation of their execution. Prior Control-Flow Attestation (CFA) techniques focus on CPU-side CFA, while GPU attestation is limited to static, load-time verification and does not provide runtime guarantees. As a result, runtime attacks on GPU kernels and violations of the CPU-GPU interaction contract remain unaddressed. We present WarpGuard, the first composite CFA framework for heterogeneous CPU-GPU workloads. WarpGuard verifies execution against a unified control-flow graph (CFG) that captures both CPU and GPU components. It extends prior CFA techniques in two ways: it enables runtime CFA of GPU kernels by tracing their execution against kernel-specific CFGs, and it monitors kernel launch events and enforces per-call site policies to detect violations at the CPU-GPU boundary. These extensions address challenges arising from GPU parallelism and cross-device interactions. We implement WarpGuard using software-based instrumentation, requiring no specialized hardware or binary modifications. Our evaluation on an NVIDIA Jetson Orin Nano shows that WarpGuard detects GPU-side control-flow and cross-boundary attacks. Across microbenchmarks, SPECAccel, and eight TensorRT inference workloads, WarpGuard incurs moderate overheads, suggesting practicality for embedded safety-critical settings.

cs.CR↗

arXiv2025

Hazel: Secure and Efficient Disaggregated Storage

Disaggregated storage with NVMe-over-Fabrics (NVMe-oF) has emerged as the standard solution in modern supercomputers and data center clusters, achieving superior performance, resource utilization, and power efficiency. Simultaneously, confidential computing (CC) is becoming the de facto security paradigm, enforcing stronger isolation and protection for sensitive workloads. However, securing state-of-the-art storage with traditional CC methods struggles to scale and compromises performance or security. To address these issues, we introduce Hazel, a storage management system that extends the NVMe-oF protocol capabilities and adheres to the CC threat model, providing confidentiality, integrity, and freshness guarantees. Hazel offers an appropriate control path with novel concepts such as counter-leasing. Hazel also optimizes data path performance by leveraging NVMe metadata and introducing a new disaggregated Hazel Merkle Tree (HMT), all while remaining compatible with NVMe-oF. For additional efficiency, Hazel also supports offloading to CC-capable smart NIC accelerators. We prototype Hazel on an NVIDIA BlueField-3 and demonstrate that it can achieve as little as 1-2% performance degradation for synthetic patterns, AI training, IO500, and YCSB.

cs.CR↗

arXiv2024

DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs

This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework's hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence's effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.

cs.CR↗

arXiv2024

One for All and All for One: GNN-based Control-Flow Attestation for Embedded Devices

Control-Flow Attestation (CFA) is a security service that allows an entity (verifier) to verify the integrity of code execution on a remote computer system (prover). Existing CFA schemes suffer from impractical assumptions, such as requiring access to the prover's internal state (e.g., memory or code), the complete Control-Flow Graph (CFG) of the prover's software, large sets of measurements, or tailor-made hardware. Moreover, current CFA schemes are inadequate for attesting embedded systems due to their high computational overhead and resource usage. In this paper, we overcome the limitations of existing CFA schemes for embedded devices by introducing RAGE, a novel, lightweight CFA approach with minimal requirements. RAGE can detect Code Reuse Attacks (CRA), including control- and non-control-data attacks. It efficiently extracts features from one execution trace and leverages Unsupervised Graph Neural Networks (GNNs) to identify deviations from benign executions. The core intuition behind RAGE is to exploit the correspondence between execution trace, execution graph, and execution embeddings to eliminate the unrealistic requirement of having access to a complete CFG. We evaluate RAGE on embedded benchmarks and demonstrate that (i) it detects 40 real-world attacks on embedded software; (ii) Further, we stress our scheme with synthetic return-oriented programming (ROP) and data-oriented programming (DOP) attacks on the real-world embedded software benchmark Embench, achieving 98.03% (ROP) and 91.01% (DOP) F1-Score while maintaining a low False Positive Rate of 3.19%; (iii) Additionally, we evaluate RAGE on OpenSSL, used by millions of devices and achieve 97.49% and 84.42% F1-Score for ROP and DOP attack detection, with an FPR of 5.47%.

cs.CR↗

arXiv2016

Aware: Controlling App Access to I/O Devices on Mobile Platforms

Smartphones' cameras, microphones, and device displays enable users to capture and view memorable moments of their lives. However, adversaries can trick users into authorizing malicious apps that exploit weaknesses in current mobile platforms to misuse such on-board I/O devices to stealthily capture photos, videos, and screen content without the users' consent. Contemporary mobile operating systems fail to prevent such misuse of I/O devices by authorized apps due to lack of binding between users' interactions and accesses to I/O devices performed by these apps. In this paper, we propose Aware, a security framework for authorizing app requests to perform operations using I/O devices, which binds app requests with user intentions to make all uses of certain I/O devices explicit. We evaluate our defense mechanisms through laboratory-based experimentation and a user study, involving 74 human subjects, whose ability to identify undesired operations targeting I/O devices increased significantly. Without Aware, only 18% of the participants were able to identify attacks from tested RAT apps. Aware systematically blocks all the attacks in absence of user consent and supports users in identifying 82% of social-engineering attacks tested to hijack approved requests, including some more sophisticated forms of social engineering not yet present in available RATs. Aware introduces only 4.79% maximum performance overhead over operations targeting I/O devices. Aware shows that a combination of system defenses and user interface can significantly strengthen defenses for controlling the use of on-board I/O devices.

cs.OS↗

arXiv2016

AuDroid: Preventing Attacks on Audio Channels in Mobile Devices

Voice control is a popular way to operate mobile devices, enabling users to communicate requests to their devices. However, adversaries can leverage voice control to trick mobile devices into executing commands to leak secrets or to modify critical information. Contemporary mobile operating systems fail to prevent such attacks because they do not control access to the speaker at all and fail to control when untrusted apps may use the microphone, enabling authorized apps to create exploitable communication channels. In this paper, we propose a security mechanism that tracks the creation of audio communication channels explicitly and controls the information flows over these channels to prevent several types of attacks.We design and implement AuDroid, an extension to the SELinux reference monitor integrated into the Android operating system for enforcing lattice security policies over the dynamically changing use of system audio resources. To enhance flexibility, when information flow errors are detected, the device owner, system apps and services are given the opportunity to resolve information flow errors using known methods, enabling AuDroid to run many configurations safely. We evaluate our approach on 17 widely-used apps that make extensive use of the microphone and speaker, finding that AuDroid prevents six types of attack scenarios on audio channels while permitting all 17 apps to run effectively. AuDroid shows that it is possible to prevent attacks using audio channels without compromising functionality or introducing significant performance overhead.

cs.CR↗