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Model-serving systems have become increasingly popular, especially in real-time web applications. In such systems, users send queries to the server and specify the desired performance metrics (e.g., desired accuracy, latency). The server maintains a set of models (model zoo) in the back-end and serves the queries based on the specified metrics. This paper examines the security, specifically robustness against model extraction attacks, of such systems. Existing black-box attacks assume a single model can be repeatedly selected for serving inference requests. Modern inference serving systems break this assumption. Thus, they cannot be directly applied to extract a victim model, as models are hidden behind a layer of abstraction exposed by the serving system. An attacker can no longer identify which model she is interacting with. To this end, we first propose a query-efficient fingerprinting algorithm to enable the attacker to trigger any desired model consistently. We show that by using our fingerprinting algorithm, model extraction can have fidelity and accuracy scores within 1% of the scores obtained when attacking a single, explicitly specified model, as well as up to 14.6% gain in accuracy and up to 7.7% gain in fidelity compared to the naive attack. Second, we counter the proposed attack with a noise-based defense mechanism that thwarts fingerprinting by adding noise to the specified performance metrics. The proposed defense strategy reduces the attack's accuracy and fidelity by up to 9.8% and 4.8%, respectively (on medium-sized model extraction). Third, we show that the proposed defense induces a fundamental trade-off between the level of protection and system goodput, achieving configurable and significant victim model extraction protection while maintaining acceptable goodput (>80%). We implement the proposed defense in a real system with plans to open source. 

Scheduler side-channels can leak critical information in real-time systems, thus posing serious threats to many safety-critical applications. The main culprit is the inherent determinism in the runtime timing behavior of such systems, e.g., the (expected) periodic behavior of critical tasks. In this paper, we introduce the notion of "schedule indistinguishability/", inspired by work in differential privacy, that introduces diversity into the schedules of such systems while offering analyzable security guarantees. We achieve this by adding a sufficiently large (controlled) noise to the task schedules in order to break their deterministic execution patterns. An "epsilon-Scheduler" then implements schedule indistinguishability in real-time Linux. We evaluate our system using two real applications: (a) an autonomous rover running on a real hardware platform (Raspberry Pi) and (b) a video streaming application that sends data across large geographic distances. Our results show that the epsilon-Scheduler offers better protection against scheduler side-channel attacks in real-time systems while still maintaining good performance and quality-of-service(QoS) requirements.