State-of-the-art (SOTA) weight-shared SuperNets dynamically activate subnetworks at runtime, enabling robust adaptive inference under varying deployment conditions. However, we find that adversaries can take advantage of the unique training and inference paradigms of SuperNets to selectively implant backdoors that activate only within specific subnetworks, remaining dormant across billions of other subnetworks. We present VillainNet (VNET), a novel poisoning methodology that restricts backdoor activation to attacker-chosen subnetworks, tailored either to specific operational scenarios (e.g., specific vehicle speeds or weather conditions) or to specific subnetwork configurations. VNET's core innovation is a novel, distance-aware optimization process that leverages architectural and computational similarity metrics between subnetworks to ensure that backdoor activation does not occur across non-target subnetworks. This forces defenders to confront a dramatically expanded search space for backdoor detection. We show that across two SOTA SuperNets, trained on the CIFAR10 and GTSRB datasets, VNET can achieve attack success rates comparable to traditional poisoning approaches (approximately 99%), while significantly lowering the chances of attack detection, thereby stealthily hiding the attack. Consequently, defenders face increased computational burdens, requiring on average 66 (and up to 250 for highly targeted attacks) sampled subnetworks to detect the attack, implying a roughly 66-fold increase in compute cost required to test the SuperNet for backdoors.

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.