Mad Monk: Arbitrary Criticality Escalation in Mixed Criticality Real-Time Systems

In safety critical computing, real-time and security concerns are often considered separately, though the behavior of a scheduling model itself may be an attack surface which can be exploited by an attacker to reduce system performance. In this work, we explore how the semantics of mode changes in mixed-criticality systems could be used as one such attack vector. This attack, dubbed Mad Monk, uses a mixed criticality scheduler's mode switches against itself by allowing a task of a lower criticality to interfere with tasks of a higher criticality, thereby forcing a disruptive mode switch which could possibly reduce service to some tasks. We describe this attack in detail, along with a case study demonstrating its risk. Furthermore, extensive simulations of this attack demonstrate its potential effectiveness based on a variety of timing and system factors.

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Publication Details

Published
2025-05-26
DOI
https://doi.org/10.1109/isorc65339.2025.00020
Primary Topic
Real-Time Systems Scheduling
Type
article
Field-Weighted Citation Impact
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article

Mad Monk: Arbitrary Criticality Escalation in Mixed Criticality Real-Time Systems

Tanmaya Mishra, Ryan Gerdes, Thidapat Chantem, Nathan Fisher et al.
Real-Time Systems Scheduling
article

Mad Monk: Arbitrary Criticality Escalation in Mixed Criticality Real-Time Systems

Tanmaya Mishra, Ryan Gerdes, Thidapat Chantem, Nathan Fisher, Mitch J. Duncan, Ao Li, Ning Zhang
article en

Abstract

In safety critical computing, real-time and security concerns are often considered separately, though the behavior of a scheduling model itself may be an attack surface which can be exploited by an attacker to reduce system performance. In this work, we explore how the semantics of mode changes in mixed-criticality systems could be used as one such attack vector. This attack, dubbed Mad Monk, uses a mixed criticality scheduler's mode switches against itself by allowing a task of a lower criticality to interfere with tasks of a higher criticality, thereby forcing a disruptive mode switch which could possibly reduce service to some tasks. We describe this attack in detail, along with a case study demonstrating its risk. Furthermore, extensive simulations of this attack demonstrate its potential effectiveness based on a variety of timing and system factors.

Wayne State University (US), Washington University in St. Louis (US), Virginia Tech (US)
Openalex Percentile: Top 71%
Real-Time Systems Scheduling
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