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We perform a rigorous analysis of the roaming aspect of the PWS, incl. Our PWS barring attack is an effective technique to eliminate legitimate warning messages. We discover that MitM-based attacks have more severe impact than their non-MitM counterparts. Additional to identifying the vulnerabilities, we investigate two PWS spoofing and three PWS suppression attacks, with or without a man-in-the-middle (MitM) attacker. We demonstrate five practical attacks that may impact the security of 5G-based Commercial Mobile Alert System (CMAS) as well as Earthquake and Tsunami Warning System (ETWS) alerts. In this work, we conduct the first comprehensive investigation of PWS security in 5G networks. PWS attacks disseminating fake warnings or concealing precarious events can have a serious impact, causing fraud, panic, physical harm, or unrest to users within an affected area. Warnings can notify users of hazardous events (e.g., floods, earthquakes) and crucial national matters that require immediate attention. The Public Warning System (PWS) is an essential part of cellular networks and a country's civil protection. We found some non-compliance which we later confirmed to be bugs caused by race conditions.

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We also designed a new dynamic analysis method to perform driver code compliance checks against the specification. Benefiting from the accurate emulation, our emulator effectively avoids false positives observed in existing fuzzing work. Using a new edit-distance-based algorithm to calculate trace differences, our evaluation against a large firmware corpus confirmed that our prototype achieves much higher fidelity compared with state-of-the-art solutions. We have implemented our idea for five popular MCU boards spanning three different chip vendors.

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This assists us in the manual correction of the problematic rules. We, therefore, propose incorporating symbolic execution to quickly pinpoint the root cause. The extracted condition-action rules might not be complete or even be wrong. By checking, executing, and chaining them at runtime, we can dynamically synthesize a peripheral model for each firmware execution. Using NLP, we translate peripheral behaviors in human language (documented in chip manuals) into a set of structured condition-action rules. In this work, we propose a new approach that builds peripheral models from the peripheral specification.

This is problematic because the firmware sometimes does not contain enough clues to support the emulation or even contains misleading information (e.g.

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Existing work finds out how to respond to peripheral read operations by analyzing the target firmware. Emulating firmware of microcontrollers is challenging due to the lack of peripheral models.

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