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Wanda Guo

Publications and source records attributed to Wanda Guo.

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Semantics Over Syntax: Uncovering Pre-Authentication 5G Baseband Vulnerabilities

Modern 5G user equipment (UE) processes Radio Resource Control (RRC) configuration messages during early control-plane exchanges, before authentication and integrity protection are established. Prior work for testing 5G UEs has largely focused on constructing syntactically invalid inputs. In contrast, we show that syntactically valid but semantically inconsistent messages, which violate specification-level field constraints or cross-field dependencies, can drive baseband implementations into invalid states, triggering assertion failures or modem crashes. These findings reveal semantic inconsistencies in pre-authentication signaling as a critical yet underexplored attack surface in 5G UE implementations. To address this gap, we present Constraint-Guided Semantic Testing (ConSeT), a framework that systematically extracts specification-level constraints and leverages them to generate targeted semantic violations for testing 5G UEs. ConSeT decodes RRC messages into structured fields, derives schema-based rules, infers cross-field dependencies using a Large Language Model (LLM) in an evidence-bounded manner, and produces syntactically valid test cases that intentionally violate semantic constraints. We evaluate ConSeT on both commercial and open-source 5G UEs. On commercial smartphones, it uncovers 7 previously unknown vulnerabilities through responsible disclosure, including 3 high-severity CVEs, affecting 64 chipset models and over 542 commercially available smartphone models. On the open-source OAI UE, ConSeT additionally triggers 29 distinct crash sites.

cs.CR

Bridging Both Worlds in Semantics and Time: Domain Knowledge Based Analysis and Correlation of Industrial Process Attacks

Modern industrial control systems (ICS) attacks infect supervisory control and data acquisition (SCADA) hosts to stealthily alter industrial processes, causing damage. To detect attacks with low false alarms, recent work detects attacks in both SCADA and process data. Unfortunately, this led to the same problem - disjointed (false) alerts, due to the semantic and time gap in SCADA and process behavior, i.e., SCADA execution does not map to process dynamics nor evolve at similar time scales. We propose BRIDGE to analyze and correlate SCADA and industrial process attacks using domain knowledge to bridge their unique semantic and time evolution. This enables operators to tie malicious SCADA operations to their adverse process effects, which reduces false alarms and improves attack understanding. BRIDGE (i) identifies process constraints violations in SCADA by measuring actuation dependencies in SCADA process-control, and (ii) detects malicious SCADA effects in processes via a physics-informed neural network that embeds generic knowledge of inertial process dynamics. BRIDGE then dynamically aligns both analysis (i and ii) in a time-window that adjusts their time evolution based on process inertial delays. We applied BRIDGE to 11 diverse real-world industrial processes, and adaptive attacks inspired by past events. BRIDGE correlated 98.3% of attacks with 0.8% false positives (FP), compared to 78.3% detection accuracy and 13.7% FP of recent work.

cs.CR