Qi Wang (Tsinghua University), Jianjun Chen (Tsinghua University), Jingcheng Yang (Tsinghua University), Jiahe Zhang (Tsinghua University), Yaru Yang (Tsinghua University), Haixin Duan (Tsinghua University)

Session Initiation Protocol (SIP) is a cornerstone of modern real-time communication systems, powering voice calls, text messaging, and multimedia sessions across services such as VoIP, VoLTE, and RCS. While SIP provides mechanisms for authentication and identity assertion, its inherent flexibility poses the risk of semantic ambiguity among implementations that can be exploited by attackers.

In this paper, we present SIPCHIMERA, a novel black-box fuzzing framework designed to systematically identify ambiguity-based identity spoofing vulnerabilities across SIP implementations. We evaluated SIPCHIMERA against six widely used opensource SIP servers—including Asterisk and OpenSIPS—and nine popular user agents, uncovering that attackers could spoof their identity via manipulating identity headers and circumvent authentication. We demonstrate the real-world impact of these vulnerabilities by evaluating five VoIP devices, seven commercial SIP deployments, and three carrier-grade RCS-based SMS platforms. Our experiments show that attackers can exploit these vulnerabilities to perform caller ID spoofing in VoIP calls and send spoofed SMS messages over RCS, impersonating arbitrary users or services. We have responsibly disclosed our findings to affected vendors and received positive acknowledgments. We finally propose remedies to mitigate those issues.

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Q-MLLM: Vector Quantization for Robust Multimodal Large Language Model...

Wei Zhao (Singapore Management University), Zhe Li (Singapore Management University), Yige Li (Singapore Management University), Jun Sun (Singapore Management University)

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Proactive Hardening of LLM Defenses with HASTE

Henry Chen (Palo Alto Networks), Victor Aranda (Palo Alto Networks), Samarth Keshari (Palo Alto Networks), Ryan Heartfield (Palo Alto Networks), Nicole Nichols (Palo Alto Networks)

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Risk Assessment for ML-Based Applications in Satellite Systems

Simon Shigol (Ben Gurion University of the Negev), Roy Peled (Ben Gurion University of the Negev), Avishag Shapira (Ben Gurion University of the Negev), Yuval Elovici (Ben Gurion University of the Negev), Asaf Shabtai (Ben Gurion University of the Negev)

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