~ similar to 2605.01721v1· 20 results
The paper introduces Platum, a novel framework that synthesizes verified, low-latency runtime monitors for MAVLink protocols, enabling robust enforcement of contextual message validity on resource-con…
Xiang Liu, Sa Song, Zhaowei Zhang, Huiying Lan +5 more
The paper introduces Agora, a domain-aware multi-agent framework that successfully detects deep, previously unknown logic bugs in complex consensus protocols, outperforming existing LLM-based analysis…
Jiaying Meng, Xuewei Feng, Qi Li, Min Liu +1 more
AFL-ICP is a novel specification-driven fuzzing framework that significantly enhances the security testing of industrial control protocols by detecting subtle semantic and logic bugs missed by traditi…
Oliver Jacobsen, Tobias Kirsch, Haya Schulmann, Niklas Vogel +1 more
This paper analyzes RPKI specifications, demonstrating that vague or conflicting requirements in dozens of RFCs cause systemic vulnerabilities in real-world implementations, leading to 61 undocumented…
The paper introduces CAT, a novel coverage-guided fuzzing tool that overcomes the limitations of existing fuzzers for complex, multi-object cryptographic repositories like RPKI, leading to the discove…
COBALT-TLA introduces a neuro-symbolic verification loop that successfully and autonomously discovers novel cross-chain bridge vulnerabilities by integrating an LLM with the TLA+ model checker.
The paper introduces a systematic, executable taxonomy of security properties to bridge the gap between theoretical security definitions and their practical implementation in formal verification tools…
Qiqing Huang, Xingyu Wang, Wanda Guo, Guofei Gu +1 more
The paper introduces Constraint-Guided Semantic Testing (ConSeT), a novel framework that systematically finds critical, pre-authentication vulnerabilities in 5G User Equipment (UE) by exploiting seman…
The paper introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…
The paper introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…
AutoSOUP is a system that automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs, leveraging a hybrid LLM-based architecture to overcome manual workflow limitat…
The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…
Matthias Cosler, Cas Cremers, Bernd Finkbeiner, Mohamed Ghanem +1 more
The paper introduces a reinforcement learning framework, inspired by AlphaZero, to automate and improve the proof search process within the Tamarin protocol analysis tool, resulting in shorter and mor…
Yuxiang Yang, Ao Wang, Xuewei Feng, Qi Li +1 more
This paper systematically identifies and demonstrates multiple session manipulation attacks against VPN connection tracking frameworks, revealing widespread vulnerabilities in popular VPN services.
Yunze Zhao, Yibo Zhao, Yuchen Zhang, Zaoxing Liu +1 more
The paper introduces GRIEF, a greybox fuzzer that discovers critical, concurrency-related vulnerabilities in LLM serving systems by treating timed multi-request traces as inputs, finding issues like c…
The paper introduces a novel pipeline integrating formal verification and process mining to systematically identify and analyze root causes of security property invalidations in complex automotive net…
Yiheng Huang, Zhijia Zhao, Bihuan Chen, Susheng Wu +4 more
This paper introduces a component-centric framework and a novel detector, Connor, to understand and detect sophisticated, multi-component attacks targeting the Model Context Protocol (MCP) servers.
The paper proposes a bottom-up, system-oriented approach to formally verify authorization algorithms for large-scale, Byzantine fault-tolerant local-first systems, using Rust and the Verus framework.
The paper introduces a lightweight, sampling-based cryptographic protocol for verifiable AI inference that drastically reduces proving overhead from minutes to milliseconds by leveraging statistical p…
The paper introduces a graded symbolic verification method that models cumulative side-channel leakage, demonstrating that protocols safe under traditional binary attacker models can fail when continu…