cs.CR · 2026-06-08 · No. 17

Cryptography and Security, 2026-06-08.

3 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    From Privacy to Workflow Integrity: Communication-Graph Metadata in Autonomous Agent Interoperability

    Bijaya Dangol

    cs.CR · cs.AI · cs.MA · cs.NI

    Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such transports protect message content, increasingly with end-to-end encryption. What they leave in the clear is the communication graph: which agent contacts which, when, and how often. In agent systems this graph is more consequential than a privacy framing suggests. Endpoints are often...

    arxiv.org/abs/2606.07150 · PDF

  2. 02

    AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification

    Parthajit Borah, Sakshi Singh, D. K. Bhattacharyya, J. K. Kalita

    cs.CR · cs.LG

    As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware. These...

    arxiv.org/abs/2606.06815 · PDF

  3. 03

    What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media

    Zifan Peng, Yini Huang, Aiwen Lu, Qiming Ye, Peixian Zhang, Jingyi Zheng, Yule Liu, Xuechao Wang, Xinlei He, Jiaheng Wei

    cs.CR · cs.AI · cs.CY

    Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that appear harmless in isolation may jointly expose a user's home, workplace, or routine. However, current research lacks a unified benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy. To...

    arxiv.org/abs/2606.06784 · PDF

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