cs.CR · 2026-06-18 · No. 27

Cryptography and Security, 2026-06-18.

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

01 — The papers

6 entries
  1. 01

    OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing

    Nahum Korda, Gadi Evron

    cs.CR · cs.LG

    Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs. Recent advances in large language models (LLMs) enable semantic reasoning about program behavior, but applying LLMs to repository-scale security analysis introduces challenges related to...

    arxiv.org/abs/2606.19149 · PDF

  2. 02

    Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

    Ousmane Touat, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar

    cs.CR · cs.LG

    Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors. While enforcing confidentiality calls for hiding the exchanged model parameters/gradients (e.g., by using cryptographic techniques), dealing with...

    arxiv.org/abs/2606.19129 · PDF

  3. 03

    Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution

    Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore, Stefano Zanero, Stefano Longari, Michele Carminati

    cs.CR · cs.LG

    The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static, format-specific rules or known attack signatures, which limit their ability to generalize across frameworks and to detect novel exploitation paths. In...

    arxiv.org/abs/2606.19023 · PDF

  4. 04

    TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction

    Moon Ye-Bin, Nam Hyeon-Woo, Baek Seong-Eun, Yejin Yeo, Tae-Hyun Oh

    cs.CR · cs.AI

    Agents are increasingly deployed in document-intensive workflows where sensitive private information is not an edge case but a routine input, e.g., an agent booking a flight needs passport numbers. In such settings, the agent must use private information to complete tasks accurately while never exposing it in its responses, because it cannot verify who is actually at the keyboard. These two obligations are in fundamental tension. A model...

    arxiv.org/abs/2606.18996 · PDF

  5. 05

    Code-Augur: Agentic Vulnerability Detection via Specification Inference

    Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury

    cs.CR · cs.AI · cs.SE

    The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents are uncovering critical vulnerabilities in fundamental software underpinning digital society. Many of these vulnerabilities remained masked for years, surfacing only now with AI agents. Yet the reasoning behind these discoveries remains alarmingly opaque and unvalidated. What...

    arxiv.org/abs/2606.18619 · PDF

  6. 06

    MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba

    Qiqi Liu, Runhan Song, Lei Cui, Heng Zhang, Yuyan Sun, Limin Sun

    cs.CR · cs.AI

    The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats. Existing intrusion detection systems are largely tuned to fabrication-style attacks (DoS, fuzzing, ID spoofing realised by frame injection), in which detection signals such as per-ID inter-arrival statistics are readily...

    arxiv.org/abs/2606.18599 · PDF

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