cs.CR · 2026-06-17 · No. 26

Cryptography and Security, 2026-06-17.

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

01 — The papers

7 entries
  1. 01

    Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

    Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian, Xenofon Koutsoukos

    cs.CR · cs.AI · cs.LG · eess.SY

    With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL). These agents employ neurosymbolic approaches such as behavior trees with learning-enabled components (LECs) to learn, reason, adapt, and implement security rules while maintaining critical operations. However, these autonomous networks are partially observable systems,...

    arxiv.org/abs/2606.18223 · PDF

  2. 02

    Gatling: Rapid-Fire Consensus from Parallel Composition

    Giulia Scaffino, Max Resnick, Joachim Neu

    cs.CR · cs.DC

    Consensus protocols form the core of blockchains and other replicated state machines, ensuring that all correct nodes process the same totally ordered log of input transactions. In fault-free executions, performance is driven by the good-case transaction latency -- the time between a transaction becoming known to all nodes and its confirmation by the consensus protocol -- which depends on both how frequently proposals are made and, once made,...

    arxiv.org/abs/2606.18220 · PDF

  3. 03

    A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models

    Nicola Franco

    cs.CR · cs.AI · cs.CL

    We evaluate the adversarial robustness of two frontier large language models (LLMs) developed by Anthropic, Fable 5 and Opus 4.8, against four families of automated jailbreak attack across 7 826 harmful intents spanning a ten-category harm taxonomy. Using the HackAgent red-teaming framework, hundreds of thousands of adversarial attempts were generated and every apparent success was independently re-adjudicated by a panel of three judge models...

    arxiv.org/abs/2606.18193 · PDF

  4. 04

    Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

    Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi, Md Erfan, Md Rayhanur Rahman

    cs.CR · cs.LG

    Multi-stage cyberattacks span system, network, and browser logs. Detecting them requires correlating events across all three sources. Machine learning methods can learn these cross-source patterns, but they need labeled multi-source data. Existing public datasets fall short. Network-only datasets such as CICIDS and UNSW-NB15 miss host and browser activity. Host-focused datasets such as LMDG and CICAPT-IIoT lack browser telemetry. ATLAS...

    arxiv.org/abs/2606.18190 · PDF

  5. 05

    Evaluating Open-Source LLMs for Multi-Label ATT&CK Technique Classification on CTI Reports

    Ahmed Ryan, Saad Sakib Noor, Md Erfan, Shaswata Mitra, Sudip Mittal, Md Rayhanur Rahman

    cs.CR · cs.LG

    Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort. Pre-Large Language Model (LLM) automation sped up this process, but could not resolve the complex language and multi-step attack patterns found in unstructured CTI reports. LLMs addressed previous limitations by using contextual reasoning to...

    arxiv.org/abs/2606.18166 · PDF

  6. 06

    Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping

    Mohammadreza Rashidi

    cs.CR · cs.AI · cs.CL · cs.LG

    Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace {x} expression HTML-escapes the interpolated value and is documented as the safe default; its triple-brace {x} expression inserts the value raw. We show that this choice silently governs an application's exposure to structural role injection,...

    arxiv.org/abs/2606.18120 · PDF

  7. 07

    An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts

    Joseph Walusimbi, Joshua Benjamin Ssentongo

    cs.CR · cs.AI · cs.CE · cs.ET

    Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements. Static rule engines that reliably catch brute-force and high-velocity events are structurally blind to business-email-compromise (BEC) payment redirection, session...

    arxiv.org/abs/2606.17555 · PDF

This edition is part of The Daily Abstract — cs.CR archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.