arXiv AI June 30, 2026 By Chen Frydman, Aviram Zilberman, Rubin Krief, Abed Showgan, Andres Murillo, Sekiya Motoyoshi, Asaf Shabtai, Yuval Elovici, Rami Puzis

COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies

Read the original on arXiv AI →

arXiv:2606. 30479v1 Announce Type: cross Abstract: Mitigating an observed adversary in an enterprise network typically takes weeks of expert work: an analyst derives a mitigation tailored to that adversary, validates it without breaking production, and verifies it disrupts the specific attack.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 23

APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks

arXiv:2605.02346v2 Announce Type: replace-cross Abstract: Operational technology (OT) devices run safety-critical physical processes, yet their security testing remains manual and expert-intensive. A...

By Adel ElZemity, Budi Arief, Shujun Li, George Oikonomou, James Pope

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arXiv AI

Aug 18

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

arXiv:2608. 15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands.

By Yiran Gao, Juntao Chen, Tao Li

llms agents fine-tuning

arXiv AI

Jul 3

Distributed Attacks in Persistent-State AI Control

arXiv:2607. 02514v1 Announce Type: new Abstract: As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions.

By Josh Hills, Ida Caspary, Asa Cooper Stickland

llms agents benchmarks

arXiv AI

Jun 26

Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents

arXiv:2606. 26479v1 Announce Type: cross Abstract: Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with a deterministic policy that mediates the agent's actions.

By Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi, Jayaram Kumarapu

llms agents benchmarks

arXiv AI

Jun 30

MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems

arXiv:2606. 30602v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows.

By Kunyang Li, Kyle Domico, Jonathan Gregory, Patrick McDaniel

llms agents benchmarks

arXiv AI

4d ago

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering

arXiv:2609.36570v1 Announce Type: cross Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...

By Mark Russinovich

llms agents robotics fine-tuning benchmarks

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APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

Distributed Attacks in Persistent-State AI Control

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