arXiv AI

SoK: Timestamps Are Not a Time Model: Rethinking Temporal Reasoning in Host-Based Intrusion Detection

arXiv Machine Learning
Aug 4

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

arXiv:2608. 01454v1 Announce Type: cross Abstract: Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols.

By Lorenzo Guerra, Thomas Chapuis, Guillaume Duc, Pavlo Mozharovskyi, Van-Tam Nguyen
arXiv AI
Aug 25

HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systems

arXiv:2608.22512v1 Announce Type: new Abstract: Autonomous multi-agent systems nowadays act in finance, software supply chains, and security operations. Already, the first largely AI-orchestrated int...

By Christos Sardianos, Iliana Pla, Vasilis Efthymiou, Iraklis Varlamis, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos
arXiv Machine Learning
Sep 10

Counter-Swarm Doctrine: Containing Coordinated Agent Intrusions

The paper discusses how agents can exploit shared infrastructure for coordinated intrusion, citing the Hugging Face incident and a public‑wiki investigation as examples. It proposes that defense should focus on revisable coordination episodes—linking observed data transfers, task authority, and response history—to detect coordinated actions before they are fully formed. The authors outline a prospective episode discovery framework, define unsanctioned coordination, and suggest an evaluation comparing isolated actions, rolling windows, known groups, and prospectively discovered episodes, measuring harmful outcomes and recurrence after channel closure.

By Gregory N Frank
arXiv AI
Jul 8

StepShield: When, Not Whether to Intervene on Rogue Agents

arXiv:2601. 22136v2 Announce Type: replace-cross Abstract: Agent safety benchmarks measure whether a monitor detects harm, not when.

By Gloria Felicia (University of Virginia), Zitha Sasindran (Indian Institute of Science Bangalore), Jinfeng He (Cornell University), Michael Eniolade (University of the Cumberlands), Hemant Kumar (University of Arizona), Milan Hussain Angati (California State University Northridge)
arXiv AI
Oct 23

Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642

The paper introduces an open generator for telecom alarm storms that produces exact root causes and propagation paths for every alarm on real backbone topologies, with each emitted field tied to standardized terms from NORIA‑O, RFC 8632, RFC 8345, or TMF642. Unlike previous benchmarks, the generator does not rely on confidential operator logs and includes fixed configurations, splits, a scorer, and nine baselines. A simple rule—ranking the device that raised the earliest critical alarm—outperforms more complex methods on both synthetic and real operator data, highlighting a key property of alarm streams.

By Madhulatha Mandarapu, Sandeep Kunkunuru
arXiv AI
Jul 28

TRACE-CTI: Auditable Post-Extraction Governance of TTP Claims with Knowledge Graphs

arXiv:2607. 24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fallible and are often stored without the evidence, provenance, and validation history needed to decide whether an individual mapping should be trusted.

By Federico Valletta, Giacomo Longo, Enrico Russo, Alessio Merlo