Wagner Comin Sonaglio

arXiv2026

FlyBlind: Cross-Slice Timeliness Attacks on UAV Situational Awareness over 5G

Beyond Visual Line of Sight (BVLOS) Uncrewed Aerial Systems (UAS) operating over 5G Standalone (SA) networks use a shared User Plane for both command-and-control (C2) data and video feedback. Operators assess link quality through latency and availability, relying on soft isolation between network slices. However, the risk that an authorized co-tenant could make the Ground Control Station (GCS) state outdated without disrupting the connection remains underexplored. This work introduces FlyBlind, a timeliness attack in which an authorized co-tenant on a neighboring slice maintains legitimate uplink demand, causing state aging at the GCS without a rogue gNB or direct interference with C2 traffic. Our key insight is that, under soft isolation, sharing idle resources turns authorized competition for grants into state aging that conventional link monitors fail to detect. We formalize this effect, termed Silent State Staleness, as a falsifiable false-healthy predicate. On a dedicated testbed, telemetry age at the GCS saturates at approximately 12 seconds, with discrepancies of tens of meters between the GCS position estimate and ground truth, while the one-way delay (OWD) p99 remains in the tens of milliseconds, availability exceeds 99.9%, and the vehicle keeps operating in GUIDED mode without triggering failsafe mechanisms. These findings indicate that, in deployments with asymmetric uplink enforcement, verifying state freshness at the destination is essential rather than relying solely on link health.

cs.CR↗

arXiv2026

A Proposal for an Agentic AI Architecture to Support Multi-Domain Decision-Making in the Brazilian Armed Forces

The growing complexity of multi-domain operational environments (land, aerospace, naval, cyber, and electromagnetic spectrum) has increased the volume and velocity of data reaching command-and-control (C2) centers, straining the observe-orient-decide-act (OODA) decision cycle. Artificial Intelligence (AI) systems currently employed in defense are, in general, reactive and isolated tools that still rely heavily on human operators to integrate information, assess scenarios, and formulate courses of action. This paper proposes a conceptual Agentic AI architecture for AI systems that can plan, access data sources, execute tools, and act autonomously and audibly, aimed at supporting decision-making across the three Brazilian Armed Forces (Navy, Army, and Air Force). Four application fronts are discussed (decision support, situational analysis, feasibility studies, and countermeasure suggestion), as well as the data and sensor access requirements and the security and permission safeguards necessary for responsible employment across administrative, strategic, operational, and tactical contexts.

cs.AI↗

arXiv2026

ARENA: An Architecture for Measuring the Transferability of Autonomous Cyber Defense

Operational evidence is not automatically scientific evidence. The most realistic Security Operations Center (SOC) data is production telemetry, yet it remains scientifically inaccessible because raw logs cannot be released; as a result, research relies on synthetic or dated datasets. We treat the boundary between private production telemetry and reusable research artifacts as the design object: a methodology that extracts, anonymizes, structures, and validates Security Information and Event Management (SIEM) data from a production financial SOC while preserving task-relevant investigative structure within a declared privacy boundary. Two consumers stress the same artifact. As training material, it fails loudly: 37 MITRE ATT&CK-mapped HIKARI challenges work only when anonymization preserves temporal order and entity consistency. As a measurement substrate, it fails quietly: across 200 SOCpilot incidents, a deterministic verifier detects non-compliant Large Language Model (LLM) actions that are absent from the human baseline. The result is a measurable privacy-utility boundary rather than a formal anonymity claim.

cs.CR↗

arXiv2026

From Production SIEM to Reusable Cybersecurity Artifacts

Operational evidence is not automatically scientific evidence. The most realistic Security Operations Center (SOC) data is production telemetry, yet it remains scientifically inaccessible because raw logs cannot be released; as a result, research relies on synthetic or dated datasets. We treat the boundary between private production telemetry and reusable research artifacts as the design object: a methodology that extracts, anonymizes, structures, and validates Security Information and Event Management (SIEM) data from a production financial SOC while preserving task-relevant investigative structure within a declared privacy boundary. Two consumers stress the same artifact. As training material, it fails loudly: 37 MITRE ATT&CK-mapped HIKARI challenges work only when anonymization preserves temporal order and entity consistency. As a measurement substrate, it fails quietly: across 200 SOCpilot incidents, a deterministic verifier detects non-compliant Large Language Model (LLM) actions that are absent from the human baseline. The result is a measurable privacy-utility boundary rather than a formal anonymity claim.

cs.CR↗

arXiv2026

When Connectivity Is Not Enough: Cross-Layer Attacks on UAV C2 over 5G

Beyond Visual Line of Sight (BVLOS) unmanned aerial vehicle (UAV) operations increasingly use 5G standalone (SA) networks for command and control (C2) between the UAV and the ground control station (GCS). The 3rd Generation Partnership Project (3GPP) has specified mechanisms for authentication and authorization of unmanned aircraft systems (UAS) in this architectural setting. As a result, operators may treat registration state, Protocol Data Unit (PDU) session status, and IP reachability as evidence that the C2 path is available. In practice, however, these connectivity indicators alone do not guarantee that closed-loop control remains operationally safe. Attacks can degrade UAS C2 when timeliness degrades under shared User Plane contention, mobility continuity fails during Control Plane instability, or command integrity is violated at a trusted next-generation Node B (gNodeB). Such failures undermine connectivity as the central security indicator for UAV operations. In this paper, we demonstrate these issues using three distinct threat models on a reproducible Open5GS and UERANSIM testbed that carries Micro Air Vehicle Link (MAVLink) over the 5G User Plane, and we use a commercial Nokia core to ground deployment assumptions. We address timeliness, availability, and integrity through experiments in which attack success is defined as forcing an unsafe closed-loop state without a clean disconnect. We observe stale telemetry and heavy-tailed delay under co-tenant User Plane contention, failsafe after handover under Control Plane instability, and navigation hijacking after command rewriting at a compromised gNodeB. We further discuss why each threat model arises and evaluate mitigations for these cross-layer failures. Across the study, we disclosed five robustness issues: three CVEs have already been assigned, and two additional CVE requests are pending.

cs.CR↗

arXiv2026

A Systematic Security Testing Approach for InterUSS-based environments

Unmanned Traffic Management (UTM) federated ecosystems, such as InterUSS, enable secure coordination among UAS Service Suppliers (USSs). However, they bring up some security challenges at the infrastructure level that haven't been fully explored. This paper presents a security testing approach for InterUSS-based environments from the maintainer's perspective. By deploying and analyzing a working InterUSS infrastructure, we pinpoint key components and develop specific security tests aligned with established standards and protocols, such as mTLS and OAuth 2.0. We compiled these tests into a Testing Guide that aids both component validation and interaction analysis across InterUSS-based ecosystems, filling a gap in current research.

cs.CR↗