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Cyber threat intelligence (CTI) reports now serve as essential resources for capturing adversary tactics, techniques, and procedures observed in modern attack campaigns. While traditional CTI platforms reduce this intelligence to isolated indicators through fixed schemas such as STIX, ontology-based representations preserve the semantic relationships needed for structured threat analysis. However, existing approaches for ontology-aligned CTI extraction face three challenges: (i) schema-specific pipelines that require manual reconfiguration whenever the schema changes, (ii) prompt-based schema inclusion that fails to scale on large ontologies such as UCO, and (iii) reliance on enterprise LLM APIs that conflicts with privacy constraints when integrating sensitive internal incident data. In this paper, we present ANCHOR, a schema-agnostic CTI knowledge graph construction system that bridges LLMs and formal ontology schemas. At the core of ANCHOR is hybrid ontology discovery, a search-and-navigate mechanism that dynamically explores large-scale ontology schemas, combined with SHACL-based validation to enforce schema-compliant type assignments. Experimental results on the UCO, STIX, and MALOnt schemas show that ANCHOR outperforms existing baselines in ontology typing and schema compliance. In addition, ANCHOR with a local LLM closely matches enterprise LLM typing performance, enabling privacy-preserving CTI analysis with high fidelity.

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, all with assigned CVEs.

LLM agents combine information from multiple principals while acting externally. We formalise privilege escalation (PE) as the system-level security objective: an action may proceed only if every principal that influenced it would allow it. Model-level defences remain probabilistic, so enforcement must sit outside model judgement. We present Conflux, a principal-context defence. Its kernel, ITES, derives authority from provenance and the organisation's existing access-control system and intersects the permissions of every influencing principal. We prove a conditional authority-confinement theorem: under complete mediation, no-laundering provenance, a known non-empty decision context, fresh permission checks, and action binding, the kernel admits no action that escalates privilege with respect to any recorded influencing principal. The theorem covers the represented value-influence fragment; control influence from tool, branch, recipient, or plan-step selection remains a refinement obligation. Conflux extends the kernel with independent policy ports, argument-aware authorisation, disclosure, consent, delegation, restriction, and exposure planning. We describe two system- level evaluators. SLED is a black-box evaluator: it exhaustively explores a declared finite model of a defence up to a bound. SLED-V is a white-box evaluator: it checks a serialisable intermediate representation against security and provenance properties. The evidence is bounded and partial. The appendix reports retained AgentDojo, SLED, and SLED-V results, including the limits of those runs. No result in this paper is an unbounded deployment guarantee.