CTIFoundry is an agent‑native corpus scaffold designed to improve cyber threat intelligence (CTI) investigations by LLM agents. It transforms traditional CTI data—such as CVE, CWE, CAPEC, and ATT&CK—into a deterministic ontology graph with typed, traversable edges, a span‑grounded report layer that resolves entity aliases and provenance, and hybrid dense‑plus‑lexical retrieval surfaces. When integrated with a standard open‑source agent harness, CTIFoundry boosts overall F1 scores by 0.19 to 0.28 on the CTIConnect benchmark, achieving higher accuracy with fewer tool calls compared to agents using flat, retrieval‑augmented corpora.
arXiv:2510. 11974v2 Announce Type: replace-cross Abstract: Cyber Threat Intelligence (CTI) is foundational to modern cybersecurity, enabling organizations to proactively defend against evolving threats.
arXiv:2607. 05001v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning.
arXiv:2607. 03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intelligence, cybersecurity, and cyber investigation.
arXiv:2510. 14113v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets.
arXiv:2606. 18166v1 Announce Type: cross Abstract: 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.
MITRE‑SAGE is a multi‑agent retrieval‑augmented generation framework that combines semantic and structural cybersecurity knowledge to enhance large language model question‑answering. It decomposes tasks into query interpretation, evidence retrieval, and answer synthesis, supporting vulnerability assessment, threat profiling, and relationship extraction. The authors also introduce MITRE‑QA, a benchmark of 3,000 question‑answer pairs, and show that MITRE‑SAGE outperforms standalone LLMs and conventional RAG methods, with a lightweight configuration achieving top performance on most tasks.
arXiv:2608. 07808v1 Announce Type: cross Abstract: Four years after prompt injection was first identified in 2022, attacks are still predominantly documented as verbatim strings rather than structured exploits, despite advancing agent capabilities and threat actors embedding injections to subvert AI-assisted security analysis.
arXiv:2607. 23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time.
arXiv:2606. 03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation.
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.
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.
arXiv:2608. 16921v1 Announce Type: cross Abstract: Effective cybersecurity operations require timely and accurate analysis of large-scale heterogeneous security information; however, analysts increasingly struggle with information overload, alert fatigue, and time-constrained decision-making.