This paper proposes an automated framework that extracts reachable attack chains by modeling each attack step as an attack unit of preconditions, an attack behavior, and postconditions, and produces attack units that are more complete and consistent than those generated by end-to-end LLM baselines.
Cyber Threat Intelligence (CTI) enables Security Operations Centers (SOCs) to understand adversary behavior,
prioritize risks, and respond to cyber threats. However, cur-rent CTI workflows still depend heavily on manual analysis
of unstructured threat reports, vulnerability advisories, open-source intelligence, social media posts, and structured feeds. This
creates operational latency, inconsistent extraction quality, weak provenance, and limited scalability. This paper presents Auto
Threat AI, an agentic and explainable framework for automated CTI extraction, correlation, scoring, and analyst-governed SOC
operationalization. The proposed framework integrates determin-istic indicator extraction, Natural Language Processing (NLP),
schema-guided Large Language Model (LLM) agents, graph-aware threat correlation, bounded risk scoring, evidence-first
explainability, and Human-in-the-Loop (HITL) governance. The system ingests heterogeneous CTI sources, extracts entities and
relations such as IOCs, CVEs, malware, campaigns, threat actors, tools, and techniques, constructs a threat knowledge graph,
gen-erates campaign candidates, and presents risk-ranked intelligence through a SOC dashboard. Experimental evaluation on
safe demonstration CTI data shows that the implemented prototype ingested 6 sources, extracted 36 entities, generated 33
relations, detected 11 threat events, identified 5 campaign candidates, and routed 8 items for HITL review. The results
demonstrate that Auto Threat AI can reduce manual CTI processing effort while improving traceability, explainability, and
analyst trust.
Methari Keeravani· International Journal for Re...· 0 citations
Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand. While classical AI planning using the Planning Domain Definition Language (PDDL) offers a formal method to automate this process, it relies on the accurate translation of techniques into symbolic predicates. Current state-of-the-art systems like AURORA employ a nine-category Attack Action Linking Model (AALM), but the necessity of this specific granularity remains unvalidated. This work investigates whether AURORA's nine-category taxonomy provides representational distinctions beyond those captured by a reduced, empirically derived scheme. Utilizing a pipeline where a Large Language Model (LLM) performs translation and the Fast Downward engine performs deterministic reasoning, the study compares the full nine-category AALM against a reduced five-category scheme derived empirically from Atomic Red Team (ART) execution evidence. Because the nine-category domain is constructed as a relabeling of the five-category domain, plan validity and cost are held identical between schemes by design; the substantive test of granularity's effect lies instead in the resulting predicate category resolution. There, a controlled A/B test isolates a case where a coarser scheme's plan passes every validity check while remaining operationally wrong: holding administrator privilege and being able to exercise it over a network logon prove to be causally distinct system states. Results from a sixteen-technique corpus show 81.3% identical plan outcomes across both schemes by construction, with a genuine predicate category resolution gain confined to a single technique out of sixteen. The findings suggest that higher granularity primarily enhances the internal structural resolution of a plan's justification rather than the viability of the generated attack chain itself.
: Mapping unstructured Cyber Threat Intelligence (CTI) reports to the MITRE ATT&CK framework is critical for proactive defence but remains a manual, time-consuming process. Existing automated approaches either rely on black-box language models that lack interpretable reasoning or brittle, rule-based knowledge graphs that fail to scale. To bridge this gap, we propose Graph2TTP, a novel neural-symbolic framework for automated, paragraph-level Tactic, Technique and Procedure (TTP) identification. Graph2TTP leverages the zero-shot comprehension of Large Language Models (LLMs) to automatically extract entities and relations from extensive CTI narratives, constructing rich, localized Cyber Security Knowledge Graphs (CSKGs). We encode these symbolic structures into dense embeddings and process them via a multi-label Edge-featured Graph Attention Network (EGAT). This hybrid architecture delivers both the high-fidelity accuracy of neural networks and the transparent, verifiable reasoning paths required by security analysts. To facilitate rigorous evaluation, we curate and release APTCTI, a real-world dataset comprising 690 Advanced Persistent Threat reports spanning over 18,087 paragraphs. Extensive evaluations across multiple datasets demonstrate that Graph2TTP outperforms state-of-the-art neural baselines (e.g., CySecBERT) by approximately 20% in F1 score, establishing a robust new standard for accurate and interpretable threat intelligence analysis.
Patrick Zounon, Yufei Han, Michel Hurfin et al.· Proceedings of the 23rd Inte...· 0 citations
Smart contract vulnerabilities pose severe financial risks, yet existing security tools largely stop at vulnerability detection, offering limited support for explaining whether reported flaws are exploitable, how attacks unfold, and what concrete damage they cause. To bridge this gap, we propose KASS (Knowledge-Augmented Attack Synthesis and Simulation), a multi-agent framework for executable smart contract exploit verification. KASS decomposes automated exploit generation into planning, generation, and testing stages, and integrates three complementary mechanisms: retrieval-augmented planning over real-world audit knowledge, formal generation and validation constraints that bind attack plans to executable proof-of-concept tests, and a hierarchical dual-loop refinement process that repairs code-level errors while triggering strategy-level replanning when attack assumptions fail. We evaluate KASS on 104 SmartBugs-Curated contracts across four vulnerability categories. Experimental results show that KASS successfully generates executable exploits for 94.23% of tested contracts; this rate is higher than previously reported results for REX and AdvSCanner on comparable SmartBugs-Curated subsets, and higher than our reproduced Claude Code baseline under the same evaluation protocol. On 11 real-world CVE-tagged contracts, KASS successfully validates 9 cases. Beyond exploit generation, KASS produces structured attack plans that document exploitation flows, quantify potential asset losses, and serve as semantic false positive filters for static analysis tools.
Xianhao Zhang, Jing Sun, Zijian Zhang et al.· 0 citations
AUTOSIGMA, an automated solution for transforming unstructured CTI reports into relevant Sigma rules that enables accurate, context-aware, and relevant rule generation, outperforms alternative solutions and LLM models in rule validity, rule relevancy, MITRE ATT&CK technique coverage, and robustness to input quality.
Sepehr Ghaffarzadegan, Boubakr Nour, M. Pourzandi et al.· 0 citations
Large language models (LLMs) are increasingly applied to vulnerability detection due to their strong code comprehension capabilities, but most existing approaches rely on isolated functions or context extracted by fixed program-analysis rules. These methods cannot adaptively explore repository-level dependencies to gather sufficient context when vulnerabilities span multiple functions or files, compromising detection reliability. We present DREA (Decoupled Reasoning and Exploration Agents), a hypothesis-driven framework for repository-level vulnerability detection. DREA decouples reasoning from exploration through two collaborating agents: a planning agent backed by an advanced LLM that forms vulnerability hypotheses and directs the investigation, and an explorer agent powered by a lightweight model that retrieves repository-level context on demand. Goal-directed context acquisition is the primary source of detection improvement in this design, while offloading token-heavy exploration to the local model keeps inference economically tractable. To support evaluation, we construct RepoPairBench, a repository-grounded benchmark of validated Python vulnerability-fix pairs from real-world projects. Beyond binary detection accuracy, we introduce a reasoning correctness evaluation to assess whether a model's rationale matches the documented vulnerability mechanism. Across three LLMs, DREA improves Pair-Correctness from 19-26% to 30-42% while offloading over 93% of tokens to the explorer, reducing estimated billable API cost by a factor of 16-48. Reasoning correctness analysis further reveals that 26-55% of true positives, for both DREA and the function-only baseline, are correct predictions supported by flawed rationales, identifying security reasoning quality as a shared bottleneck for current LLMs.