arXiv:2605.25836v1 Announce Type: cross Abstract: Extracting MITRE ATT&CK techniques from cyber threat intelligence (CTI) reports is an open-set, multi-label problem requiring both high recall (not missing techniques) and high precision (not hallucinating unsupported ones). Existing methods--rule-based, supervised, and LLM-based--struggle to achieve both: rule-based and supervised approaches lack generalizability across diverse attack descriptions, while LLM-based approaches that couple candidate generation and validation within a single inference step suffer from limited recall and precision
The increasing sophistication of cyber threats and the limitations of current AI/LLM approaches are driving the need for more robust, evidence-grounded threat intelligence extraction.
Improved, precise, and high-recall automated threat intelligence extraction significantly enhances cybersecurity defense postures, enabling faster and more accurate response to attacks.
The ability to automatically extract MITRE ATT&CK techniques with high precision and recall means a more efficient and effective identification of adversary tactics, techniques, and procedures.
- · Cybersecurity companies
- · National security agencies
- · Large enterprises (critical infrastructure)
- · AI/ML developers specializing in security
- · Threat actors (to a limited degree)
- · Companies with weak cyber-defenses
- · Manual threat intelligence analysts (tasks automated)
More accurate and faster automated identification of cyber threats will lead to enhanced defensive capabilities for organizations.
This improved threat intelligence could enable proactive countermeasure development and predict attack patterns more effectively, altering the asymmetrical advantage in cyber warfare.
The widespread adoption of such precise extraction could shift the focus of cyberattacks towards novel, less detectable techniques, spurring new cycles of defensive innovation.
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Read at arXiv cs.CL