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Xiangsen Chen
Microsoft Research
X
Xuan Feng
Microsoft Research
S
Shuo Chen
Microsoft Research
M
Matthieu Maitre
Microsoft
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Founder's Pitch
"CyberThreat-Eval automates cybersecurity threat research workflows with LLMs to improve efficiency and accuracy in OSINT analysis."
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0-10 scaleHigh Potential
3/4 signals
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Why It Matters
The research introduces a comprehensive benchmark tailored for real-world cybersecurity intelligence tasks, addressing crucial gaps in existing LLM evaluations for threat research. This is vital as it paves the way for efficient automation in cybersecurity, potentially reducing the workload of human analysts and increasing the accuracy and speed of threat detection and intelligence reporting.
Product Angle
Convert CyberThreat-Eval into a managed service for cybersecurity firms, providing regular updates and customizable workflows to fit various organizational needs, enhancing integration with existing security frameworks.
Disruption
This solution could replace traditional, manual threat analysis methods and existing LLM benchmarks that do not cater to the specific, nuanced needs of cybersecurity workflows.
Product Opportunity
The cybersecurity market, particularly in threat intelligence, is rapidly growing due to increasing cyber threats. Companies are willing to pay for solutions that enhance the speed and accuracy of threat analysis, which CyberThreat-Eval can provide.
Use Case Idea
Develop a SaaS platform for cybersecurity analytics companies that leverages CyberThreat-Eval to automate OSINT analysis, offering an AI-powered tool to enhance threat intelligence operations.
Science
The paper introduces CyberThreat-Eval, a benchmark collected from real-world Cyber Threat Intelligence workflows, aimed at assessing LLMs through practical tasks involving triage, deep search, and threat intelligence drafting. The benchmark uses analyst-centric metrics such as factual accuracy and operational costs to evaluate the effectiveness of LLMs in handling OSINT data for threat reports.
Method & Eval
The study used an expert-annotated benchmark to evaluate LLMs across triage, deep search, and threat intelligence drafting tasks. Analyst-centric metrics were used to reveal limitations and strengths, showing LLMs' strong recall in triage but low precision and difficulty with TTP reasoning.
Caveats
The LLMs require external knowledge databases and human feedback for optimal performance. Additionally, current models may still struggle with nuanced expertise required for specific cybersecurity tasks.
Author Intelligence
Xiangsen Chen
LEAD Microsoft Research
Xuan Feng
Microsoft Research
Shuo Chen
Microsoft Research
Matthieu Maitre
Microsoft
Sudipto Rakshit
Microsoft
Diana Duvieilh
Microsoft
Ashley Picone
Microsoft
Nan Tang
Hong Kong University of Science and Technology