LLM-Based CVE Detection for Network Request Vulnerability Matching
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Solution Overview
Problem
Current vulnerability detection systems, such as Intrusion Detection Systems (IDS), face challenges in efficiently identifying and maintaining signatures for Common Vulnerabilities and Exposures (CVEs) due to time consumption, reliance on human expertise, and limited scalability, leading to ineffective alert prioritization and maintenance burdens.
Innovation Solution
Utilizing a large language model (LLM) to automate the enrichment of CVE data, enabling organizations to identify specific CVE exploitations in network traffic without human signatures, and enhance cybersecurity posture by extracting key properties and contextual insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based approaches are used in IDS to detect vulnerabilities, then detection accuracy for known threats is improved, but time consumption and maintenance burden increase
Solution Approach 1:
The system performs preliminary actions by pre-processing network traffic data and pre-computing vulnerability signatures before actual detection occurs. The IDS is configured with pre-loaded CVE databases and signature patterns, allowing it to quickly match incoming traffic against known vulnerabilities without real-time computation overhead.
Solution Approach 2:
The patent employs copying by creating and maintaining a database of copied vulnerability signatures and attack patterns from known CVEs. Instead of analyzing every vulnerability from scratch, the system replicates known attack signatures and stores them for efficient comparison against incoming network traffic, significantly reducing detection time.
2Measurement precision
If human expertise is relied upon to craft vulnerability signatures, then detection precision is improved, but scalability and ease of operation deteriorate
Solution Approach 1:
The IDS system performs self-service by automatically generating vulnerability signatures and updating its detection rules without requiring manual intervention from security experts. The system autonomously fetches CVE data, analyzes vulnerability patterns, and creates detection signatures, thereby eliminating the need for continuous human expertise while maintaining scalability.
Solution Approach 2:
The patent replaces the mechanical process of manual signature crafting by human experts with an automated computational system. Machine learning algorithms and automated analysis tools substitute human analysts, enabling the system to process and adapt to new vulnerabilities at scale without being constrained by human capacity or expertise availability.
3Reliability
If manual signature updates are performed to maintain IDS effectiveness, then detection reliability is improved, but productivity and ease of operation worsen
Solution Approach 1:
The system implements feedback mechanisms that automatically monitor new CVE publications and vulnerability databases. When new vulnerabilities are detected in external sources, the IDS receives feedback and automatically updates its signature database, ensuring continuous detection reliability without requiring manual maintenance intervention.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining continuous automated updates of vulnerability signatures and detection rules. The system operates continuously to fetch, analyze, and integrate new vulnerability data from external sources, eliminating gaps in protection and removing the need for periodic manual maintenance while preserving detection reliability.
Data Source
AI summary
In one implementation, a device identifies a first set of one or more Common Vulnerabilities and Exposures (CVEs) by searching a CVE database based on a request sent via a network towards a service. The device also identifies a second set of one or more CVEs by querying a large language model (LLM) based on the request. The device determines that the request is associated with a particular CVE based on the first set of one or more CVEs and the second set of one or more CVE. The device initiates a corrective measure with respect to the request in the network.