CVE Exploit Labeling Using LLM Indicators and Match Confidence

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The increasing rate of vulnerability discovery and volume of exploits has overwhelmed the manual process of mapping malicious packets to CVE identifiers, leading to insufficient use of automated remediation services.

Innovation Solution

A system that determines CVE identifiers for labeling malicious packets by constructing and maintaining a database of exploit indicators mapped to corresponding CVE identifiers, using a language model to extract elements from cybersecurity data such as URI path components, header field names, and keywords, and generating multiple indicators for match confidence.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual mapping of malicious packets to CVE identifiers is used, then accuracy and reliability of labeling is maintained, but productivity and speed of vulnerability response deteriorates due to overwhelming volume of exploits

Engineering Contradiction:

Improvelabeling accuracyVSAvoidvulnerability response speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent introduces an intermediary system consisting of a language model and exploit indicator database that mediates between malicious packet analysis and CVE identifier assignment. The language model extracts exploit indicators from packet data, which are then matched against the database of known exploit indicators mapped to CVE identifiers, enabling automated accurate labeling without manual intervention

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the manual mechanical process of vulnerability labeling with an automated system using language models and database matching. The language model processes packet data and generates exploit indicators automatically, substituting human analysts with an AI-based system that maintains accuracy while dramatically improving throughput

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated labeling systems are implemented, then productivity and speed of vulnerability response is improved, but measurement precision and confidence in CVE identifier accuracy deteriorates due to complexity of automated systems

Engineering Contradiction:

Improvelabeling throughputVSAvoidCVE identifier confidence

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements feedback mechanisms where the language model generates exploit indicators that are matched against a pre-existing database of known indicators with known CVE identifiers. This feedback loop allows the system to learn from previous accurate mappings and adjust its labeling decisions, maintaining high confidence in automated assignments through continuous validation against established knowledge

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent creates copies of known exploit indicators and their associated CVE identifiers in a database, allowing the system to match new packet data against these templates. By copying and storing previously identified exploit patterns, the system can accurately label new exploits by matching them against the replicated knowledge base, ensuring precision through pattern recognition rather than guesswork

Inventive Principle:

Principle #26Copying

3Reliability

If manual CVE labeling process is used, then reliability of vulnerability mapping is maintained, but loss of time and resources increases due to limited availability of cybersecurity professionals

Engineering Contradiction:

Improvevulnerability mapping accuracyVSAvoidtime for manual labeling

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent enables the system to label vulnerabilities autonomously without requiring continuous human intervention. The language model automatically extracts exploit indicators from packet data, matches them against the database, and assigns CVE identifiers independently, making the labeling process self-service capable and eliminating the time loss associated with waiting for human analysts

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260106886A1CVE labeling for exploits using proof-of-concept and llm

Publication Date: 2026.04.16 PALO ALTO NETWORKS INC

AI summary

The disclosed system determines a CVE identifier based on match confidence against entries in a database of indicators of vulnerability exploits that are mapped to corresponding CVE identifiers. The system builds and maintains the database by generating these exploit indicators from various cybersecurity data having associated vulnerability identifiers. The system extracts elements from the cybersecurity data to construct different exploit indicators and then stores them in the database mapped to corresponding CVE identifiers. Depending upon the cybersecurity data from which elements are extracted, different types of indicators of an exploit may be generated for a same vulnerability.