CertLLM: An Internet Asset Attribution Framework Combining SSL Certificates and Large Language Models

CertLLM is an innovative internet asset attribution framework developed by yangzz02 and implemented in Python. By combining the hard evidence from SSL certificates with the semantic reasoning capabilities of large language models (LLMs), it addresses issues in traditional asset attribution methods such as information fragmentation, semantic gaps, and high false positive rates, enabling more accurate asset ownership analysis.

In the field of internet security, asset attribution is a core challenge. Traditional methods rely on data such as IP addresses and domain WHOIS records, but they face issues like information fragmentation, semantic gaps (pure technical features struggle to capture semantic correlations), and high false positive rates (rule-based matching easily generates a large number of false positives). With the widespread adoption of SSL/TLS certificates, certificate information has become an important clue, but relying solely on precise matching of certificate fields still cannot solve the problem of semantic-level correlations.

CertLLM's technical architecture consists of three layers:

Certificate Evidence Layer

Collects hard evidence from SSL certificates such as Subject fields (organization name, unit, etc.), Issuer fields, SAN extensions (associated domains), and certificate chains, which are non-forgeable and authoritative.

LLM Semantic Reasoning Layer

Uses LLMs to solve problems like entity disambiguation (identifying name variations of the same organization), relationship inference (asset ownership relationships), and anomaly detection (suspicious certificate issuance behaviors).

Fusion Decision Layer

Comprehensively evaluates certificate field matching degree, LLM semantic similarity scores, historical confidence levels, and multi-source cross-validation results, integrating the advantages of hard evidence and semantic reasoning.

Enterprise Asset Management

Helps discover unknown subdomain/IP assets, identify certificate configuration errors/expiration risks, and monitor abnormal certificate issuance to prevent supply chain attacks.

Threat Intelligence Analysis

Discovers attackers' C2 infrastructure through certificate associations, identifies attack clusters sharing certificates, and tracks certificate abuse behaviors.

Compliance Auditing

Supports automated certificate asset inventory, certificate policy compliance checks, and certificate lifecycle management.

• Practicality First: Focuses on solving real engineering problems and provides directly deployable solutions.
• Evidence-Driven: Based on hard certificate evidence, enhanced by LLM reasoning to avoid AI hallucinations.
• Modular Design: Loosely coupled functional modules for easy customization and expansion.
• Controllable Cost: Reasonable LLM calling strategies balance effectiveness and API costs.

Limitations

• Data Coverage: The completeness and timeliness of certificate data affect attribution effectiveness.
• LLM Dependence: Relies on LLM service availability and cost.
• Privacy Considerations: Large-scale certificate scanning requires consideration of privacy compliance.

Future Directions

• Integrate more data sources (WHOIS, DNS records).
• Support private LLM deployment.
• Develop visual analysis interfaces.
• Build a knowledge graph of attribution results.

CertLLM provides a powerful tool for asset attribution by integrating traditional certificate analysis with LLM technology, which is an important exploration direction in this field. In complex network environments, this fusion solution is expected to become a standard practice for asset discovery and threat intelligence analysis. It is recommended that practitioners focusing on cybersecurity asset management deeply understand and try CertLLM.