CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection

Cristian Curaba, Denis D'Ambrosi, Alessandro Minisini, Natalia Pérez-Campanero Antolín·November 20, 2024

Summary

CryptoFormalEval is a benchmark that assesses Large Language Models' ability to autonomously identify vulnerabilities in cryptographic protocols through interaction with a theorem prover. It features a manually curated dataset of flawed protocols, a middleware for AI-agent theorem prover communication, and an automated system for evaluating detected vulnerabilities. The study aims to explore integrating LLMs with symbolic reasoning for automated cryptographic protocol security verification. The benchmark integrates LLMs and formal verification for automated detection of cryptographic protocol vulnerabilities, combining both aspects of machine learning and symbolic reasoning. It evaluates AI agents' ability to identify vulnerabilities using formal verification tools through a structured pipeline involving input, formalization, verification, and attack validation. The dataset, consisting of 15 protocols, tests formalization and reasoning capabilities without relying on memorization.

Key findings

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Introduction
Background
Overview of cryptographic protocols and their importance
Challenges in manually identifying vulnerabilities in cryptographic protocols
Role of Large Language Models (LLMs) in automated security assessment
Objective
To evaluate the capability of LLMs in autonomously identifying vulnerabilities in cryptographic protocols
To explore the integration of LLMs with symbolic reasoning for automated cryptographic protocol security verification
Method
Data Collection
Description of the manually curated dataset of flawed cryptographic protocols
Criteria for selecting and curating the protocols
Data Preprocessing
Process of formalizing the protocols for AI-agent theorem prover communication
Techniques for preparing the data for LLM interaction
AI-Agent Theorem Prover Communication Middleware
Functionality and design of the middleware
How it facilitates communication between LLMs and theorem provers
Automated System for Evaluating Detected Vulnerabilities
Overview of the system's architecture
Evaluation metrics and criteria for assessing the detected vulnerabilities
Benchmark Pipeline
Input
Description of the input protocols and their preparation for formal verification
Formalization
Process of converting protocols into a formal language understandable by theorem provers
Verification
Utilization of theorem provers for automated detection of vulnerabilities
Techniques for formal verification and reasoning
Attack Validation
Methodology for validating the identified vulnerabilities through simulation or other means
Dataset
Composition
Details of the 15 protocols included in the dataset
Characteristics and challenges they present for formalization and reasoning
Evaluation
How the dataset tests formalization and reasoning capabilities without relying on memorization
Results and Analysis
Performance Metrics
Quantitative and qualitative analysis of LLM performance
Comparison with existing methods or human experts
Insights and Findings
Key observations regarding the integration of LLMs with symbolic reasoning
Challenges and limitations encountered during the benchmark
Conclusion
Summary of Achievements
Recap of the benchmark's objectives and outcomes
Future Directions
Potential areas for further research and development
Recommendations for improving the integration of LLMs with formal verification in cryptographic protocol security
Basic info
papers
cryptography and security
symbolic computation
artificial intelligence
Advanced features
Insights
What is the main purpose of the CryptoFormalEval benchmark?
What is the goal of the study involving LLMs and symbolic reasoning in the context of cryptographic protocol security?
What components make up the CryptoFormalEval benchmark?
How does CryptoFormalEval integrate Large Language Models (LLMs) and formal verification for automated cryptographic protocol security verification?