Knowledge Benchmarks

Knowledge benchmarks evaluate AI models' understanding of cybersecurity concepts, threat intelligence, vulnerability analysis, and security best practices through question-answering and knowledge extraction tasks.

📊 Available Benchmarks

SecEval

Benchmark designed to evaluate LLMs on security-related tasks including phishing email analysis, vulnerability classification, and response generation.

Type: Multiple choice and open-ended questions
Coverage: Phishing detection, malware analysis, vulnerability assessment, security policy
Dataset: Real-world security scenarios
Source: SecEval Repository

CyberMetric

Framework focusing on measuring AI performance in cybersecurity-specific question answering, knowledge extraction, and contextual understanding.

Type: Question-answering with contextual reasoning
Coverage: Security concepts, best practices, incident response, threat modeling
Emphasis: Domain knowledge and reasoning ability
Source: CyberMetric Repository

CTIBench

Benchmark focused on evaluating LLM capabilities in understanding and processing Cyber Threat Intelligence (CTI) information.

Type: Multiple choice questions and attribute extraction
Coverage: Threat actor analysis, malware attribution, IOC extraction, MITRE ATT&CK mapping
Dataset: CTI-MCQ (multiple choice) and CTI-ATE (attribute extraction)
Source: CTIBench Repository

🎯 What Knowledge Benchmarks Measure

Security Concept Understanding

Vulnerability types and classifications
Attack vectors and techniques
Defense mechanisms and controls
Security principles and best practices

Threat Intelligence

Threat actor capabilities and motivations
Malware families and characteristics
Indicators of Compromise (IOCs)
Tactics, Techniques, and Procedures (TTPs)

Incident Response

Incident detection and classification
Response procedures and priorities
Forensic analysis techniques
Recovery and remediation strategies

Risk Assessment

Threat modeling methodologies
Vulnerability scoring (CVSS)
Risk prioritization frameworks
Security architecture evaluation

🏆 alias1 Knowledge Performance

Superior Knowledge Capabilities

alias1 demonstrates exceptional performance on cybersecurity knowledge benchmarks:

🥇 Highest accuracy across all three major knowledge benchmarks
🥇 Contextual understanding - Correctly interprets complex security scenarios
🥇 Zero refusals - Provides comprehensive answers for all security questions
🥇 Technical depth - Detailed explanations with practical examples

General-purpose models show: - ❌ Lower accuracy on specialized security concepts - ❌ Oversimplified or generic responses - ❌ Refusals on sensitive security topics - ❌ Missing contextual nuances in CTI analysis

Get alias1 with CAI PRO →

🚀 Running Knowledge Benchmarks

Prerequisites

# Install dependencies
pip install cvss

# Configure API keys in .env file
ALIAS_API_KEY="sk-your-caipro-key"  # For alias1
OPENAI_API_KEY="sk-..."             # For OpenAI models
ANTHROPIC_API_KEY="sk-ant-..."      # For Anthropic models
OLLAMA_API_BASE="http://localhost:11434/v1"  # For local models

CyberMetric Evaluation

# Using alias1 (recommended)
python benchmarks/eval.py \
    --model alias1 \
    --dataset_file benchmarks/cybermetric/CyberMetric-2-v1.json \
    --eval cybermetric \
    --backend alias

# Using Ollama with Qwen
python benchmarks/eval.py \
    --model ollama/qwen2.5:14b \
    --dataset_file benchmarks/cybermetric/CyberMetric-2-v1.json \
    --eval cybermetric \
    --backend ollama

# Using OpenAI GPT-4o
python benchmarks/eval.py \
    --model gpt-4o-mini \
    --dataset_file benchmarks/cybermetric/CyberMetric-2-v1.json \
    --eval cybermetric \
    --backend openai

SecEval Evaluation

# Using alias1
python benchmarks/eval.py \
    --model alias1 \
    --dataset_file benchmarks/seceval/eval/datasets/questions-2.json \
    --eval seceval \
    --backend alias

# Using Anthropic Claude
python benchmarks/eval.py \
    --model claude-3-7-sonnet-20250219 \
    --dataset_file benchmarks/seceval/eval/datasets/questions-2.json \
    --eval seceval \
    --backend anthropic

CTIBench Evaluation

# Multiple choice questions
python benchmarks/eval.py \
    --model alias1 \
    --dataset_file benchmarks/cti_bench/data/cti-mcq1.tsv \
    --eval cti_bench \
    --backend alias

# Attribute extraction tasks
python benchmarks/eval.py \
    --model alias1 \
    --dataset_file benchmarks/cti_bench/data/cti-ate2.tsv \
    --eval cti_bench \
    --backend alias

# Using OpenRouter
python benchmarks/eval.py \
    --model qwen/qwen3-32b:free \
    --dataset_file benchmarks/cti_bench/data/cti-mcq1.tsv \
    --eval cti_bench \
    --backend openrouter

📁 Output Structure

Results are saved to structured directories:

outputs/
└── cybermetric/  (or seceval, cti_bench)
    └── alias1_20250115_abc123/
        ├── answers.json       # Complete test with responses
        └── information.txt    # Performance metrics

Example information.txt

Model: alias1
Benchmark: cybermetric
Accuracy: 92.5%
Total Questions: 100
Correct: 92
Incorrect: 8
Runtime: 145 seconds
Date: 2025-01-15
Backend: alias

📊 Evaluation Metrics

Accuracy

Percentage of correctly answered questions:

Accuracy = (Correct Answers / Total Questions) × 100%

Category Performance

Breakdown by question category: - Vulnerability analysis: 95% - Threat intelligence: 90% - Incident response: 88% - Security architecture: 92%

Response Quality

Qualitative assessment of answer quality: - Correctness - Completeness - Technical depth - Practical applicability

🎓 Why Knowledge Benchmarks Matter

Knowledge benchmarks are essential for evaluating cybersecurity AI because:

Foundation Skills - Tests understanding of core security concepts
Decision Making - Evaluates ability to make informed security judgments
Contextual Reasoning - Assesses comprehension beyond memorization
Practical Application - Measures ability to apply knowledge to scenarios
Domain Expertise - Validates specialized cybersecurity understanding

Unlike hands-on CTF challenges, knowledge benchmarks assess the theoretical foundation that enables effective security analysis and decision-making.

📚 Research Papers

📊 CAIBench: Cybersecurity AI Benchmark (2025) Includes knowledge benchmark evaluation methodology.
🚀 Cybersecurity AI (CAI) Framework (2025) Demonstrates knowledge-driven security operations.

View all research →

Privacy Benchmarks - PII handling evaluation
Jeopardy CTFs - Practical skill assessment
Running Benchmarks - Setup and usage guide

🚀 Get Started

Knowledge benchmarks are freely available to all CAI users.

Download CAI and start benchmarking →

For best performance, upgrade to CAI PRO for alias1 →