cai/benchmarks

AI Model Evaluation Benchmarks

This chapter is a curated collection of benchmark datasets and evaluation tools designed to assess the capabilities of custom AI models, particularly in domains related to cybersecurity.

The collection is intended to support researchers and developers who are evaluating their own models using reliable, task-specific benchmarks.

Currently, this are the benchmarks included:

Benchmark	Description
SecEval	Benchmark designed to evaluate large language models (LLMs) on security-related tasks. It includes various real-world scenarios such as phishing email analysis, vulnerability classification, and response generation.
CyberMetric	Benchmark framework that focuses on measuring the performance of AI systems in cybersecurity-specific question answering, knowledge extraction, and contextual understanding. It emphasizes both domain knowledge and reasoning ability.
CTIBench	Benchmark focused on evaluating LLM models' capabilities in understanding and processing Cyber Threat Intelligence (CTI) information.
PentestPerf	An internal benchmarking framework that measures penetration testing capabilities of LLM models in a proprietary set of IT, OT and robotics scenarios. Reach out if you wish to cooperate in this direction.
CyberPII-Bench	Benchmark designed to evaluate the ability of LLM models to maintain privacy and handle Personally Identifiable Information (PII) in cybersecurity contexts. Built from real-world data generated during offensive hands-on exercises conducted with CAI (Cybersecurity AI).

The goal is to consolidate diverse evaluation tasks under a single framework to support rigorous, standardized testing.

📊 General Summary Table

Model	SecEval	CyberMetric	Total Value
model_name	XX.X%	XX.X%	XX.X%

▶️ Usage

git submodule update --init --recursive  # init submodules
pip install cvss

Set the API_KEY for the corresponding backend as follows in .env: NAME_BACKEND + API_KEY

OPENAI_API_KEY = "..."
ANTHROPIC_API_KEY="..."
OPENROUTER_API_KEY="..."

Some of the backends need and url to the api base, set as follows in .env: NAME_BACKEND + API_BASE:

OLLAMA_API_BASE="..."
OPENROUTER_API_BASE="..."

Once evething is configured run the script

python benchmarks/eval.py --model MODEL_NAME --dataset_file INPUT_FILE --eval EVAL_TYPE --backend BACKEND

Arguments:
    -m, --model         # Specify the model to evaluate (e.g., "gpt-4", "ollama/qwen2.5:14b")
    -d, --dataset_file  # IMPORTANT! By default: small test data of 2 samples 
    -B, --backend       # Backend to use: "openai", "openrouter", "ollama" (required)
    -e, --eval          # Specify the evaluation benchmark

Output:
   outputs/
   └── benchmark_name/
       └── model_date_random-num/
           ├── answers.json       # the whole test with LLM answers
           └── information.txt    # report of that precise run (e.g. model_name, benchmark_name, metrics, date)

🔍 Examples

How to run different CTI Bench tests with the "llama/qwen2.5:14b" model using Ollama as the backend

python benchmarks/eval.py --model ollama/qwen2.5:14b --dataset_file benchmarks/cybermetric/CyberMetric-2-v1.json --eval cybermetric --backend ollama

python benchmarks/eval.py --model ollama/qwen2.5:14b --dataset_file benchmarks/seceval/eval/datasets/questions-2.json --eval seceval --backend ollama

How to run different CTI Bench tests with the "qwen/qwen3-32b:free" model using Openrouter as the backend

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

python benchmarks/eval.py --model qwen/qwen3-32b:free  --dataset_file benchmarks/cti_bench/data/cti-ate2.tsv --eval cti_bench --backend openrouter

How to run different backends such as openai and anthropic

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

python benchmarks/eval.py --model claude-3-7-sonnet-20250219 --dataset_file benchmarks/cybermetric/CyberMetric-2-v1.json --eval cybermetric --backend anthropic