26.3.2 Benchmark Runner Framework

The Need for a Standardized Executor

As you expand your security testing, you’ll face a combinatorial explosion of variables: models, datasets, attack methods, and evaluation metrics. A dedicated runner framework addresses this complexity by decoupling the components of an evaluation. It acts as an orchestrator, allowing you to define experiments in a declarative way (e.g., in a configuration file) and then executes them consistently.

The core benefits include:

• Reproducibility: Every test run is based on a version-controlled configuration file, eliminating “it worked on my machine” issues.
• Scalability: Easily add new models, datasets, or attack algorithms without rewriting the core execution logic.
• Efficiency: Automate the entire pipeline from data loading to report generation, freeing up your team to focus on analysis rather than manual execution.
• Comparability: Ensures that all tests are run under the same conditions, making cross-model or cross-attack comparisons valid.

Architectural Overview

A well-designed benchmark runner is modular. It treats models, datasets, attacks, and metrics as pluggable components. The framework’s job is to wire these components together based on a user-defined configuration and manage the execution flow.

Defining Experiments with Configuration

The entry point for any benchmark run is a configuration file. This file explicitly defines what to test and how. Using a human-readable format like YAML is highly recommended. It allows you to define the matrix of tests without touching the runner’s code.

Example `config.yaml`

# config.yaml: Defines the benchmarking tasks

# List of models to evaluate
models:
  - name: 'sentiment-distilbert-v1'
    path: './models/distilbert'
    type: 'text-classification'

  - name: 'toxic-comment-roberta-v2'
    path: 'hf-hub/roberta-base-toxic'
    type: 'text-classification'

# List of datasets to use for evaluation
datasets:
  - name: 'imdb-test-sample'
    path: './data/imdb_test_100.csv'
    label_column: 'sentiment'

# List of attacks and their parameters
attacks:
  - name: 'TextFooler'
    module: 'attacks.text.textfooler'
    params:
      max_candidates: 50

  - name: 'DeepWordBug'
    module: 'attacks.text.deepwordbug'
    params:
      scorer: 'char_swap'

# Metrics to compute for each run
metrics:
  - 'accuracy'
  - 'attack_success_rate'
  - 'query_count'

Implementation Sketch

The runner itself can be implemented as a class in a language like Python. Its primary role is to parse the configuration, instantiate the necessary objects (models, attacks), and loop through the defined experiments, collecting results along the way.

# pseudocode/python for a simple benchmark runner
import yaml

class BenchmarkRunner:
    def __init__(self, config_path):
        # 1. Load and parse the configuration file
        with open(config_path, 'r') as f:
            self.config = yaml.safe_load(f)
        self.results = []

    def run(self):
        # 2. Iterate through each combination of model, dataset, and attack
        for model_conf in self.config['models']:
            model = self.load_model(model_conf)
            for dataset_conf in self.config['datasets']:
                dataset = self.load_dataset(dataset_conf)
                for attack_conf in self.config['attacks']:
                    attack = self.load_attack(attack_conf)
                    
                    # 3. Execute the test
                    print(f"Running {attack.name} on {model.name}...")
                    raw_results = self.execute_test(model, dataset, attack)
                    
                    # 4. Compute metrics and store them
                    metrics = self.compute_metrics(raw_results, self.config['metrics'])
                    self.results.append(metrics)
    
    def generate_report(self):
        # 5. Aggregate all results into a final report (e.g., CSV, JSON)
        print("Generating final report...")
        # ... logic to save self.results to a file ...

# Usage:
# runner = BenchmarkRunner('config.yaml')
# runner.run()
# runner.generate_report()

In this sketch, methods like load_model, load_dataset, and load_attack would contain the logic to dynamically import and instantiate the correct classes based on the configuration strings. The execute_test method would contain the core loop that feeds data samples to the attack algorithm, which in turn queries the model.

Key Component Responsibilities

Component	Responsibility
Configuration Parser	Reads YAML/JSON file and provides a structured representation (e.g., a dictionary) of the experiments to run.
Resource Loaders	Dynamically instantiates model, dataset, and attack objects based on configuration. Handles loading weights, data files, etc.
Test Executor	Orchestrates a single test run. Iterates over the dataset, applies the attack, gets model predictions, and collects raw outputs.
Metrics Calculator	Takes raw outputs from the executor and computes summary statistics like accuracy, attack success rate, or perturbation levels.
Report Generator	Aggregates metrics from all test runs and formats them into a human-readable and machine-parsable report (e.g., CSV, markdown table, JSON).

Building or adopting a benchmark runner framework is a critical step in maturing your AI red teaming capabilities. It moves your operations from ad-hoc scripts to a systematic, repeatable, and scalable evaluation process, which is essential for tracking security posture over time and comparing defenses effectively.