KaliBench: Benchmarking LLMs for Cybersecurity CLI Tool Use
KaliBench is a new benchmark designed to evaluate how well large language models (LLMs) can translate natural language instructions into precise command-line interface (CLI) commands for cybersecurity tools. It addresses a critical gap in current LLM evaluations by focusing on the practical execution of cybersecurity operations, where even small errors in commands can lead to failure.
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KaliBench is a new benchmark designed to evaluate how well large language models (LLMs) can translate natural language instructions into precise command-line interface (CLI) commands for cybersecurity tools. It addresses a critical gap in current LLM evaluations by focusing on the practical execution of cybersecurity operations, where even small errors in commands can lead to failure.
Imagine an LLM as a chef trying to follow a complex recipe. Existing benchmarks might test if the chef knows ingredients (knowledge) or can cook a whole meal (end-to-end). KaliBench, however, tests if the chef can precisely measure and combine ingredients using specific tools, where a wrong measurement or tool usage makes the dish inedible. It's about the exact 'syntax' of cooking.
How KaliBench Works
- 1KaliBench creates a dataset of 8,504 natural language queries paired with correct CLI commands for 1,642 cybersecurity tools on Kali Linux.
- 2It uses a 'manuscript-grounded pipeline' to ensure the commands are accurate and canonical, meaning they are the standard, correct way to execute the tool.
- 3A multi-stage verification process, including LLM-based checks, sandboxed terminal execution, and human review, confirms both the semantic correctness and practical executability of the commands.
- 4This fine-grained verification allows for 'runtime-free verifiable rewards,' which are signals used to train LLMs more effectively without needing to run the commands every time.
Accuracy = (Correct Commands / Total Commands) × 100%
This formula calculates the percentage of commands generated by an LLM that exactly match the correct, verified CLI command in the KaliBench dataset.
An analyst might ask, 'Scan the network for open ports on 192.168.1.1.' KaliBench would evaluate if the LLM correctly generates a command like 'nmap -p- 192.168.1.1' without any syntax errors or incorrect flags.
LLMs struggle significantly with generating precise, executable CLI commands for cybersecurity tools, highlighting a critical need for benchmarks that focus on exact command-line translation and practical executability.