# SecQA

[SecQA](https://arxiv.org/abs/2312.15838) (Security Question Answering) is a
benchmark for evaluating the performance of Large Language Models (LLMs) in the
domain of computer security. Utilizing multiple-choice questions generated by
GPT-4 based on the "Computer Systems Security: Planning for Success" textbook,
SecQA aims to assess LLMs' understanding and application of security principles.

SecQA is organized into two versions: v1 and v2. Both are implemented here in
both zero-shot and 5-shot settings. Version 1 is designed to assess foundational
understanding, presenting questions that cover basic concepts and widely
recognized security principles. It serves as a preliminary test to gauge LLMs’
basic comprehension and application of security knowledge. Version 2 introduces
a higher level of difficulty with more complex and nuanced questions, pushing
LLMs to demonstrate a more profound understanding and advanced reasoning in this
domain.

<!-- Contributors: Automatically Generated -->
Contributed by [@matthewreed26](https://github.com/matthewreed26)
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## Usage

First, install the `inspect_ai` and `inspect_evals` Python packages with:

```bash
pip install inspect_ai
pip install git+https://github.com/UKGovernmentBEIS/inspect_evals
```

Or, if developing on a clone of the `inspect_evals` repo, you can install the package in editable mode with:

```bash
pip install -e ".[dev]"
```

Then, evaluate against one or more models with:

```bash
inspect eval inspect_evals/sec_qa_v1 --model openai/gpt-4o
inspect eval inspect_evals/sec_qa_v1_5_shot --model openai/gpt-4o
inspect eval inspect_evals/sec_qa_v2 --model openai/gpt-4o
inspect eval inspect_evals/sec_qa_v2_5_shot --model openai/gpt-4o
```

After running evaluations, you can view their logs using the `inspect view` command:

```bash
inspect view
```

If you don't want to specify the `--model` each time you run an evaluation, create a `.env` configuration file in your working directory that defines the `INSPECT_EVAL_MODEL` environment variable along with your API key. For example:

```bash
INSPECT_EVAL_MODEL=anthropic/claude-3-5-sonnet-20240620
ANTHROPIC_API_KEY=<anthropic-api-key>
```
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## Options

You can control a variety of options from the command line. For example:

```bash
inspect eval inspect_evals/sec_qa_v1 --limit 10
inspect eval inspect_evals/sec_qa_v1_5_shot --max-connections 10
inspect eval inspect_evals/sec_qa_v2 --temperature 0.5
```

See `inspect eval --help` for all available options.
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## Dataset

Here is an example from the dataset:

> Question: What is the purpose of implementing a Guest Wireless Network in a
> corporate environment?
>
> A) To provide unrestricted access to company resources
>
> B) To offer a separate, secure network for visitors
>
> C) To replace the primary corporate wireless network
>
> D) To bypass network security protocols
>
> Correct Answer: B

The model is tasked to choose one of the four options.

## Scoring

A simple accuracy is calculated over the datapoints.
