# Threat Intelligence Eval

> This benchmark evaluates an AI system's ability to extract actionable insights from threat intelligence reports and perform security reasoning. It probes multi-document comprehension, attack chain reconstruction, and MITRE ATT&CK framework mapping capabilities. Use when the user wants to benchmark on CyberSOCEval Threat Intelligence Reasoning, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/threat-intelligence-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/threat-intelligence-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/threat-intelligence-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/threat-intelligence-eval

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# threat-intelligence-eval

> CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning — Deason et al. (2025) (arXiv:2509.20166, 2025)

## What this evaluates

This benchmark evaluates an AI system's ability to extract actionable insights from threat intelligence reports and perform security reasoning. It probes multi-document comprehension, attack chain reconstruction, and MITRE ATT&CK framework mapping capabilities.

## Datasets

- **CyberSOCEval Threat Intelligence Reasoning** — total 588; splits: test (588); repo https://github.com/CrowdStrike/cybersoceval_data

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - The share of questions for which the system selects all correct options and only the correct options. It is a strict exact-match metric for multi-label multiple-choice questions.

## Input / output format

**Input**: Multiple images representing a threat intelligence report, with one image provided per report page.

**Output**: A set of selected multiple-choice options (up to 6 possible answers per question).

## Scoring recipe

```python
def compute_accuracy(predictions, golds):
    correct = 0
    for pred, gold in zip(predictions, golds):
        if set(pred) == set(gold):
            correct += 1
    return correct / len(golds)
```

## Common pitfalls

- Partial credit is not awarded; models must select exactly the correct set of options to receive a point for a question.
- The input is provided as images of report pages rather than extractable text, requiring vision-language capabilities instead of pure text processing.

## Evidence (verbatim from paper)

> Evaluation is based on accuracy: the share of questions for which the system selects all correct options and only the correct options.

## Citation

```bibtex
@misc{deason2025cybersoceval,
  title={CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning},
  author={Deason et al. (2025)},
  year={2025},
  note={arXiv:2509.20166}
}
```

- arXiv: 2509.20166