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LLM Stats

CyBench

Cybench evaluates agents on professional CTF challenges, with optional subtasks that reveal progress before the final answer.

The original 40-task suite and its unguided, subtask-guided, and subtask metrics.

What it measures

Cybench contains 40 challenges drawn from four professional Capture the Flag competitions. The tasks span cryptography, web security, reverse engineering, forensics, binary exploitation, and miscellaneous problems. It evaluates an agent’s ability to investigate a contained challenge and reach a checkable answer through multiple actions.

Cybench paper · Stanford CRFM introduction to Cybench

What a task contains

Each task supplies a description, starter files, and an evaluator. The agent works in a Kali Linux container, with local artifacts and task-specific services where needed. In guided runs, intermediate questions are supplied sequentially; unguided runs present the overall objective without that decomposition.

Cybench official repository · Stanford CRFM introduction to Cybench

Three different success measures

Unguided success checks the complete task. Subtask-guided success checks the final subtask, while the subtask score measures intermediate completion and is macro-averaged across tasks. First Solve Time describes the fastest human team’s competition solve time for a task; it is a difficulty reference, not the agent’s runtime.

Cybench official website and metric definitions · Stanford CRFM introduction to Cybench

When it is useful

Use it to study tool use, sustained problem solving, and the effect of explicit intermediate guidance. The distinction between partial progress and a completed challenge helps identify where an agent stalls, rather than treating every incomplete run as equally informative.

Stanford CRFM introduction to Cybench

Compare matching protocols

CTF performance is evidence about these challenge environments, not a direct measurement of real enterprise intrusions. Published results may use subsets of the original suite or different trial aggregation. Compare the task list, guidance mode, agent harness, and budget before interpreting a percentage difference.

Cybench official website and metric definitions · Cybench paper

Data source and credit: LLM Stats

Evidence available for this benchmark

This source record contains 2 numeric observations. The source distinguishes 2 model identities or configurations. A model may appear more than once; check the version, tools and run budget before comparing. Read results and row-level evidence →

Metric direction
higher is better

Start with these resources

Cited documents and dates (1)

Catalog collected on 2026-08-17; resources and fields reflect that saved source.

Recorded attention

Observed on: 2026-10-10

GitHub stars
341
Hugging Face paper upvotes
6

andyzorigin/cybench · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
LLM Stats
Publisher
Not recorded
Benchmark release
2024-08-15
Collected
2026-08-17
Modality
text

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 2

Shown in source observation order, not ranked. Dates retain the precision provided by the source.

ModelValueUnitProtocolReported date and basisSource
Claude Mythos Preview1.0Not recordedNot recorded2026-04-07 · model_announcementLLM Stats
Grok-4.1 Thinking0.39Not recordedNot recorded2025-11-17 · model_announcementLLM Stats