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ExploitBench

ExploitBench measures stages of progress on known V8 vulnerabilities, using separate capability checks rather than one binary outcome.

The initial v8-bench release: 41 patched V8 vulnerabilities and 16 capability flags.

What it measures

ExploitBench’s first benchmark, v8-bench, uses 41 patched vulnerabilities in the V8 JavaScript and WebAssembly engine. It records 16 capability flags grouped into five tiers, ranging from reaching affected code and reproducing a bug to stronger control capabilities. The flags preserve useful evidence of partial progress.

ExploitBench paper · Official V8 benchmark specification

A known-vulnerability setting

The agent receives vulnerability context, including a patch diff, source code, and relevant program builds, and produces a test artifact for the benchmark environment. This is evaluation on already identified, patched bugs. It should be distinguished from discovering an unknown vulnerability without such starting information.

ExploitBench official repository · Official V8 benchmark specification

Read capability coverage carefully

Deterministic verifiers check the capability flags; an LLM judge does not assign the result. Capability coverage measures achieved flags, not the percentage of vulnerabilities fully exploited. The public results distinguish fixed experiments from a maximum-achievement view with varying seeds and budgets, so preserve the reported aggregation and regime.

ExploitBench official methodology and result views · Official V8 benchmark specification

When it is useful

Use it to investigate where progress stops within a difficult software target and to compare agent configurations under controlled conditions. A tier profile can distinguish reliable bug reproduction from later capabilities that a single success percentage would hide, while still crediting intermediate work.

ExploitBench paper · ExploitBench official repository

Version and protocol boundaries

The initial suite is specific to V8 and public patched bugs; it does not establish general zero-day discovery ability. Baseline runs, automated coaching, and native CLI agents use different protocols. Public prior information and runtime conditions can affect results, making matched settings essential for a fair comparison.

ExploitBench official methodology and result views · ExploitBench paper · ExploitBench official repository

Data source and credit: LLM Stats

Evidence available for this benchmark

This source record contains 5 numeric observations. The source distinguishes 5 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: 2026-10-13

GitHub stars
429
Hugging Face paper upvotes
0

exploitbench/exploitbench · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
LLM Stats
Publisher
Not recorded
Benchmark release
Not recorded
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 5

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

ModelValueUnitProtocolReported date and basisSource
Claude Fable 50.78Not recordedNot recorded2026-06-09 · model_announcementLLM Stats
GPT-5.6 Sol0.735Not recordedNot recorded2026-07-09 · model_announcementLLM Stats
GLM-5.30.544Not recordedNot recorded2026-08-14 · model_announcementLLM Stats
GPT-5.6 Terra0.529Not recordedNot recorded2026-07-09 · model_announcementLLM Stats
GPT-5.6 Luna0.332Not recordedNot recorded2026-07-09 · model_announcementLLM Stats