ExploitBench
41 recent V8 JavaScript-engine N-day vulnerabilities. Progress measured by 16 grader-verified capability flags across five tiers.
41 real V8 N-day vulnerabilities with production mitigations on. 16 binary capability flags over five tiers: coverage, bug triggering, V8 internal primitives, general primitives and arbitrary code execution. Flags are checked by oracles compiled into a patched d8 binary. Episodes cap at 300 turns. Saturated by GPT-6 Astra at 100%, and OpenAI built a post-cutoff internal port to check contamination.
In the authors’ words
Exploitation is not a binary event. It is a ladder of acquiring progressive capabilities, from executing a single buggy line of code to taking full control of the target. However, existing LLM security benchmarks treat a crash as exploitation success. That single binary outcome collapses the hard parts of exploitation: the transition from triggering a bug to constructing reusable primitives and control. We present ExploitBench, a capability-graded benchmark that decomposes exploitation into 16 measurable flags, from coverage and crash through sandbox primitives, arbitrary read/write, control-flow hijack, and arbitrary code execution. Each capability is verified by a deterministic oracle that uses a per-run randomized challenge-response for primitives, differential execution against ground-truth binaries to measure progress, and a signal-handler proof for code execution. We instantiate ExploitBench on 41 V8 bugs because V8 is both widely deployed and exploitation-hardened. We report three arms: <model,env> as the primary measurement of model-environment capability, <model,env, adaptive coaching> as a secondary arm that adds adaptive coaching to test whether targeted feedback shifts outcomes, and <model,env,harness> as an ablation that swaps in the model's native CLI to check whether vendor-side optimizations increase exploitation capabilities. Our results show a sharp capability split between publicly deployed frontier models and the private frontier. Across the 8 publicly deployed models tested, reaching the vulnerable code and triggering a crash is routine, but arbitrary code execution is not. The private model shows arbitrary code execution on approximately half. Overall, results suggest that exploit construction against hardened targets is an emerging frontier capability.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
GPT-6 Astra leads with 100%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 100.0 GPT-6 Astra
- 99.7 GPT-6.1 Sol
- 97.1 GPT-6.1 Terra
- 91.0 Claude Opus 5.5
- 87.0
Gemini 4 Argon
- 82.6 GPT-6 Sol
- 80.0 Claude Sonnet 5.5
- 74.7 Kimi K3.1 Pro
- 70.0 Claude Opus 5
- 58.9
Muse Wonder Full
- 54.4 GLM-5.3
- 52.9 GPT-5.6 Terra
- 49.0 Claude Haiku 5.5
- 47.9 MiMo-V2.6 Pro
- 46.2 GPT-6 Luna
- 33.2 GPT-5.6 Luna
- 32.0 Kimi K3
- 31.0 Claude Sonnet 5
- 28.7
Muse Spark 1.3
- 1 GPT-6 Astra 100%
- 2 GPT-6.1 Sol 99.7%
- 3 GPT-6.1 Terra 97.1%
- 4 Claude Opus 5.5 91%
- 5
Gemini 4 Argon 87%
- 6 GPT-6 Sol 82.6%
- 7 Claude Sonnet 5.5 80%
- 8 Kimi K3.1 Pro 74.7%
- 9 Claude Opus 5 70%
- 10
Muse Wonder Full 58.9%
- 11 GLM-5.3 54.4%
- 12 GPT-5.6 Terra 52.9%
- 13 Claude Haiku 5.5 49%
- 14 MiMo-V2.6 Pro 47.9%
- 15 GPT-6 Luna 46.2%
- 16 GPT-5.6 Luna 33.2%
- 17 Kimi K3 32%
- 18 Claude Sonnet 5 31%
- 19
Muse Spark 1.3 28.7%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 100% | Sep 2026 | Model card | |
| 2 | GPT-6.1 Sol | 99.7% | Sep 2026 | Model card | |
| 3 | GPT-6.1 Terra | 97.1% | Oct 2026 | Model card | |
| 4 | Claude Opus 5.5 | 91% | Sep 2026 | Model card | |
| 5 | | 87% | Oct 2026 | Model card | |
| 6 | GPT-6 Sol | 82.6% | Oct 2026 | Model card | |
| 7 | Claude Sonnet 5.5 | 80% | Sep 2026 | Model card | |
| 8 | Kimi K3.1 Pro | 74.7% | Oct 2026 | Model card | |
| 9 | Claude Opus 5 | 70% | Jul 2026 | Model card | |
| 10 | | 58.9% | Oct 2026 | Model card | |
| 11 | GLM-5.3 | 54.4% | Aug 2026 | Model card | |
| 12 | GPT-5.6 Terra | 52.9% | Jul 2026 | Model card | |
| 13 | Claude Haiku 5.5 | 49% | Oct 2026 | Model card | |
| 14 | MiMo-V2.6 Pro | 47.9% | Sep 2026 | Model card | |
| 15 | GPT-6 Luna | 46.2% | Oct 2026 | Model card | |
| 16 | GPT-5.6 Luna | 33.2% | Jul 2026 | Model card | |
| 17 | Kimi K3 | 32% | Jul 2026 | Model card | |
| 18 | Claude Sonnet 5 | 31% | Jun 2026 | Model card | |
| 19 | | 28.7% | Oct 2026 | Model card |
The benchmark's own leaderboard has 9 models. Epoch AI has scores for 9 models. We plot every source in the charts below.
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard, Epoch AI’s runs and what labs claim in their cards.
The best score passed 85% in Sep 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
4 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 24 Oct 2026 | Kimi K3.1 Pro | 74.7 | ExploitBench (pass@5 max capability score, 300 turns) |
| 22 Sep 2026 | MiMo-V2.6 Pro | 47.9 | ExploitBench |
| 14 Aug 2026 | GLM-5.3 | 54.4 | ExploitBench |
| 9 Jul 2026 | GPT-5.6 Luna | 33.2 | ExploitBench |
| 9 Jul 2026 | GPT-5.6 Terra | 52.9 | ExploitBench |
25 system cards report it, from 6 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 29 Oct 2026 | GPT-6 Luna system card addendum | 46.2 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| 26 Oct 2026 | GPT-6.1 Terra system card addendum | 97.1 | ExploitBench, ACE-normalized mean capability score, max tested solution length (Figure 26, 9.0.2.1) | Cyber risk |
| 23 Oct 2026 | | 28.7 | ExploitBench capability coverage with AutoNudge, Muse Spark 1.3 (max) (Table 1, Table 3). Mean capability score 4.16 | Cyber risk |
| 13 Oct 2026 | | 87 | ExploitBench, autonudge, 300-turn limit, best-of-three union, capabilities achieved 87% of 656 (7.1.1) | Cyber risk |
| 12 Oct 2026 | | 58.9 | ExploitBench, Muse Wonder Full, metric not named (Cyber Capability Evaluations table, section 4) | Cyber risk |
| 7 Oct 2026 | GPT-6 Sol system card addendum | 82.6 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| 7 Oct 2026 | GPT-6 Luna system card addendum | 44.7 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| 7 Oct 2026 | Claude Haiku 5.5 system card | 49 | ExploitBench, Cap% in the AutoNudge arm shown in the figure (mean 6.56 flags). Plain arm Cap% 38 (mean 5.15 flags). Full ACE 4/410 runs (3.3.1, Figure 3.3.1.A) | Cyber risk |
| 29 Sep 2026 | GPT-6.1 Sol system card addendum | 99.7 | ExploitBench, maximum reasoning effort (9.1.2.1) | Cyber risk |
| 28 Sep 2026 | Claude Sonnet 5.5 system card | 80 | ExploitBench, AutoNudge arm Cap% 80%, mean 11.53 flags, full ACE in 178 of 410 runs (43.4%) (3.2.1, Figure 3.2.1.A) | Cyber risk |
| 22 Sep 2026 | MiMo-V2.6 Pro technical report | 47.9 | ExploitBench (Table 3) | Cyber risk |
| 22 Sep 2026 | GPT-6 Sol system card | 81.7 | ExploitBench, capability coverage at maximum reasoning effort (A.8.1.2.1, Figure 79) | Cyber risk |
| 22 Sep 2026 | GPT-6 Luna system card | 43.4 | ExploitBench, capability coverage at maximum reasoning effort (A.8.1.2.1, Figure 79) | Cyber risk |
| 22 Sep 2026 | Claude Opus 5.5 system card | 91 | ExploitBench, Cap% 91% in both the plain and AutoNudge arms (mean 13.99 flags plain, 14.15 AutoNudge), full ACE in 301 of 410 runs (73.4%), 41 V8 environments (3.3.1, Figure 3.3.1.A) | Cyber risk |
| 3 Sep 2026 | GPT-6 Astra system card | 100 | ExploitBench, Cap Percent, all reasoning efforts tested (10.1.2.1) | Cyber risk |
| 27 Jul 2026 | Kimi K3 technical report | 32 | ExploitBench, UK AISI and CAISI joint assessment cited in 6.2.2. Arbitrary code execution on 0 of 41 tasks | Cyber risk |
| 24 Jul 2026 | Claude Opus 5 system card | 70 | ExploitBench, AutoNudge Cap% 70 over 41 V8 environments, mean flags 10.14 AutoNudge and 9.62 plain, 99 full ACE exploits across both arms, production safety interventions off (3.3.1, Figure 3.3.1.A) | Cyber risk |
| 9 Jul 2026 | GPT-5.6 Terra system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 9 Jul 2026 | GPT-5.6 Sol system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 9 Jul 2026 | GPT-5.6 Luna system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 30 Jun 2026 | Claude Sonnet 5 system card | 31 | ExploitBench, AutoNudge arm Cap% 31, mean 4.18 flags (plain arm mean 3.96), full ACE 0, 41 V8 environments, five trials (3.2.1, Figure 3.2.1.A) | Cyber risk |
| 25 Jun 2026 | GPT-5.6 Terra system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 25 Jun 2026 | GPT-5.6 Sol system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 25 Jun 2026 | GPT-5.6 Luna system card | ExploitBench, cap percent by output tokens, chart without printed numbers (Figure 33, 9.1.2.4.1) | Cyber risk | |
| 28 May 2026 | Claude Opus 4.8 system card | 5.45 | ExploitBench v2, mean capability flags out of 16 over three trials, AutoNudge arm, no safeguards (plain arm 5.02, plain arm with Tier-3 safeguards 0.46 on chart) (3.3.1, Figure 3.3.1.A) | Cyber risk |
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