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.
- Made by
- Carnegie Mellon (Lee & Brumley)
- Released
- May 2026
- Kind
- Finding and exploiting real bugs
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-6 Astra #3 on the capability index
- Last in a launch post
- November 2026
- Last model added
- November 2026
- Scores refreshed
- November 12th, 2026
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.
2 models share the top score of 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.
Claude Mythos Preview leads with 78% across all independent runs
Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.
GPT-6 Astra leads with 100%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 100.0 GPT-6 Astra
- 100.0 GPT-6 Cyber
- 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
- 34.8 SWE-2 Flash
- 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 Cyber 100%
- 3 GPT-6.1 Sol 99.7%
- 4 GPT-6.1 Terra 97.1%
- 5 Claude Opus 5.5 91%
- 6
Gemini 4 Argon 87%
- 7 GPT-6 Sol 82.6%
- 8 Claude Sonnet 5.5 80%
- 9 Kimi K3.1 Pro 74.7%
- 10 Claude Opus 5 70%
- 11
Muse Wonder Full 58.9%
- 12 GLM-5.3 54.4%
- 13 GPT-5.6 Terra 52.9%
- 14 Claude Haiku 5.5 49%
- 15 MiMo-V2.6 Pro 47.9%
- 16 GPT-6 Luna 46.2%
- 17 SWE-2 Flash 34.8%
- 18 GPT-5.6 Luna 33.2%
- 19 Kimi K3 32%
- 20 Claude Sonnet 5 31%
- 21
Muse Spark 1.3 28.7%
Source: Labs’ model cards
- 1 Claude Mythos Preview 78%
- 2 GPT-5.5 72%
- 3 Claude Opus 4.7 28%
- 4 Claude Sonnet 4.6 26%
- 5
Gemini 3.1 Pro 26%
- 6 GLM-5.1 18%
- 7 Kimi K2.6 18%
- 8 Claude Haiku 4.5 14%
- 9
MiniMax-M2.7 13%
Source: Official ExploitBench leaderboard
- 100.0 GPT-6 Astra
- 100.0 GPT-6 Cyber
- 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
- 78.0 Claude Mythos Preview
- 74.7 Kimi K3.1 Pro
- 72.0 GPT-5.5
- 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
- 34.8 SWE-2 Flash
- 33.2 GPT-5.6 Luna
- 32.0 Kimi K3
- 31.0 Claude Sonnet 5
- 28.7
Muse Spark 1.3
- 28.0 Claude Opus 4.7
- 26.0 Claude Sonnet 4.6
- 26.0
Gemini 3.1 Pro
- 18.0 GLM-5.1
- 18.0 Kimi K2.6
- 14.0 Claude Haiku 4.5
- 13.0
MiniMax-M2.7
- 1 GPT-6 Astra 100%
- 2 GPT-6 Cyber 100%
- 3 GPT-6.1 Sol 99.7%
- 4 GPT-6.1 Terra 97.1%
- 5 Claude Opus 5.5 91%
- 6
Gemini 4 Argon 87%
- 7 GPT-6 Sol 82.6%
- 8 Claude Sonnet 5.5 80%
- 9 Claude Mythos Preview 78%
- 10 Kimi K3.1 Pro 74.7%
- 11 GPT-5.5 72%
- 12 Claude Opus 5 70%
- 13
Muse Wonder Full 58.9%
- 14 GLM-5.3 54.4%
- 15 GPT-5.6 Terra 52.9%
- 16 Claude Haiku 5.5 49%
- 17 MiMo-V2.6 Pro 47.9%
- 18 GPT-6 Luna 46.2%
- 19 SWE-2 Flash 34.8%
- 20 GPT-5.6 Luna 33.2%
- 21 Kimi K3 32%
- 22 Claude Sonnet 5 31%
- 23
Muse Spark 1.3 28.7%
- 24 Claude Opus 4.7 28%
- 25 Claude Sonnet 4.6 26%
- 26
Gemini 3.1 Pro 26%
- 27 GLM-5.1 18%
- 28 Kimi K2.6 18%
- 29 Claude Haiku 4.5 14%
- 30
MiniMax-M2.7 13%
Sources: Labs’ model cards and official ExploitBench leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 100% | Sep 2026 | Lab model card | |
| 2 | GPT-6 Cyber | 100% | Nov 2026 | Lab model card | |
| 3 | GPT-6.1 Sol | 99.7% | Sep 2026 | Lab model card | |
| 4 | GPT-6.1 Terra | 97.1% | Oct 2026 | Lab model card | |
| 5 | Claude Opus 5.5 | 91% | Sep 2026 | Lab model card | |
| 6 | | 87% | Oct 2026 | Lab model card | |
| 7 | GPT-6 Sol | 82.6% | Oct 2026 | Lab model card | |
| 8 | Claude Sonnet 5.5 | 80% | Sep 2026 | Lab model card | |
| 9 | Kimi K3.1 Pro | 74.7% | Oct 2026 | Lab model card | |
| 10 | Claude Opus 5 | 70% | Jul 2026 | Lab model card | |
| 11 | | 58.9% | Oct 2026 | Lab model card | |
| 12 | GLM-5.3 | 54.4% | Aug 2026 | Lab model card | |
| 13 | GPT-5.6 Terra | 52.9% | Jul 2026 | Lab model card | |
| 14 | Claude Haiku 5.5 | 49% | Oct 2026 | Lab model card | |
| 15 | MiMo-V2.6 Pro | 47.9% | Sep 2026 | Lab model card | |
| 16 | GPT-6 Luna | 46.2% | Oct 2026 | Lab model card | |
| 17 | SWE-2 Flash | 34.8% | Nov 2026 | Lab model card | |
| 18 | GPT-5.6 Luna | 33.2% | Jul 2026 | Lab model card | |
| 19 | Kimi K3 | 32% | Jul 2026 | Lab model card | |
| 20 | Claude Sonnet 5 | 31% | Jun 2026 | Lab model card | |
| 21 | | 28.7% | Oct 2026 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Mythos Preview | 78% | AutoNudge | Apr 2026 | Official leaderboard |
| 2 | GPT-5.5 | 72% | Codex, AutoNudge | Apr 2026 | Official leaderboard |
| 3 | Claude Opus 4.7 | 28% | AutoNudge | Apr 2026 | Official leaderboard |
| 4 | Claude Sonnet 4.6 | 26% | AutoNudge | Feb 2026 | Official leaderboard |
| 5 | | 26% | baseline | Feb 2026 | Official leaderboard |
| 6 | GLM-5.1 | 18% | AutoNudge | Apr 2026 | Official leaderboard |
| 7 | Kimi K2.6 | 18% | AutoNudge | Apr 2026 | Official leaderboard |
| 8 | Claude Haiku 4.5 | 14% | AutoNudge | Oct 2025 | Official leaderboard |
| 9 | | 13% | baseline | Mar 2026 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 100% | Sep 2026 | Lab model card | |
| 2 | GPT-6 Cyber | 100% | Nov 2026 | Lab model card | |
| 3 | GPT-6.1 Sol | 99.7% | Sep 2026 | Lab model card | |
| 4 | GPT-6.1 Terra | 97.1% | Oct 2026 | Lab model card | |
| 5 | Claude Opus 5.5 | 91% | Sep 2026 | Lab model card | |
| 6 | | 87% | Oct 2026 | Lab model card | |
| 7 | GPT-6 Sol | 82.6% | Oct 2026 | Lab model card | |
| 8 | Claude Sonnet 5.5 | 80% | Sep 2026 | Lab model card | |
| 9 | Claude Mythos Preview | 78% | AutoNudge | Apr 2026 | Official leaderboard |
| 10 | Kimi K3.1 Pro | 74.7% | Oct 2026 | Lab model card | |
| 11 | GPT-5.5 | 72% | Codex, AutoNudge | Apr 2026 | Official leaderboard |
| 12 | Claude Opus 5 | 70% | Jul 2026 | Lab model card | |
| 13 | | 58.9% | Oct 2026 | Lab model card | |
| 14 | GLM-5.3 | 54.4% | Aug 2026 | Lab model card | |
| 15 | GPT-5.6 Terra | 52.9% | Jul 2026 | Lab model card | |
| 16 | Claude Haiku 5.5 | 49% | Oct 2026 | Lab model card | |
| 17 | MiMo-V2.6 Pro | 47.9% | Sep 2026 | Lab model card | |
| 18 | GPT-6 Luna | 46.2% | Oct 2026 | Lab model card | |
| 19 | SWE-2 Flash | 34.8% | Nov 2026 | Lab model card | |
| 20 | GPT-5.6 Luna | 33.2% | Jul 2026 | Lab model card | |
| 21 | Kimi K3 | 32% | Jul 2026 | Lab model card | |
| 22 | Claude Sonnet 5 | 31% | Jun 2026 | Lab model card | |
| 23 | | 28.7% | Oct 2026 | Lab model card | |
| 24 | Claude Opus 4.7 | 28% | AutoNudge | Apr 2026 | Official leaderboard |
| 25 | Claude Sonnet 4.6 | 26% | AutoNudge | Feb 2026 | Official leaderboard |
| 26 | | 26% | baseline | Feb 2026 | Official leaderboard |
| 27 | GLM-5.1 | 18% | AutoNudge | Apr 2026 | Official leaderboard |
| 28 | Kimi K2.6 | 18% | AutoNudge | Apr 2026 | Official leaderboard |
| 29 | Claude Haiku 4.5 | 14% | AutoNudge | Oct 2025 | Official leaderboard |
| 30 | | 13% | baseline | Mar 2026 | Official leaderboard |
The benchmark's own leaderboard has 9 models. Switch the scores above to rank them together.
The best score, 78%, costs $293 a task
Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official ExploitBench leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| Claude Mythos Preview | AutoNudge | 78% | $293 | ||
| Claude Mythos Preview | baseline | 72% | $206 | ||
| GPT-5.5 | Codex, AutoNudge | 72% | $255 | ||
| GPT-5.5 | AutoNudge | 47% | $66 | ||
| GPT-5.5 | baseline | 42% | $15 | ||
| GPT-5.5 | Codex, baseline | 33% | $10 | ||
| Claude Opus 4.7 | AutoNudge | 28% | $46 | ||
| Claude Sonnet 4.6 | AutoNudge | 26% | $54 | ||
| | baseline | 26% | $28 | ||
| Claude Sonnet 4.6 | baseline | 25% | $35 | ||
| Claude Opus 4.7 | baseline | 25% | $29 | ||
| GLM-5.1 | AutoNudge | 18% | $7.27 | ||
| Kimi K2.6 | AutoNudge | 18% | $7.23 | ||
| | AutoNudge | 16% | $20 | ||
| GLM-5.1 | baseline | 16% | $6.50 | ||
| Kimi K2.6 | baseline | 16% | $5.42 | ||
| Claude Haiku 4.5 | AutoNudge | 14% | $2.74 | ||
| Claude Haiku 4.5 | baseline | 14% | $0.85 | ||
| | baseline | 13% | $0.77 | ||
| | AutoNudge | 13% | $1.63 |
4 models have held number one in labs’ own numbers since its release in May 2026 Number one over time 2 models have held number one with claims included since its release in May 2026
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 and what labs claim in their cards.
Sources: Official ExploitBench leaderboard and labs’ model cards
OpenAI’s own number for GPT-6 Astra passed 85% in September 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official ExploitBench leaderboard and labs’ model cards
5 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 |
|---|---|---|---|
| Nov 5, 2026 | GPT-6 Cyber | 100 | ExploitBench, ACE-normalized mean capability score |
| Nov 4, 2026 | SWE-2 Flash | 34.8 | ExploitBench |
| Oct 24, 2026 | Kimi K3.1 Pro | 74.7 | ExploitBench (pass@5 max capability score, 300 turns) |
| Sep 22, 2026 | MiMo-V2.6 Pro | 47.9 | ExploitBench |
| Aug 14, 2026 | GLM-5.3 | 54.4 | ExploitBench |
| Jul 9, 2026 | GPT-5.6 Luna | 33.2 | ExploitBench |
| Jul 9, 2026 | GPT-5.6 Terra | 52.9 | ExploitBench |
26 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 |
|---|---|---|---|---|
| Nov 5, 2026 | GPT-6 Cyber system card | 100 | ExploitBench, ACE-normalized mean capability score | Cyber risk |
| Oct 29, 2026 | GPT-6 Luna system card addendum | 46.2 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| Oct 26, 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 |
| Oct 23, 2026 | | 28.7 | ExploitBench capability coverage with AutoNudge, Muse Spark 1.3 (max) (Table 1, Table 3). Mean capability score 4.16 | Cyber risk |
| Oct 13, 2026 | | 87 | ExploitBench, autonudge, 300-turn limit, best-of-three union, capabilities achieved 87% of 656 (7.1.1) | Cyber risk |
| Oct 12, 2026 | | 58.9 | ExploitBench, Muse Wonder Full, metric not named (Cyber Capability Evaluations table, section 4) | Cyber risk |
| Oct 7, 2026 | GPT-6 Sol system card addendum | 82.6 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| Oct 7, 2026 | GPT-6 Luna system card addendum | 44.7 | ExploitBench, maximum reasoning effort (8.1.2.1) | Cyber risk |
| Oct 7, 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 |
| Sep 29, 2026 | GPT-6.1 Sol system card addendum | 99.7 | ExploitBench, maximum reasoning effort (9.1.2.1) | Cyber risk |
| Sep 28, 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 |
| Sep 22, 2026 | MiMo-V2.6 Pro technical report | 47.9 | ExploitBench (Table 3) | Cyber risk |
| Sep 22, 2026 | GPT-6 Sol system card | 81.7 | ExploitBench, capability coverage at maximum reasoning effort (A.8.1.2.1, Figure 79) | Cyber risk |
| Sep 22, 2026 | GPT-6 Luna system card | 43.4 | ExploitBench, capability coverage at maximum reasoning effort (A.8.1.2.1, Figure 79) | Cyber risk |
| Sep 22, 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 |
| Sep 3, 2026 | GPT-6 Astra system card | 100 | ExploitBench, Cap Percent, all reasoning efforts tested (10.1.2.1) | Cyber risk |
| Jul 27, 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 |
| Jul 24, 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 |
| Jul 9, 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 | |
| Jul 9, 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 | |
| Jul 9, 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 | |
| Jun 30, 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 |
| Jun 25, 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 | |
| Jun 25, 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 | |
| Jun 25, 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 | |
| May 28, 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 |
More in Cybersecurity
- ExploitGym In use 869 real-vulnerability exploit-development tasks (userspace, V8, Linux kernel). Success means capturing a flag through the intended vulnerability. GPT-6 Astra 42.4%
- CyberBench v1.1 (Vals) In use Agents work on OSS-Fuzz memory-safety bugs from ARVO images in two tracks. They write a proof-of-concept input that crashes the target, or patch the source so the crash stops and tests still pass.
Muse Wonder 80.4%
- SRE-Bench In use Software reverse engineering without source: 262 binary instances from 19 private programs, each with six graded objectives. GPT-6 Astra 56.9%
- CyScenarioBench In use Scenario-based long-horizon security challenges in sandboxes, organized in task, scenario and campaign tiers. Evaluation set kept private.
Gemini 4 Argon 100%
- ExploitBench - Internal Port In use Agents develop working exploits for recently disclosed vulnerabilities to limit contamination. Scored as arbitrary code execution success rate. GPT-6 Cyber 42%
- MiMo Cyber Bench In use Agents do in-house cybersecurity tasks that add to public vulnerability reproduction and exploitation benchmarks. The report gives no task count or grading details. MiMo-V2.6 Pro 81.7%