Benchmark Almanac

ExploitBench

GPT-6 Astra 100% leads since Sep 2026 In use

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.

Abstract of ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents (arXiv:2605.14153, CC0)
Made by Carnegie Mellon (Lee & Brumley) Released May 2026 Finding and exploiting real bugs All tasks public Last in a launch post Oct 2026 Last model added Oct 2026 Scores refreshed 2 Nov 2026
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.
19
Models scored in lab cards
5
Launch posts list it, from 4 labs
25
System cards report it, from 6 labs

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.

  1. 1 GPT-6 Astra 100%
  2. 2 GPT-6.1 Sol 99.7%
  3. 3 GPT-6.1 Terra 97.1%
  4. 4 Claude Opus 5.5 91%
  5. 5 Gemini 4 Argon 87%
  6. 6 GPT-6 Sol 82.6%
  7. 7 Claude Sonnet 5.5 80%
  8. 8 Kimi K3.1 Pro 74.7%
  9. 9 Claude Opus 5 70%
  10. 10 Muse Wonder Full 58.9%
  11. 11 GLM-5.3 54.4%
  12. 12 GPT-5.6 Terra 52.9%
  13. 13 Claude Haiku 5.5 49%
  14. 14 MiMo-V2.6 Pro 47.9%
  15. 15 GPT-6 Luna 46.2%
  16. 16 GPT-5.6 Luna 33.2%
  17. 17 Kimi K3 32%
  18. 18 Claude Sonnet 5 31%
  19. 19 Muse Spark 1.3 28.7%
#ModelScoreSettingDateSource
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 Gemini 4 Argon 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 Muse Wonder Full 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 Muse Spark 1.3 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.

Anthropic OpenAI
Own leaderboard Not tested since May 2026
Epoch AI runs Not tested since May 2026
Lab claims Passed 85% in Sep 2026, by GPT-6 Astra

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.

On the benchmark’s own leaderboard Run or collected by Epoch AI Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2026 2027 Near the ceiling (85%) GPT-6 Astra Claude Mythos Preview Gemini 3.1 Pro Claude Haiku 4.5 0% 20% 40% 60% 80% 100% 2026 2027 Near the ceiling (85%) GPT-6 Astra Claude Mythos Preview

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.

OpenAI
Z.ai
Xiaomi Corp
Moonshot AI
DateLaunch postScoreAs 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.

DateDocumentScoreAs the document writes itSection
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 Muse Spark 1.3 system card 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 Gemini 4 Argon system card 87 ExploitBench, autonudge, 300-turn limit, best-of-three union, capabilities achieved 87% of 656 (7.1.1) Cyber risk
12 Oct 2026 Muse Wonder Full model card 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

More in Cybersecurity

All 111 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%
  • CWE-bench In use Agents audit a repository and write a patch for a security flaw, without being told where the bug is. Covers six programming languages. Gemini 4 Carbon 71%
  • Real-world vulnerability discovery In use Agents search popular open-source codebases to rediscover about 1,200 recent confirmed vulnerabilities across many languages. Reports share discovered. Gemini 4 Argon 86%
  • Artificial Analysis Cyber Index In use Agents do enterprise cyber defense tasks from three benchmarks, CWE-Bench-AA, DeepsecBench-AA and CyberGym-E2E-AA, combined into one success rate. Mistral Large 4 (Le Chonk, open weights) 51%
  • Big Sleep Evaluation (Google) In use Agents search large real codebases such as Chrome and Safari for critical, hard to find vulnerabilities. Scored by pass@1 success rate, run without safety guardrails. Gemini 3.5 Flash Cyber 72%
  • Chrome Production Commit Scanning (Google) In use Models scan commits in Chrome's production pipeline for vulnerabilities that were never publicly disclosed. Scored by pass@1 success rate. Gemini 3.5 Flash Cyber 72%