ExploitGym leaderboard
As of November 8, 2026, GPT-6 Astra (OpenAI) leads ExploitGym at 42.4%, ahead of GPT-5.6 Sol at 30.3% and Kimi K3.1 Pro at 20.7%. 8 systems ranked. Every score here is published by the model's own lab; each row links to the page it comes from.
What is ExploitGym? ExploitGym measures whether an AI agent can turn a known software vulnerability into a working exploit, across userspace programs, Google's V8 engine and the Linux kernel. Made by UC Berkeley (Berkeley RDI) with academic and industry co-authors, May 2026.
Top models on ExploitGym, November 2026
| # | Model | Score | Price / 1M | Context | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra OpenAI | without production safeguards | $10 / $50 | 1.05M | Vendor-reported OpenAI |
| 2 | GPT-5.6 Sol OpenAI | without production safeguards | $4 / $20 | 1.05M | Vendor-reported OpenAI |
| 3 | Kimi K3.1 Pro Moonshot (Kimi) | max effort | $2 / $10 | 1M | Vendor-reported Moonshot AI |
| 4 | MiMo-V2.6-Pro Xiaomi MiMo | as reported | $0.435 / $0.87 | 1M | Vendor-reported Xiaomi |
| 5 | DeepSeek V4.1 Flash DeepSeek | pass@1 | $0.3 / $1.2 | 1M | Vendor-reported DeepSeek |
| 6 | MiMo-V2.6-Flash Xiaomi MiMo | as reported | $0.14 / $0.28 | 1M | Vendor-reported Xiaomi |
| 7 | DeepSeek V4 Pro DeepSeek | pass@1 | $1.32 / $3.96 | 1M | Vendor-reported DeepSeek |
| 8 | DeepSeek V4 Flash Deprecated DeepSeek | pass@1 | $0.44 / $1.32 | 1M | Vendor-reported DeepSeek |
ExploitGym: score against price
Can you trust ExploitGym scores?
No independent leaderboard publishes ExploitGym results for these models yet, so every number on this page is the lab's own report of its own model. Labs choose the harness, the effort level and the subset they report, and they rarely publish failures. Read the setting under each score before comparing two rows.
- Each instance can run with or without standard security protections, and the authors show protections change success rates. A score means little without the configuration.
- The task is dual-use: the authors frame strong scores as a growing cybersecurity risk, not only a capability.
- There is no official public leaderboard; every score here is a lab's own report.
How ExploitGym works
The task
The agent gets vulnerable source code, an input that triggers the bug and a containerised runtime, and has to extend that input into an exploit that reaches unauthorised file access or code execution.
The score
An instance is solved when the exploit captures a secret flag that legitimate use cannot reach. Scores are the share of instances solved.
Test set
898 instances from real vulnerabilities, as published by the authors.
Made by
UC Berkeley (Berkeley RDI) with academic and industry co-authors, first published May 2026.
Official sources
- github.com/sunblaze-ucb/exploitgym
- Paper: arxiv.org/abs/2605.11086
- Definition read on 2026-11-08
Sources and freshness
8 published scores from 4 sources. Each was read on its source page; the oldest reading on this page is from November 8, 2026. Prices beside each score come from our model price index and move when a vendor reprices.
- Vendor-reported DeepSeek
- Vendor-reported OpenAI
- Vendor-reported Xiaomi
- Vendor-reported Moonshot AI
ExploitGym FAQ
Answers derived from the 8 published scores on this page and the current price index.