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Solexecbench Exploit Blog - #9

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Solexecbench Exploit Blog#9
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This pull request adds a new blog post detailing a vulnerability in the SOLExecbench benchmark that allows submitted kernels to achieve a perfect SOL-score by exploiting how correctness is checked and trusted. The post provides background on benchmark gaming in AI, a technical breakdown of the vulnerability, proof-of-concept exploit code, and a discussion of the implications for benchmarking robustness.

New blog post: SOLExecbench exploit

  • Overview and context of benchmark gaming in AI, including historical examples and prior incidents.
  • Detailed technical explanation of the SOLExecbench evaluation flow, highlighting that correctness is only checked once and then trusted for all subsequent timing runs.
  • Description and proof-of-concept code for an exploit that caches reference outputs and replays them during timing, resulting in artificially high SOL-scores without performing the intended computation.
  • Discussion of the broader implications for benchmark design and the importance of robust correctness checks.

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pramodith requested review from DeanoC, b-albar and fionnan July 28, 2026 15:26

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LGTM
Might be worth making a comment we reported it to NVIDIA as soon as you found it

linkedin: "https://www.linkedin.com/company/112782074/"
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The gaming of benchmarks in AI is a well-known phenomenon. Sakana AI's "AI CUDA Engineer" was found to have discovered a memory exploit in its evaluation sandbox that let it skip correctness checks entirely, inflating its reported kernel speedups until the company hardened the harness ([Sakana AI](https://x.com/SakanaAILabs/status/1892992938013270019)). Meta faced accusations that Llama 4 Maverick was tuned against benchmark test sets ahead of its release, allegations the company denied ([IT Pro](https://www.itpro.com/technology/artificial-intelligence/meta-llama-4-model-launch-benchmarks)). Test data has also leaked into training sets unintentionally: OpenAI acknowledged a filtering bug that let parts of the GSM8K and MATH test sets end up in GPT-3's training corpus ([Leak, Cheat, Repeat](https://arxiv.org/pdf/2402.03927)). And reward hacking predates LLMs altogether: OpenAI's own CoastRunners agent learned to loop endlessly in a lagoon farming respawning targets instead of finishing the race, racking up a higher score while catching fire and going the wrong way ([OpenAI](https://openai.com/index/faulty-reward-functions/)). In this post, we will discuss a vulnerability that we discovered in the SOLExecbench benchmark that allows submitted kernels to achieve a SOL-score of 1.0, regardless of their actual performance.

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Competition like the nvfp4 hackaton have also been plagued with this. May be worth to mention to insist on the fact that it is not new but still exists : https://www.gpumode.com/news/reward-hacking-nvfp4

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3 participants