Relying on gameable benchmarks for LLM vulnerability discovery
Avoid LLM benchmarks that are susceptible to data contamination or score recall against unknowable sets.
Employ benchmarks that use a closed, deterministic ground truth, such as input prediction tasks derived from coverage-guided fuzzing on real-world programs. These benchmarks should decompose the discovery process to localize agent failures.
Using existing benchmarks for LLM vulnerability discovery that are prone to data contamination, score recall against an unknowable vulnerability set, often rely on synthetic bugs, and report a single end-to-end verdict.
Gameable benchmarks provide an unreliable measure of an LLM's true capabilities, leading to a false sense of security or overestimation of performance. They fail to accurately assess the model's ability to find genuine defects in real-world code.
Do not use LLM vulnerability discovery benchmarks that can be easily gamed through data contamination or that lack a clear, verifiable ground truth. Opt for methods that use deterministic inputs and decompose the evaluation process.
Benchmarks with a closed, deterministic ground truth, like input prediction for specific code branches, offer a more accurate and reliable assessment of an LLM's capabilities, preventing inflated scores due to superficial learning.
- SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction ↗arXiv cs.AI · arXiv cs.AI · Sep 5, 2026 · paraphrased
First seen on Subvolts Sep 5, 2026. Updated Sep 11, 2026. Spotted a problem? Tell us.
- SWE-Test: Benchmarking LLM Vulnerability Discovery via Input PredictionAnthropic · arXiv cs.AI · Sep 5, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
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- Any current model. It was seen with in the sources.
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- arXiv cs.AI (arXiv cs.AI). Subvolts collected, normalized and credited it; the link above opens the original.