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MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation

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arXiv:2606. 11416v1 Announce Type: new Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, and directly transplanting general-purpose benchmarks such as SWE-bench fails on three structural fronts: (i) MPC repositories are dominated by generic Python infrastructure rather than cryptographic logic; (ii) high-value MPC fixes lack the standardized tests rigid extraction pipelines require; and (iii) standard fail-to-pass evaluation is insufficient for code that must also be cryptographically safe.

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Authors: Yukuan Zhang, Mengxin Zheng, Qian Lou
Released: 2026-06-11 (28d ago)
Topic: Related to current project
Relation to my work: Read next because MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, text, rect, soft, eval, line, extraction. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.11416

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