arXiv:2606.11416v1 Announce Type: cross 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
The proliferation of LLMs creates a pressing need to evaluate their security patching capabilities, especially in sensitive domains like multi-party computation, leading to the development of specialized benchmarks.
This development highlights the critical gap in existing LLM evaluation benchmarks for secure computing, signaling a crucial step towards robust, trustworthy AI in cybersecurity.
The focus shifts from general-purpose LLM code repair benchmarks to specialized, security-aware evaluations tailored for complex cryptographic applications.
- · Cybersecurity researchers
- · Developers of secure multi-party computation systems
- · AI model developers specializing in code security
- · General-purpose LLM code repair benchmarks
- · Organizations relying solely on unverified LLM code for security-critical applic
Improved security and reliability of LLM-generated code patches for multi-party computation environments.
Increased adoption of security-aware LLMs in critical infrastructure and privacy-preserving technologies.
A potential renaissance in cryptographic engineering, accelerated by AI-assisted secure code development and patching.
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Read at arXiv cs.AI