An Empirical Analysis of Static Analysis Methods for Detection and Mitigation of Code Library Hallucinations
Factual evidence
What the source reports
Research finds LLMs hallucinate non-existent library features in 8.1-40% of generated code; evaluates static analysis for detection and mitigation.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 9 November 2026
- Collected by OneBench
- 11 Apr 2026, 10:17 UK
Stored source excerpt
arXiv:2604.07755v1 Announce Type: new Abstract: Despite extensive research, Large Language Models continue to hallucinate when generating code, particularly when using libraries. On NL-to-code benchmarks that…
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OneBench interpretation
Institutional assessment
So what
LLM code generation hallucinating non-existent library features poses a tangible model risk for G-SIBs automating development workflows, requiring robust static analysis integration.
Do what
This research confirms the need to integrate advanced static analysis tools with any LLM-based code generation systems to mitigate critical code quality and security risks before deployment.