As the AI-code assistant tools become widespread, automatic assessment of the correctness of the generated code becomes a significant challenge. Code LLMs are prone to hallucinations, which may lead to code that does not solve a required problem, or even to code with severe security vulnerabilities. In this paper, we introduce CodeTD – the first approach to pre-execution assessment of code correctness based on topological data analysis (TDA) of Code LLM attention maps. Our method quantifies prompt-generation mismatch from topological patterns of attention maps. We carry out experiments with common benchmarks (HumanEval, MBPP, BigCodeBench, MultiPL-E), 5 programming languages and 10 Code LLMs of size up to 34B parameters. The experimental results show that the proposed method is better than recent baselines. Moreover, CodeTD is transferable between coding benchmarks.