Sonar's report assesses LLM-generated code across various models, revealing strengths in syntax correctness and coding capabilities. The models showed proficiency in generating boilerplate code and translating snippets between programming languages. However, they also presented critical vulnerabilities such as hard-coded credentials and path-traversal injections, with varying prevalence among models. For instance, Llama-3.2-vision:90b had over 70% vulnerabilities rated as 'blocker' severity. The report notes that enhanced performance in models can come with increased security risks, highlighting the need for careful review in coding contexts.
The analysis found that all evaluated LLMs produced a high percentage of vulnerabilities with high severity ratings, indicating that improved performance often correlates with increased risk.
Despite high capabilities to generate valid and executable code, models like Claude Sonnet 4 and GPT-4o still introduced critical security flaws commonly found in LLM-generated outputs.
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