Mike Vizard summarizes SonarSource’s research into AI-generated code, highlighting both the strengths and serious security pitfalls of relying on LLMs such as GPT-4o, Claude Sonnet 4, and others.

SonarSource Research Highlights Security Risks in LLM-Generated Code

Author: Mike Vizard

Overview

A recent SonarSource analysis warns DevOps teams about several key risks when depending on large language models (LLMs) to write code. Although tools like GPT-4o, Claude Sonnet, and Llama-3.2 can generate correct, working code with a high degree of success, their output frequently contains severe vulnerabilities and poor quality patterns that may introduce long-term issues.

Key Points

  • LLM Evaluation: SonarSource evaluated LLMs including GPT-4o, Claude Sonnet 4 and 3.7, Llama-3.2-vision:90b, and OpenCoder-8B over 4,400 Java assignments.
  • Functionality vs Risk: While LLMs such as Claude Sonnet 4 scored 95.57% on HumanEval, indicating strong coding ability, this performance comes with an increased rate of high-severity bugs.
  • Security Weaknesses Detected: Common issues across LLMs included hard-coded credentials and path-traversal vulnerabilities. Some models, like Llama-3.2-vision:90b, showed over 70% of vulnerabilities classified as ‘blocker’ level.
  • Tech Debt and Maintainability: Over 90% of the issues found were “code smells,” referencing poor code structure—like dead or redundant code—which leads to technical debt.
  • Variation Among LLMs: Each model demonstrated its own quirks or “coding personality,” with different propensities for code quality and safety issues.
  • Impact on DevOps: The study suggests developers must rigorously review any code generated by LLMs and consider the trade-offs between productivity and increased risk.

Developer Takeaways

  • Review Required: Do not deploy LLM-generated code without manual and/or automated security checks.
  • Understand LLM Bias: Different LLMs may require different review practices due to their unique characteristics.
  • Technical Debt Awareness: Watch for “code smells” and pursue code refactoring to maintain long-term health of your codebase.
  • AI in DevOps: While LLMs boost productivity, unexamined adoption may increase future costs due to security flaws and maintainability problems.

Resources

Quote from the article:

“There is no doubt that developers will be more productive, but some of those gains will come at a cost tomorrow that organizations may not realize they are incurring today.”

Conclusion

SonarSource’s findings serve as a caution for teams eager to adopt LLM-powered development. Security and quality reviews remain critical, regardless of model sophistication.

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