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2025 State of Software Security Public Sector Snapshot 2025 GenAI Code Security Report AI is generating code Our research shows it’s generating risk The 2025 GenAI Code Security Report analyzes the security of code generated by over 100 large language models across Java, JavaScript, Python, and C# The results are clear: AI-generated code often isn’t secure, and the risk is likely already in your stack
Can Large Language Models Find And Fix Vulnerable Software? In this study, we evaluated the capability of Large Language Models (LLMs), particularly OpenAI's GPT-4, in detecting software vulnerabilities, comparing their performance against traditional static code analyzers like Snyk and Fortify Our analysis covered numerous repositories, including those from NASA and the Department of Defense GPT-4 identified approximately four times the
How secure is AI-generated code: a large-scale comparison of large . . . As the usage of AI-based tools in coding continues to expand, understanding their potential to introduce software vulnerabilities becomes increasingly important Given that LLMs are trained on data freely available on the internet, including potentially vulnerable code, there is a high risk that AI tools could replicate the same patterns
Survey reveals nearly 50% of organizations knowingly push vulnerable . . . New research finds nearly half of organizations regularly and knowingly ship vulnerable code despite using application security tools Among the top reasons cited for pushing vulnerable code were pressure to meet release deadlines (54 percent) and finding vulnerabilities too late in the software development lifecycle (45 percent), according to the Veracode and Enterprise Strategy Group (ESG