New Research on Proof of Concept Reveals Security Challenges in Coding with Large Language Models
Security researchers have identified significant vulnerabilities in code generated by Large Language Models (LLMs), highlighting how “vibe coding” with AI assistants can lead to critical security flaws in production applications. A recent study indicates that LLM-generated code often prioritises functionality over security, creating exploitable attack vectors that can be accessed with simple curl commands. Key findings reveal that LLM-generated code inherits insecure patterns, trading security for functionality, and that exposed endpoints facilitate easy exploits. Human oversight, including threat modelling, reviews, and scans, is essential to mitigate these risks.
Himanshu Anand reports that the core issue arises from LLMs being trained on internet-scraped data, where most code examples focus on functionality rather than security best practices. When developers heavily rely on AI-generated code without adequate security reviews, these insecure patterns can proliferate into production systems at scale. Research shows that LLMs lack an understanding of business risk and the contextual awareness necessary for effective threat modelling. A concerning case involved a JavaScript application hosted on Railway.com, where the entire email API infrastructure was exposed client-side. The research underscores the need for organisations to implement proper threat modelling, security reviews, and defence-in-depth strategies, ensuring that AI-generated code is not deployed directly to production without thorough human oversight.
Categories: Vulnerabilities in LLM-Generated Code, Security Oversight and Best Practices, Risks of Insecure Training Data
Tags: Vulnerabilities, Large Language Models, Security Flaws, Attack Vectors, Insecure Patterns, Human Oversight, Threat Modeling, Proof-of-Concept, Security Reviews, Automated Scanning