{"data":{"id":"fa55f58c-235d-44f2-8baa-01c0227943b9","title":"Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis","summary":"This measurement study evaluates code generated by five widely used LLM families across Python, Java, C++, and C, using 200 manually curated tasks. Generated code was assessed for compilation success, semantic correctness with 4,000 per-program unit test files, and quality and security with SonarQube and CodeQL. Python and Java showed higher compilation and correctness rates and fewer security findings than C and C++, which showed more memory safety issues, hard-coded secrets, and cryptographic misuses.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11429547","publishedAt":"2026-03-10T13:16:32.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["LLM-generated code","large language models"],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-03-10T13:16:32.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","confidentiality"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}