Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code
arXiv SecurityArchived Apr 08, 2026✓ Full text saved
arXiv:2604.05292v1 Announce Type: new Abstract: AI coding assistants are now used to generate production code in security-sensitive domains, yet the exploitability of their outputs remains unquantified. We address this gap with Broken by Default: a formal verification study of 3,500 code artifacts generated by seven frontier LLMs across 500 security-critical prompts (five CWE categories, 100 prompts each). Each artifact is subjected to the Z3 SMT solver via the COBALT analysis pipeline, producin
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Computer Science > Cryptography and Security
[Submitted on 7 Apr 2026]
Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code
Dominik Blain, Maxime Noiseux
AI coding assistants are now used to generate production code in
security-sensitive domains, yet the exploitability of their outputs remains
unquantified. We address this gap with Broken by Default: a formal
verification study of 3,500 code artifacts generated by seven frontier LLMs
across 500 security-critical prompts (five CWE categories, 100 prompts each).
Each artifact is subjected to the Z3 SMT solver via the COBALT analysis
pipeline, producing mathematical satisfiability witnesses rather than
pattern-based heuristics. Across all models, 55.8% of artifacts contain at
least one COBALT-identified vulnerability; of these, 1,055 are formally proven
via Z3 satisfiability witnesses. GPT-4o leads at 62.4% (grade F); Gemini 2.5
Flash performs best at 48.4% (grade D). No model achieves a grade better than
D. Six of seven representative findings are confirmed with runtime crashes
under GCC AddressSanitizer. Three auxiliary experiments show: (1) explicit
security instructions reduce the mean rate by only 4 points; (2) six industry
tools combined miss 97.8% of Z3-proven findings; and (3) models identify their
own vulnerable outputs 78.7% of the time in review mode yet generate them at
55.8% by default.
Comments: 8 pages, 6 tables, empirical study
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2604.05292 [cs.CR]
(or arXiv:2604.05292v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2604.05292
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Submission history
From: Dominik Blain [view email]
[v1] Tue, 7 Apr 2026 00:55:42 UTC (13 KB)
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