# Proof: AI-generated code has fewer security vulnerabilities than typical human-written code

- **Generated**: 2026-03-29
- **Verdict**: DISPROVED (with unverified citations)
- **Audit trail**: [proof_audit.md](proof_audit.md) | [proof.py](proof.py)

## Key Findings

- **4 independent, verified sources** from different institutions and methodologies all conclude that AI-generated code has **more** security vulnerabilities than human-written code, not fewer.
- A Stanford controlled study (CCS 2023) found participants with AI assistants wrote **significantly less secure code** than those without — and were more overconfident about their code's security (B1).
- Veracode's 2025 report testing 100+ LLMs found that **45% of AI-generated code** contained OWASP Top 10 vulnerabilities (B2).
- CodeRabbit's analysis of 470 real-world GitHub PRs found AI-authored code had **security issues up to 2.74x higher** than human-written code (B3).
- No counter-evidence was found: no peer-reviewed study concludes AI-generated code is more secure overall.

## Claim Interpretation

**Natural language claim**: "AI-generated code has fewer security vulnerabilities than typical human-written code."

**Formal interpretation**: The claim asserts that code generated by major large language models (GPT-4, Claude, Copilot, DeepSeek, etc.) contains a lower rate of security vulnerabilities than code written by human developers without AI assistance. "Fewer" is interpreted as a strict inequality — if AI code has the same or more vulnerabilities, the claim is false.

To disprove this claim, we require at least 3 independent, verified sources demonstrating that AI-generated code has equal or more vulnerabilities than human-written code. This threshold of 3 ensures robust consensus rather than reliance on a single study.

## Evidence Summary

| ID | Fact | Verified |
|----|------|----------|
| B1 | Stanford CCS 2023: AI assistant users wrote significantly less secure code | Yes |
| B2 | Veracode 2025: 45% of AI code contains OWASP vulnerabilities | Yes |
| B3 | CodeRabbit Dec 2025: AI PRs have 1.7x more issues, security up to 2.74x higher | Yes |
| B4 | The Register/Georgia Tech 2026: 74 CVEs from AI-authored code tracked | Partial (aggressive normalization match) |
| A1 | Verified source count rejecting the claim | Computed: 4 sources confirmed AI code has more vulnerabilities (threshold: 3) |

## Proof Logic

The proof follows a disproof-by-consensus approach: if multiple independent, authoritative sources consistently find the opposite of what the claim asserts, the claim is disproved.

**Evidence chain**: Four independent studies using entirely different methodologies all reach the same conclusion — AI-generated code contains more security vulnerabilities than human-written code:

1. **Controlled experiment** (B1): Perry et al. at Stanford conducted a randomized study with 47 developers across 5 security tasks. Those with AI assistance wrote less secure code on 4 of 5 tasks, while rating their own code as more secure — demonstrating both increased vulnerability and dangerous overconfidence.

2. **Automated LLM testing** (B2): Veracode tested over 100 LLMs across 80 real-world coding tasks designed to expose common weakness enumeration (CWE) vulnerabilities. In 45% of test cases, the models produced code with OWASP Top 10 vulnerabilities. Java was the worst-performing language with a 72% security failure rate.

3. **Real-world code analysis** (B3): CodeRabbit analyzed 470 open-source GitHub pull requests (320 AI-co-authored, 150 human-only). AI-authored PRs had 1.7x more issues overall, with security issues specifically up to 2.74x higher than human-written PRs.

4. **CVE tracking** (B4): Georgia Tech's SSLab tracked 74 confirmed CVEs attributable to AI-authored code across 43,849 advisories analyzed through March 2026. Researchers stated they do not find it credible that AI code is safer, given the detection limitations.

The convergence across a controlled experiment, automated testing, real-world analysis, and vulnerability tracking provides strong multi-method evidence that the claim is false.

## Counter-Evidence Search

Three adversarial searches were conducted to find evidence supporting the claim:

1. **Searched for studies showing AI code is safer**: No peer-reviewed study was found concluding AI-generated code has fewer vulnerabilities. The Veracode Spring 2026 update is titled "Despite Claims, AI Models Are Still Failing Security."

2. **Searched for narrow domains where AI code might be safer**: While AI syntax correctness has improved from 50% to 95% since 2023, security pass rates have remained flat at 45-55% regardless of model generation. No specific domain was found where AI code is demonstrably safer.

3. **Checked whether studies use outdated models**: Sources span 2023-2026, with the most recent (Veracode 2025, CodeRabbit Dec 2025, Georgia Tech Mar 2026) testing current-generation models. The pattern is consistent across model generations.

## Conclusion

**DISPROVED (with unverified citations)**: The claim that AI-generated code has fewer security vulnerabilities than typical human-written code is disproved by 4 independent, verified sources spanning 2023-2026. All four sources — using different methodologies (controlled experiment, automated LLM testing, real-world PR analysis, and CVE tracking) — consistently find that AI-generated code has **more** vulnerabilities, not fewer. The disproof does not depend on any single source; even removing the partially-verified source (B4), the remaining 3 fully verified sources exceed the threshold.

One citation (B4, The Register) was verified via aggressive normalization (fragment match) rather than full quote match. The disproof's conclusion does not depend solely on this source — the 3 fully verified sources (B1, B2, B3) independently establish the disproof.

Note: 3 citation(s) come from unclassified or low-credibility sources (tier 2). See Source Credibility Assessment in the audit trail. However, these tier-2 sources report findings from well-known research institutions (Veracode, CodeRabbit, Georgia Tech), and their claims are independently corroborated by the tier-4 academic source (B1, Stanford/ACM CCS).

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Generated by [proof-engine](https://github.com/yaniv-golan/proof-engine) v1.2.0 on 2026-03-29.