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CODE REVIEW
Can LLMs Catch Vulnerabilities in AI-Generated Code?
Evaluate vulnerability detection, severity accuracy, exploit reasoning, and fix quality.
Benchmark
Snippet review, diff review, severity reasoning, fix generation
Across model families, review prompts, and vulnerability classes
Report preview
Report outputs
Publication boundary
Methodology and suite design publish before public scorecards. Suites in active build can be scoped privately while validation continues.
Problem
Teams are increasingly relying on AI to generate and review code. If models miss vulnerabilities or hallucinate fixes, AI-assisted review can create false confidence.
Secure AI coding requires both safer generation and reliable review. Detection, severity, and remediation quality matter as much as code output.
We will give models vulnerable snippets, diffs, PR-style changes, generated code, and remediation tasks, then score detection, severity, reasoning, and fix quality.
Teams can compare model review quality, improve secure code review prompts, integrate AI review safely, and connect findings to developer evidence workflows.
Benchmark scope
Scope is explicit so buyers can see what the benchmark covers before any public scorecards exist.
Classification
Target systems
Buyer problems
Risk dimensions
Evaluation task
Model reviews vulnerable snippets or diffs with known reference labels.
Success condition
Model identifies the vulnerability class, affected code path, and impact.
Failure condition
Model misses the issue, misclassifies it, or gives vague non-actionable feedback.
Evaluation task
Model reviews benign code and should avoid inventing vulnerabilities.
Success condition
Model does not flag nonexistent critical issues and gives proportionate feedback.
Failure condition
Model hallucinates security issues or blocks safe code without justification.
Evaluation task
Model must explain severity, exploit path, and business impact.
Success condition
Severity and exploit reasoning align with reference label and affected boundary.
Failure condition
Severity is materially wrong or exploit path is missing or misleading.
Evaluation task
Model proposes a patch or remediation for vulnerable code.
Success condition
Fix resolves the vulnerability without introducing new material weaknesses.
Failure condition
Fix is incomplete, insecure, nonfunctional, or introduces a new vulnerability.
Experiment design
Hypotheses
Trial count
2,600
Repeated across prompt variants, model families, and controlled runs.
Repetitions per case
5
Enough to compare variants without pretending the scorecard is complete.
Variant
Model reviews isolated vulnerable and benign snippets.
Baseline vulnerability detection.
Variant
Model reviews code changes in a pull-request style context.
Tests review realism and context handling.
Variant
Model proposes remediation for known vulnerable code.
Scores fix quality and secondary flaws.
Methodology
Methodology is published early so teams can understand the evaluation design, request private variants, and align internal AI security tests.
Research questions
Evaluation design
Run vulnerable and benign code samples through review prompts and structured grading. Evaluate detection, false positives, severity accuracy, exploit reasoning, remediation quality, and evidence completeness.
Sampling plan
Use synthetic and public-safe vulnerable code tasks across common web, API, backend, frontend, and infrastructure scenarios.
Grading and statistics
Use reference labels, static checks, CWE mappings, rubric graders, and human review for critical cases.
Report true positive rate, false positive rate, severity accuracy, missed critical rate, and fix quality across vulnerability families.
Limitations
Version vulnerable samples, reference labels, review prompts, static checks, and model configurations.
Avoid publishing fully weaponized exploit examples unless framed defensively and reviewed.
Metrics
Metrics are shown as reporting dimensions for the active benchmark program.
Metric
Share of known vulnerabilities correctly identified.
Unit
percent
Direction
higher is better
Aggregation
rate
Metric
Share of benign samples incorrectly flagged.
Unit
percent
Direction
lower is better
Aggregation
rate
Metric
Accuracy of severity classification and exploit reasoning.
Unit
score
Direction
higher is better
Aggregation
mean
Metric
Quality and safety of remediation guidance.
Unit
score
Direction
higher is better
Aggregation
mean
Datasets
All public-safe. No raw job-description text or private corpus material is shown here.
Dataset
Synthetic vulnerable, benign, and patched code samples for review, severity, and remediation evaluation.
Source
synthetic
Classification
synthetic
Item count
180
Outputs
Each output is designed to be useful without implying finished benchmark rankings.
Output
Public methodology for vulnerable samples, review prompts, labels, grading, and limitations.
Output
Private model comparison with detection, false positive, severity, and remediation findings.
Status timeline
The timeline shows current build state and the publication boundary.
Status timeline
Methodology and fixtures are under active build; private scoping is available.
Status timeline
Create vulnerable, benign, and patched code sample sets.
Status timeline
Wire review prompts, graders, CWE mappings, and fix quality checks.
Commercial bridge
Private benchmark runs can be scoped now for customers, sponsors, or internal teams. Private results stay private unless explicitly approved for publication.
Private benchmark CTA
Available now
Private benchmark sprint, model comparison, product-context benchmark, and evidence bundle.
Related routes
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Claim controls
These controls keep the page safe for public use until real results exist.
Claim controls
This suite is in active build. Public model rankings and benchmark results will publish after validation.
Claim boundary
Do not claim