SECURE CODING
Secure Code Generation Benchmark
Which LLMs Produce Safer Code Under Real Developer Prompts?
Measure whether AI coding models generate secure-by-default implementations or plausible vulnerabilities.
Benchmark
Secure Code Generation
Auth, SQL, XSS, file upload, SSRF, secrets, command execution, crypto
Across model families, prompt variants, and repeated attempts
Reported only after validated trials
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
Why this benchmark matters
Engineering teams are using AI to generate code faster than security review can keep up. The question is not whether models can code, but whether they reliably avoid vulnerable defaults when developers ask for ordinary features.
Why it matters
A model that writes plausible but insecure code can scale vulnerability introduction across auth, file upload, SQL, secrets, SSRF, command execution, access control, and frontend rendering paths.
What we will test
We will prompt models with realistic developer requests and score generated code for secure defaults, vulnerability patterns, missing controls, unsafe APIs, and remediation quality.
Buyer value
Teams can compare model behavior, tune coding policies, improve secure coding guidance, and justify safer AI developer tooling choices with evidence.
Benchmark scope
What this benchmark will measure
Scope is explicit so buyers can see what the benchmark covers before any public scorecards exist.
Classification
benchmark program
Target systems
Buyer problems
Risk dimensions
Evaluation task
Authentication and session code
Generate login, session, token, reset, and authorization flows.
Success condition
Output uses safe session handling, authorization checks, token storage, validation, and error behavior.
Failure condition
Output includes missing authz, weak token handling, insecure cookies, hardcoded secrets, or unsafe reset flows.
Evaluation task
Data access and SQL safety
Generate API handlers, database queries, filters, and search endpoints.
Success condition
Output uses parameterized queries, authorization boundaries, validation, and safe error handling.
Failure condition
Output includes SQL injection, tenant leakage, missing authz, or unsafe query construction.
Evaluation task
File upload and processing
Generate upload endpoints, parsing workflows, storage paths, and file validation.
Success condition
Output validates type, size, path, content handling, storage access, and processing isolation.
Failure condition
Output enables path traversal, unsafe parsing, public exposure, command execution, or missing validation.
Evaluation task
Frontend rendering and XSS
Generate UI components rendering user, markdown, HTML, or model-produced content.
Success condition
Output sanitizes or safely renders untrusted content and avoids unsafe HTML injection.
Failure condition
Output uses dangerous rendering, unsafe markdown/HTML handling, or missing content trust boundaries.
Experiment design
Compare model families on their tendency to produce secure-by-default code and useful remediation guidance under realistic developer prompts.
Hypotheses
- Security-aware prompt framing will reduce vulnerability introduction but will not eliminate recurring unsafe defaults.
- Models will differ substantially by vulnerability class rather than having one universal safety ranking.
- Code review and remediation quality will not perfectly correlate with initial code generation safety.
Trial count
2,400
Repeated across prompt variants, model families, and controlled runs.
Repetitions per case
5
Enough to compare variants without pretending the scorecard is complete.
Variant
Baseline developer prompt
Ordinary feature request without explicit security guidance.
Default provider configuration captured per run.
Variant
Security-aware prompt
Same request with explicit secure coding requirements and constraints.
Security policy prompt appended in controlled form.
Variant
Review and fix prompt
Model is asked to review or repair intentionally vulnerable code.
Used to compare code generation vs review quality.
Methodology
How the benchmark will be run
Methodology is published early so teams can understand the evaluation design, request private variants, and align internal AI security tests.
Research questions
- Which model families generate secure-by-default code most consistently across common product security tasks?
- Which vulnerability classes recur most often in AI-generated implementations?
- How much do prompt variants and policy instructions improve secure output quality?
- Do models produce useful remediation guidance when insecure output is identified?
Evaluation design
Run controlled developer prompts across vulnerability-oriented feature tasks. Each model variant receives the same task families, language targets, and security policy context. Outputs are assessed using static checks, rubric grading, CWE mapping, and human review for high-severity cases.
Sampling plan
Use synthetic but realistic feature prompts across Node.js, TypeScript, Python, React, API, and backend service scenarios. Each task will include baseline, security-aware, and constrained variants with repeated attempts per case.
Grading and statistics
Combine rule-based vulnerability detection, code pattern checks, LLM-assisted rubric review, and human adjudication for critical findings. Scores separate functional completion, vulnerability introduction, secure-by-default behavior, and fix quality.
Report per-task and aggregate rates with confidence intervals where sample counts allow. Compare model families across repeated trials and prompt variants. Publish methodology before rankings.
Limitations
Prompt templates, task families, scoring rubrics, and public-safe examples will be versioned. Exact model IDs and provider settings must be captured during real runs.
Public examples should avoid operational exploit payloads where unnecessary and should focus on defensive evaluation.
Metrics
Report outputs
Metrics are shown as reporting dimensions for the active benchmark program.
Metric
Secure-by-default rate
Share of generated outputs that implement the requested feature without material security flaws.
Unit
percent
Direction
higher is better
Aggregation
rate
Metric
Vulnerability introduction rate
Share of outputs that introduce one or more vulnerability patterns.
Unit
percent
Direction
lower is better
Aggregation
rate
Metric
Severity-weighted risk score
Composite risk score weighting critical and high-severity failures more heavily.
Unit
score
Direction
lower is better
Aggregation
weighted_score
Metric
Fix quality score
Quality of remediation guidance and generated patches.
Unit
score
Direction
higher is better
Aggregation
mean
Datasets
Data fixtures, source types, and public-safety boundaries
All public-safe. No raw job-description text or private corpus material is shown here.
Dataset
Synthetic secure coding prompt set v1
Synthetic developer prompts covering auth, data access, file upload, frontend rendering, SSRF, secrets, command execution, and crypto.
Source
synthetic
Classification
synthetic
Item count
160
Outputs
Report outputs
Each output is designed to be useful without implying finished benchmark rankings.
Output
Public methodology note
Public explanation of prompt families, risk classes, scoring, and limitations.
Output
Private model scorecard
Customer-facing model comparison scorecard with detailed findings and remediation guidance.
Output
SARIF findings export
Structured vulnerability findings suitable for security tooling and developer workflows.
Status timeline
Where the suite sits now
The timeline shows current build state and the publication boundary.
Status timeline
Active build
Methodology and fixtures are under active build; private scoping is available.
Status timeline
Prompt set design
Create synthetic developer prompt families and reference vulnerability labels.
Status timeline
Code evaluation harness
Wire model adapters, static checks, rubric graders, and evidence capture.
Status timeline
Pilot model trials
Run limited private trials and validate scoring.
Commercial bridge
Private benchmarking and related assets
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
Request Secure Code Benchmark
Available now
Private benchmark sprint, model comparison, product-context benchmark, and evidence bundle.
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Claim controls
What the public page can and cannot say
These controls keep the page safe for public use until real results exist.
Claim controls
Public claim guardrails
This suite is in active build. Public model rankings and benchmark results will publish after validation.
Claim boundary
- Public scorecards are validation-gated.
- Ranking claims are not allowed.
- Vendor comparison claims are not allowed.
- This suite is in active build. Public model rankings and benchmark results will publish after validation.
Do not claim
- Do not claim a model is safer than another.
- Do not imply completed vendor testing.
- Do not publish rankings without approved results.