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You can reproduce an AI coding assistant’s security claim only by freezing the exact claim, model version, prompts, task corpus, tools and runtime, then rerunning that scenario set in an isolated environment. Evaluate every output with independent human review, static and dynamic analysis, negative tests and adversarial attacks. A passing test suite or a high vendor benchmark score is evidence about a measured sample—not proof that generated code is secure.
This guide gives you a falsifiable protocol, a recording template, isolation controls, layered verification steps, fair comparison criteria and a reporting format that exposes failures instead of hiding them.
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What does a reproducible security claim look like?
A useful claim states who generated what, under which conditions, and how success was measured. Marketing language such as “secure by design” cannot be reproduced until it becomes a proposition with observable outcomes.
Rewrite the claim as a testable proposition
For example: “With assistant A, model release V, prompt P and repository state R, at least 80% of 100 independent generations compile and contain no findings for the specified CWE set under analyzer version Z.” That sentence defines the population, denominator, security property and tooling. If the vendor has not supplied those details, mark them as unknown rather than silently choosing your own.
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Define failure before running anything
Write operational definitions for a vulnerability, a compilable output, a duplicate, a tool error and a failed run. Decide whether a finding in generated comments counts, whether a partial patch is evaluated, and whether a vulnerability that tests do not reach is still a failure. Keep these rules in version control so they cannot change after results appear.
Separate code security from agent security
There are two different questions. Code-generation security asks whether the produced program has defects. Agent-runtime security asks whether the assistant can be induced to run dangerous commands, read secrets, install a malicious dependency or modify tests and continuous-integration files. Report both; a secure patch produced by an over-privileged agent is not a secure workflow.
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Capture the complete experimental context before the first generation. The following record prevents an apparently successful reproduction from being impossible to audit later.
| Record | What to capture | Why it changes the result |
|---|---|---|
| Assistant and model | Product name, model name, exact version or release date, endpoint and feature flags | Models and safety filters change over time. |
| Interface | IDE extension, API, command-line client or autonomous agent; client version | Interfaces expose different context windows, tools and defaults. |
| Instructions | System prompt, user prompt, hidden policy text when disclosed, follow-up messages and prompt order | Small wording and context changes can alter security behavior. |
| Task and code | Language, framework, task specification, repository commit, branch and fixture data | Security properties depend on the exact application and inputs. |
| Dependencies | Lockfile, package indexes, compiler or interpreter version and base container image | Resolution and vulnerable transitive packages can vary. |
| Runtime controls | Operating system, user identity, filesystem mounts, tool permissions, network policy and credential exposure | These determine the agent’s attack surface. |
| Generation settings | Temperature, top-p, maximum tokens, sampling seed when available, timeout and retry policy | Nondeterministic sampling makes a single run unrepresentative. |
| Sampling plan | Number of runs, randomization, stopping rule and deduplication rule | Changing the denominator can change the apparent rate. |
| Evaluation | Evaluator identity, blinding procedure, analyzer names and versions, rulesets and security metric | Reviewer expectations and tool versions affect findings. |
Store prompts, outputs, logs, patches, tool reports and environment manifests as immutable artifacts. A hash of each artifact helps prove that the item evaluated is the item generated.
How do I make a fixed scenario corpus?
Build a versioned set of representative tasks before comparing assistants. The corpus, not a handful of hand-picked demos, defines what your percentage means.
Map tasks to security requirements
For each scenario, state the intended behavior and the applicable Common Weakness Enumeration (CWE) or application-security requirement. Include authentication and authorization, input validation, file handling, cryptography, logging, error handling, dependency use and data protection. Record both the secure design and the specific observable checks that distinguish it from an insecure implementation.
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Include normal, negative and boundary cases
- Valid requests and expected successful flows.
- Missing, malformed, oversized and type-confused inputs.
- Expired, revoked and wrongly scoped tokens.
- Cross-tenant and privilege-escalation attempts.
- Concurrent requests, retries and partial failures.
- Dependency-resolution failures and unavailable services.
- Unexpected encodings, null bytes, path separators and shell metacharacters.
Negative cases are essential: code that works for the happy path can still fail at the security boundary. Keep prompts and expected properties in the same repository as the harness, with a commit identifier for every run.
Prevent corpus leakage
If the assistant has seen the benchmark tasks or their solutions, the score measures memorization as much as generation. Keep a private holdout set, rotate scenarios, and disclose whether examples were public, synthetic or derived from production incidents. Do not mix development prompts with the final evaluation set.
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How should I freeze and isolate the execution environment?
Pin every component that can affect an output or finding, then run the assistant with the least privilege needed to complete the task.
- Create a disposable virtual machine or container from a recorded image digest. Pin the operating-system image, compiler, interpreter, package manager and analyzer versions.
- Check out the repository at an exact commit and verify its dependency lockfile. Prefer an internal package mirror and a deny-by-default network policy.
- Run as an unprivileged user. Mount only the workspace, preferably read-only for source files that the assistant must not alter, and use a separate writable directory for generated patches.
- Provide short-lived, scoped credentials only when a scenario explicitly tests credential handling. Never expose a developer’s home directory, cloud tokens, SSH keys or production endpoints.
- Log every tool invocation, command, file read and file write. Block or alert on changes to tests, CI configuration, dependency manifests and unrelated directories.
- Snapshot the workspace before and after each run. Destroy the environment after collection so persistence and cross-run contamination are impossible.
Repository text, issue descriptions, pull requests, fetched pages and tool responses can contain indirect prompt-injection instructions. Treat all external content as untrusted data, quote it in the task fixture, and require explicit approval before an agent can execute commands or change evaluation infrastructure.
How many generations should I run, and what do I do with failures?
Run repeated, independent generations because sampling and tool timing can change the result. There is no universal run count; choose one that gives useful uncertainty for your expected rate and publish the rationale.
Preserve every outcome
Save successful patches, syntax errors, compilation failures, timeouts, tool errors, refusals and empty responses. A run that cannot produce code is not silently removed from the denominator unless your pre-registered definition says otherwise. Report both a completion rate and a security rate among completed outputs.
Handle duplicates transparently
Define a duplicate rule before evaluation: exact byte match, normalized abstract syntax tree, or semantic equivalence. Publish the number removed and show rates both before and after deduplication when the choice materially affects the result. A stricter duplicate or error-removal rule can make a replication appear safer without any change in generated code.
Use independent sampling
Reset conversation state between runs unless persistent context is part of the claim. Randomize scenario order, record seeds where the interface exposes them, and separate retries caused by infrastructure faults from fresh samples. Keep a ledger linking each output to its prompt, model, environment and evaluator decision.
What verification layers reveal whether generated code is secure?
No single scanner or test suite covers all failure modes. Apply checks in layers, and add tests the assistant did not write.
1. Build and functional tests
Compile or package the output using the pinned toolchain. Run unit and integration tests against a clean database and service configuration. Record compiler warnings, flaky tests and skipped tests. A build failure is an outcome to report, not a reason to replace the patch.
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2. Static analysis
Run a pinned ruleset for the language and framework, including taint and data-flow checks where available. Record analyzer version, configuration, suppressed findings and rule identifiers. Review both high-confidence findings and suspicious “no result” cases: an analyzer may lack coverage for a language feature or framework abstraction.
3. Dependency and secret scanning
Resolve dependencies from the locked manifest, inspect transitive packages and compare advisories using a dated database snapshot. Scan source, build artifacts and history for credentials, private keys and tokens. A clean scan means only that the selected tools and databases found nothing; it does not establish that dependencies are trustworthy or current.
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Generate malformed and boundary inputs for parsers, upload handlers, serializers, protocol endpoints and authorization checks. State the time budget, seed and corpus. Properties such as “a user cannot read another tenant’s record” often expose defects that example-based tests miss.
5. Dynamic testing
Where an executable service exists, exercise it in a disposable environment with dynamic application-security tests. Check authentication, authorization, injection, path traversal, unsafe redirects, rate limits and error leakage. Keep test traffic and credentials isolated from real systems.
6. Manual review of critical paths
An independent reviewer should trace trust boundaries, privilege checks, cryptographic choices, secret handling, deserialization and failure paths. Reviewers should be blinded to the assistant identity when practical. Have at least two reviewers classify disputed findings and report their agreement and adjudication process.
OWASP’s secure-coding guidance summarizes the principle: “Measure security confidence by adversarial testing results and independent analysis, not by ‘all tests pass.’” NIST IR 8397 likewise treats code review, static analysis, fuzzing and testing as developer-verification activities.
How can I adversarially challenge the claim?
Design attacks that target the assumptions made in the claim, not just the examples supplied by its author.
- Send shell metacharacters, SQL fragments, template expressions, alternate encodings and oversized values.
- Try path traversal, symlink escapes, archive extraction attacks and unrestricted file uploads.
- Use expired, revoked, wrongly signed and cross-tenant tokens; test missing authentication on administrative functions.
- Exercise unsafe deserialization, object-type confusion and untrusted redirects.
- Run concurrent updates to expose race conditions and time-of-check/time-of-use bugs.
- Place prompt-injection text in repository files, issue descriptions and fetched documentation, then watch whether the agent reveals secrets or changes tests.
- Attempt to alter the test harness, CI files, dependency manifest or unrelated source and verify that controls detect and stop it.
Include known-vulnerable fixtures and secure controls in the same corpus. A system that refuses a dangerous request but writes an insecure workaround has not passed the security scenario.
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How should I compare two assistants fairly?
Use identical prompts, repository commits, dependency locks, environment limits, sampling plans and evaluation rules. Compare the dimensions below rather than a single headline score.
| Comparison axis | Questions to answer |
|---|---|
| Security outcome | What is the vulnerability rate, severity distribution and confidence interval? Are compilation failures included? |
| Reproducibility | Are prompts, model versions, environment manifests, seeds and raw outputs disclosed? |
| Coverage | Which languages, frameworks and CWEs are tested? Are negative, boundary and adversarial cases included? |
| Evaluator independence | Who reviewed findings, were they blinded, and how were disagreements resolved? |
| Analyzer transparency | Which tools, rulesets, versions and suppressions were used? |
| Runtime isolation | Can the assistant execute commands, access the network, read credentials or modify tests and CI? |
| Dependencies and secrets | Are transitive packages, lockfiles, secret scans and advisory dates covered? |
| Maintenance cost | How often must prompts, harnesses, analyzers, vulnerability databases and expected properties be updated? |
Report per-scenario results as well as aggregate rates. Two assistants can have the same average while one fails almost every authorization case and the other fails only low-impact formatting tasks.
What do published replication results actually show?
A targeted replication of a Copilot security study illustrates why methods matter. The figures below apply only to the populations and procedures of those studies; they are not universal rates for AI coding assistants.
| Study | Suggestions analyzed | Vulnerable suggestions | Important methodological context |
|---|---|---|---|
| Original study authors, 2022 | 550 | 36% | Original task set and tool chain. |
| Replication study authors, 2023 | 447 | 27% | Different model nondeterminism, stricter duplicate and error removal, and a changed CodeQL version contributed to the difference. |
Both studies still found vulnerable suggestions involving CWE-78 command injection, CWE-434 unrestricted file upload, CWE-306 missing authentication for a critical function and CWE-502 unsafe deserialization. The changed percentages do not establish that the assistant became safer or less safe; they show that population, filtering and analyzer versions can move a result.
Why doesn’t a high benchmark score prove secure code?
A benchmark measures performance on its task distribution. GitHub’s responsible-use documentation says its evaluations combine industry-standard benchmarks with internally developed suites and warns that code review can be biased toward particular languages or coding styles. That makes a score useful context, not a universal security guarantee.
A benchmark may omit authorization failures, malicious repository content, dependency compromise, concurrency, secrets and post-deployment monitoring. It may also reward compilation or functional correctness without testing whether an attacker can cross a trust boundary. Ask for the scenario list, population, vulnerability definitions, analyzer versions, reviewer process and raw failure counts before treating a score as security evidence.
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Use standards as a coverage map, not as a substitute for testing. OWASP AISVS is described as a catalogue of testable requirements. Its 1.0 page states 191 requirements across 12 chapters and three appendices; record that version and count because standards change. Map each relevant requirement to one or more corpus scenarios, automated checks and manual-review questions.
The workflow should continue from design through post-deployment monitoring, with mandatory security gates retained even when an assistant writes the code. A generated patch should not bypass threat modeling, review, release approval, vulnerability response or operational telemetry.
How do I report uncertainty and failures?
Publish enough detail for another team to calculate the result independently.
- State the exact claim, population, sample count and completion count.
- Give vulnerable counts and rates, severity categories and scenario-level results.
- Provide confidence intervals or another uncertainty estimate appropriate to the sampling design.
- List model, client, compiler, dependency, analyzer and vulnerability-database versions.
- Explain duplicate, timeout, compilation-error and refusal handling.
- Report reviewer identities or roles, blinding, agreement and adjudication.
- List every deviation from the original claim and every known coverage gap.
- Release prompts, expected properties, harness code and sanitized artifacts when disclosure is safe.
Do not collapse “no finding,” “not analyzed” and “could not compile” into one green result. Readers need to know whether a scenario passed, was outside tool coverage or never reached evaluation.
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Reproduction is a maintenance process. Model releases, system prompts, package registries, analyzers and vulnerability databases all change.
- Run a small sentinel suite on every model or client update and compare outputs with the previous version.
- Rebuild the environment from locked manifests and image digests; record any unavailable package or changed advisory.
- Review new language features, frameworks and agent tools for missing rules and new permissions.
- Refresh adversarial cases from incidents and newly disclosed weakness patterns while preserving an unchanged holdout set.
- Recalculate rates and uncertainty; never splice new runs into an old denominator without labeling the version boundary.
- Reapprove security gates when an assistant gains command execution, network access, repository search or automatic pull-request creation.
What is a practical end-to-end reproduction checklist?
- Write the vendor claim as a measurable proposition with a defined denominator and vulnerability rule.
- Record assistant, model, interface, prompts, task, repository commit, lockfile, operating system, permissions, network policy and sampling settings.
- Version a corpus containing representative, negative, boundary, concurrency, dependency and authorization scenarios.
- Pin the container, compiler, dependencies, analyzers and databases; create a disposable least-privilege workspace.
- Run the predeclared number of independent generations and retain every output and error.
- Compile, run independent functional tests, perform static and dependency/secret analysis, fuzz, dynamically test where applicable and review critical paths manually.
- Attack the claim with injection, traversal, upload, deserialization, race-condition, privilege-escalation and prompt-injection cases.
- Compare assistants on outcomes, coverage, transparency, isolation and maintenance—not only aggregate benchmark scores.
- Publish counts, rates, uncertainty, reviewer agreement, tool versions, filtering rules and deviations.
- Repeat the sentinel suite after every material model, toolchain, dependency or permission change.
GitHub’s Copilot guidance puts the responsibility plainly: use the assistant with testing, code review, security tools and your own judgment, and apply the same precautions you would use for code you did not write yourself.
Frequently Asked Questions
Can I reproduce a security percentage if the vendor will not disclose its prompts?
You can reproduce a new, independently defined experiment, but not the vendor’s exact percentage. Label your result as a separate claim and state which undisclosed prompts, filters, model settings or evaluator rules prevent an exact replication.
Should compilation failures count as insecure outputs?
Treat compilation failure as its own outcome and publish it alongside vulnerability results. Whether it enters the security denominator must be fixed before sampling; silently discarding failures can inflate the apparent safety rate.
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How do I test an assistant that can edit files and run commands?
Use a disposable least-privilege workspace, deny-by-default network access and short-lived credentials, and log every command and file change. Add scenarios containing malicious repository instructions to verify that the agent cannot alter tests, CI or unrelated files without detection.
What confidence interval should I use for a vulnerability rate?
Choose an interval suited to a binomial proportion, such as Wilson or exact intervals, and state the method and confidence level. If samples are clustered by task or prompt, account for that dependence or report task-level variation instead of treating every output as fully independent.
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