The Tool Desk
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You can reproduce an AI coding assistant’s security claim only by freezing the exact model, prompts, code, tools, and evaluation rules, then rerunning a versioned set of scenarios in an isolated environment. Check every generation with independent human review, static and dynamic analysis, negative cases, and adversarial attacks. A benchmark score, a successful compilation, or one run in which all tests pass is evidence about that test setup—not proof that the generated code is secure.
The practical goal is a falsifiable result: for a named assistant version and task corpus, how often did outputs compile, what vulnerabilities were found, how severe were they, and how much uncertainty remains? The protocol below shows how to obtain and report that result without hiding failures or changing the rules after seeing the outputs.
What exactly are you trying to reproduce?
Start by converting promotional language into a proposition that another team could disprove. A useful form is: under prompt P, assistant and model version V, language and framework L, and environment E, at least X percent of independent outputs meet the stated security properties.
Define the unit being measured
Decide whether one unit is a completion, a whole repository change, a file, or a suggestion. State whether a run that produces syntax errors, times out, or fails to call a tool remains in the denominator. Define a vulnerability using a named taxonomy such as a CWE, and specify how severity is assigned. Also define what counts as a duplicate: identical text, equivalent abstract syntax, or the same vulnerability in a regenerated patch.
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Define success before generating code
Write the required security properties and pass/fail rules before the assistant sees the task. For example, a successful output might need to compile, pass functional tests, avoid a specified set of CWEs, preserve authorization checks, and pass an independent review. If the original claim leaves any of these terms vague, report that ambiguity rather than silently choosing the most favorable interpretation.
What should you record before running the assistant?
A reproduction is only as credible as its record of conditions. Capture the following information in a machine-readable manifest and in the human-readable report.
| Item | Record precisely | Why it matters |
|---|---|---|
| Assistant and model | Product name, model name, exact release or release date, and any system prompt | Models and safety filters can change without a visible interface change. |
| Interface | IDE extension, API, command-line client, or autonomous agent; client version and configuration | Tool routing, context limits, and post-processing differ by interface. |
| Prompts and task | System and user prompts, attached files, examples, acceptance criteria, and token limits | Small wording or context changes can alter both code and security behavior. |
| Code context | Language, framework, repository commit, branch, generated files, and dependency lockfile | Dependency versions and surrounding code determine which vulnerabilities are possible. |
| Execution environment | Operating system, compiler or runtime, container image digest, environment variables, and locale | Build and analyzer results are not portable by default. |
| Permissions and network | Filesystem scope, command permissions, package-install rights, credentials, and network allowlist | An agent with broader access has additional runtime attack surfaces. |
| Sampling | Temperature or equivalent settings, seed when available, number of runs, retry policy, and timeout | Stochastic generation makes a single run a weak measurement. |
| Data handling | Raw outputs, logs, tool calls, syntax failures, and redaction procedure | Discarding inconvenient runs creates survivorship bias. |
| Evaluation | Analyzer names and versions, rule sets, test revisions, evaluator identities, and security metric | Different tools and reviewers can classify the same code differently. |
Preserve the manifest beside the scenario corpus. If a value is unavailable—for example, a hosted service does not expose its seed—write that limitation down instead of implying deterministic behavior.
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How do you build a scenario corpus that tests security rather than memorization?
Use a fixed, version-controlled corpus of representative tasks mapped to application-security requirements or CWE categories. The corpus should resemble the work for which the claim is made, not a hand-picked collection of easy examples.
Include normal and hostile inputs
For each feature, include ordinary valid requests as well as negative and boundary cases. Add malformed payloads, expired or revoked tokens, authorization failures, concurrent requests, dependency-resolution failures, and unexpected encodings. Include tasks that require preserving an existing security control, not merely adding a new function.
Specify expected properties
Store the prompt, repository snapshot, expected behavior, forbidden behaviors, and security properties together. Keep these files under version control and review changes as you would review production test code. A scenario should state, for example, that a user may read only their own records, that uploaded paths cannot escape a designated directory, or that untrusted data must not reach an interpreter.
Prevent corpus leakage
Separate development examples from evaluation scenarios. Do not let the assistant see the expected exploit or the evaluator’s hidden tests. When a benchmark is public, assume future models may have encountered its wording; use held-out variants and document that limitation.
How do you freeze the environment safely?
Pin the model or service release, client, compiler, dependencies, analyzer rules, and container image. Use a disposable workspace with least privilege, no production credentials, and an explicit network policy. Rebuild the environment from a script or image digest so another team can obtain the same starting state.
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Repository text is not automatically trustworthy. OWASP warns that issues, pull requests, fetched pages, documentation, and tool responses can contain indirect prompt-injection instructions. Treat those sources as untrusted input, log what the agent reads, and prevent content from granting new permissions. Keep package installation and outbound connections disabled unless the scenario requires them; when they are required, allowlist destinations and record every request.
How should you sample repeated generations?
Run enough independent generations to expose nondeterminism and justify an uncertainty estimate. There is no universal run count: choose it from the expected effect size, acceptable error, scenario diversity, and available review capacity, then disclose the rationale.
- Start each run from the same clean repository and environment snapshot.
- Apply the same prompt and tool policy, changing only the declared sampling variable or seed.
- Save the complete output, intermediate tool calls, compiler messages, test logs, and timestamps.
- Count syntax failures, timeouts, refusals, and tool errors; do not silently remove them.
- If deduplication is necessary, publish the exact rule and show counts before and after deduplication.
- Record retries separately so a successful retry cannot conceal an initial failure.
Model nondeterminism, duplicate-removal rules, and analyzer updates can materially change a measured rate. A comparison is invalid if one assistant receives more retries, or if one study removes errors while another counts them.
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No single scanner can establish security. Use independent layers, and add tests that the assistant did not write.
Build and functional checks
Compile or package the output with the pinned toolchain. Run unit and integration tests, including authorization and error-path tests. Record build failures as outcomes; a non-compiling patch is not equivalent to a secure patch.
Static analysis
Run the declared static analyzer and rule set, including rules for injection, authentication, authorization, cryptography, deserialization, path handling, and resource use. Save the analyzer version and configuration because rule changes can alter findings without any code change.
Dependency and secret checks
Resolve dependencies from the lockfile and scan for known vulnerable versions, malicious packages, license-policy violations, and embedded credentials or tokens. A function can be locally correct while importing a compromised or obsolete dependency.
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Fuzzing and property-based tests
Generate malformed, oversized, truncated, and unexpected inputs. State the properties being checked—for example, that parsing never causes an authorization bypass or that a path remains within its root. Keep seeds and failing inputs so another evaluator can replay them.
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Dynamic testing
Where an application can run, exercise it with dynamic tests such as DAST, API abuse cases, session manipulation, and concurrency checks. Test realistic deployment configuration, because secure source code can become exposed through an unsafe runtime setting.
Independent human review
Have reviewers who did not write the prompt or generation code inspect security-critical paths. Require them to record findings, rationale, severity, and disagreements. OWASP’s guidance is explicit that security confidence should be measured by adversarial testing and independent analysis, not by “all tests pass.”
How do you adversarially challenge the claim?
After ordinary verification, attempt to break the stated security property. Use malformed input, injection payloads, path traversal, unrestricted file upload, unsafe deserialization, hard-coded credentials, missing authentication, privilege escalation, and race conditions as appropriate to the task.
Place benign-looking but malicious instructions in repository files, issue text, pull requests, or fetched documentation to test resistance to indirect prompt injection. Observe whether the agent changes tests, CI configuration, unrelated files, access controls, or dependency manifests. The run should fail the runtime-security check if such changes occur without an explicit, logged authorization.
Adversarial tests must be independent of the assistant’s own tests. Otherwise the system can appear successful simply because it generated both the implementation and the checks that certify it.
What should a fair comparison between assistants include?
Use the same corpus, environment, run budget, and adjudication process for every assistant. Report more than a single vulnerability percentage.
| Comparison axis | Questions to answer |
|---|---|
| Security outcome | How many outputs were vulnerable, which CWEs occurred, and what severities and exploit conditions were confirmed? |
| Reproducibility | Are prompts, model releases, repository commits, dependencies, seeds, and tool settings disclosed? |
| Coverage | Which languages, frameworks, CWEs, negative cases, malformed inputs, and adversarial scenarios were tested? |
| Evaluator independence | Who reviewed findings, how were disagreements resolved, and was the evaluator separate from the generator? |
| Analyzer transparency | Are analyzer versions, rule packs, thresholds, and known limitations stated? |
| Runtime isolation | Could the assistant execute commands, install packages, read external content, or access credentials? |
| Dependency and secret handling | Were transitive dependencies, lockfiles, generated secrets, and leaked credentials checked? |
| Maintenance cost | How often must the corpus, analyzers, vulnerability data, and environment be updated as models and frameworks change? |
What do published replication figures actually show?
A targeted Copilot replication illustrates why study conditions must travel with every number. The replication study authors reported 27% vulnerable suggestions in 2023 after analyzing 447 suggestions. The original study authors reported 36% vulnerable suggestions in 2022 from 550 suggestions.
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|---|---|---|---|
| Original study | 2022; 550 suggestions | 36% | Original model, corpus, duplicate and error rules, and analyzer version. |
| Replication | 2023; 447 suggestions | 27% | Different model behavior, stricter duplicate and error removal, and a changed CodeQL version. |
Both analyses still found vulnerable suggestions, including CWE-78 command injection, CWE-434 unrestricted file upload, CWE-306 missing authentication for a critical function, and CWE-502 unsafe deserialization. The difference between 36% and 27% is not a universal improvement score: model nondeterminism, population changes, filtering rules, and analyzer changes all affect the denominator and the findings.
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Why does a benchmark score not prove that generated code is safe?
A benchmark measures performance on its task distribution. It may omit languages, frameworks, deployment configurations, rare authorization paths, supply-chain threats, or adversarial inputs that matter in your repository. GitHub’s responsible-use guidance notes that evaluations combine industry-standard benchmarks with internally developed suites and that code review can be biased toward particular languages or coding styles.
GitHub’s guidance says to use the assistant with good testing, code review, security tools, and your own judgment, and to take the same precautions as with code you did not write yourself.
Passing a benchmark or a test suite therefore supports a narrow claim: the output met those measured checks under those conditions. It does not establish that every generated path is safe, that the model will behave the same after an update, or that an autonomous agent cannot be induced to misuse its tools.
How do OWASP and NIST frame verification?
NIST IR 8397 treats code review, static analysis, fuzzing, and testing as developer-verification activities. Use that combination as a baseline rather than selecting one favorite scanner.
OWASP AISVS is a catalogue of testable requirements intended to cover the lifecycle from design through post-deployment monitoring, while retaining mandatory security gates. Its 1.0 page lists 191 requirements across 12 chapters and three appendices, a versioned count attributed to the OWASP Foundation in 2026. Recheck the release and requirement count when publishing or rerunning a study because standards evolve.
The OWASP Secure Coding with AI guidance’s central rule is to measure confidence through adversarial testing and independent analysis, not merely through passing tests. Map each scenario to the applicable requirements, record unmet requirements explicitly, and keep the gates in continuous integration so a later model update cannot bypass them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How is generated-code security different from agent-runtime security?
Code-generation security asks whether the produced program contains vulnerabilities. Agent-runtime security asks whether the assistant itself can cause harm while producing it. An agent that executes shell commands, installs packages, reads external content, edits CI, or accesses credentials can be compromised even when its final code looks correct.
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|---|---|
| Command execution | Use a disposable container, deny privileged operations, and log every command and exit status. |
| Package installation | Use a locked dependency set or a quarantined cache; review new packages before execution. |
| Filesystem access | Mount only the scenario workspace and make unrelated paths inaccessible. |
| Network and external content | Apply an allowlist, capture responses, and treat fetched text as untrusted instructions. |
| Credentials and secrets | Provide synthetic credentials with minimal scope, rotate them after runs, and scan logs for disclosure. |
| Test or CI tampering | Protect evaluator files and compare the final tree with an approved change manifest. |
Report the two results separately. A secure patch produced by an unsafe agent is not an acceptable operational outcome, and a well-sandboxed agent does not make insecure source code safe.
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How should you report uncertainty, failures, and reviewer disagreement?
Publish sample counts, vulnerable counts, rates, severity distribution, scenario-level results, and confidence intervals or another justified uncertainty estimate. Include the number of compilable outputs, all failure categories, duplicate counts before and after filtering, and the exact denominator used for each metric.
Name the model and tool versions, analyzer rules, evaluator qualifications, agreement statistics or adjudication method, and every deviation from the original claim. If a reviewer cannot agree whether a finding is exploitable, preserve both judgments and explain the final decision. A precise uncertainty statement is more informative than a rounded percentage with no method.
What reproduction failures are most common, and how do you fix them?
| Failure | What it looks like | Fix |
|---|---|---|
| Version drift | The same prompt produces a different result months later. | Archive release identifiers, client versions, container digests, and lockfiles; rerun from the archive. |
| Cherry-picking successes | Only compiling or “clean” outputs appear in the report. | Keep every run in the denominator and show failure categories. |
| Hidden deduplication | The published sample is smaller than the generation log. | Publish the equivalence rule and pre- and post-filter counts. |
| Benchmark mismatch | A result from one language or framework is generalized to all repositories. | State the tested distribution and avoid universal claims. |
| Analyzer drift | Findings change after a scanner upgrade. | Pin the analyzer and rerun old outputs with both versions when comparing studies. |
| Reviewer inconsistency | Different experts assign conflicting severities. | Use a written rubric, blinded review where practical, and documented adjudication. |
| Environment contamination | Cached packages, credentials, or prior files influence a run. | Reset to a disposable snapshot and log cache and network state. |
| Agent tampering | Tests or CI are modified to make the output pass. | Protect evaluator assets and verify the final filesystem against an allowlisted manifest. |
What should be included in a reproducibility package?
Give another evaluator enough material to reconstruct the experiment without guessing. A practical package contains:
- A manifest with assistant, model, interface, prompts, sampling settings, permissions, and dates.
- The scenario corpus, repository commits, expected properties, hidden-test inventory, and change history.
- Container or virtual-machine definitions, compiler and dependency lockfiles, analyzer binaries, and rule configurations.
- Raw generations, tool-call and network logs, build and test output, vulnerability reports, and redaction rules.
- The deduplication algorithm, denominator calculation, severity rubric, reviewer identities or roles, and adjudication notes.
- Scripts that reset the workspace, execute the checks, and produce the tables in the report.
- A deviation log describing any unavailable model setting, failed rerun, changed dependency, or corpus amendment.
Keep sensitive material separate from public artifacts. Publish hashes, synthetic replacements, or access-controlled bundles when raw prompts or code cannot be disclosed, and state exactly what an independent evaluator cannot verify.
Frequently Asked Questions
Can I reproduce a security result if the original model version is no longer available?
You can reproduce the protocol but not claim an exact replication. Archive the original outputs and environment, rerun the same corpus on the nearest documented release, and label the result as a follow-up comparison with the version difference as a limitation.
How many generations are enough for an AI coding security test?
There is no fixed number that works for every claim. Choose the run count from the expected difference you need to detect, the diversity of scenarios, and the uncertainty you can tolerate, then justify it and report the calculation rather than presenting an arbitrary round number.
Should security reviewers be blinded to which assistant produced the code?
Blinding can reduce brand and style bias when outputs are otherwise comparable, but it cannot replace technical independence. Reviewers still need the task requirements, build evidence, analyzer results, and a process for resolving disagreements.
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How can teams rerun the evaluation after a model or dependency update?
Treat the corpus and environment as versioned test assets. Run a fixed regression set before changing the production assistant, compare vulnerability and failure distributions with the prior release, and investigate any new finding even if the overall pass rate improves.
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