A reliable automated code review does not choose between static analysis and an LLM. It stages them: deterministic tools establish reproducible facts about syntax, dependencies, data flow, secrets, and policy; a constrained language model then interprets business logic, authorization, reachability, and cross-file intent. Only validated deterministic violations or corroborated, high-confidence findings should block a merge. Everything else should arrive as evidence-rich guidance for a human reviewer.
What a hybrid review system does
A pull request (PR) changes more than the lines shown in a diff. It can alter a dependency graph, configuration, generated code, deployment permissions, database access, or the assumptions another service makes. A hybrid reviewer examines those effects in two complementary ways:
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- Deterministic analysis applies versioned rules and program analysis. The same commit produces the same result, which makes findings testable, explainable, and suitable for merge gates.
- LLM analysis reasons about intent and interactions that are difficult to encode as generic rules, such as whether an authorization check is reachable on every route or whether a validation step is bypassed in one workflow.
The model is not a replacement for CodeQL, Semgrep, linters, secret scanners, dependency checks, or architecture policies. A 2025 benchmark of 10 real-world C# projects containing 63 vulnerabilities found mean F1 scores of 0.797, 0.753, and 0.750 for three tested LLMs, compared with 0.260, 0.386, and 0.546 for three static analyzers. The same study reported noisier LLM output and less precise line localization. Those results support broad LLM triage combined with deterministic verification; they do not establish performance for every language, repository, or model.
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Recommended architecture: facts first, interpretation second
Run the workflow as an evidence-preserving pipeline. Keep raw outputs immutable, attach every observation to the exact commit, and pass the model only the context needed for a decision.
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- Trigger. Start on PR creation, each update, a scheduled full scan, or an API request. Cancel obsolete jobs when a newer commit supersedes them.
- Inventory the change. Compute the merge-base diff, changed files, renames, generated files, dependency and lockfile changes, infrastructure configuration, and the repository’s review policy.
- Run deterministic checks. Execute formatters and type checks, linters, SAST and taint/data-flow queries, secret detection, dependency and license policies, and organization-specific architecture rules.
- Normalize observations. Convert output to SARIF where supported or to a common finding record containing detector identity, version, severity, confidence, CWE or category, location, evidence, remediation, and commit SHA.
- Select bounded context. Give the LLM the relevant diff, surrounding functions, declarations and call sites needed to understand it, applicable policy files, and selected dependency/configuration facts. Do not send the entire repository by default.
- Request structured reasoning. Require quoted code evidence, exact file and line ranges, confidence, impact, reachability assumptions, and a remediation that could be disproved by inspection or a test.
- Correlate. Deduplicate identical observations, connect an LLM claim to matching rule hits, and preserve both the original evidence and the correlation decision.
- Apply policy. Block only on approved deterministic rules or corroborated high-confidence findings. Route uncertain or advisory observations to reviewers.
- Learn from outcomes. Record whether a reviewer accepted, dismissed, or fixed each finding, and whether a defect later escaped. Use those labels to tune rules, prompts, context selection, and thresholds.
Assign each question to the right detector
| Review question | Best first detector | Why |
|---|---|---|
| Does the code parse, type-check, and meet formatting rules? | Compiler, formatter, and linter | Results are deterministic and easy to reproduce. |
| Does an untrusted value reach a sensitive sink? | SAST taint or data-flow query | Explicit source-to-sink paths provide auditable evidence. |
| Is a secret committed or is a vulnerable dependency introduced? | Secret and dependency scanners | Specialized databases and policies are more reliable than model guesses. |
| Does a change violate a service boundary, naming rule, or ownership policy? | Architecture and repository rules | Organization-specific invariants can be versioned and regression-tested. |
| Can a user reach a privileged operation through an alternate workflow? | LLM with call-graph and route context, then human verification | The question depends on intent and cross-file control flow. |
| Does a fix preserve business invariants across services? | LLM explanation plus targeted tests and reviewer approval | Natural-language requirements and distributed assumptions are hard to encode as one generic rule. |
When a recurring LLM finding is confirmed, convert the invariant into a deterministic rule, helper library, test, or policy check. This reduces future ambiguity and makes the control regression-testable, as recommended by OWASP guidance.
Build the deterministic layer
Choose checks that can justify a merge gate
Use deterministic tools for known insecure patterns, syntax and type errors, dependency and secret policies, taint analysis, maintainability limits, and organization-specific invariants. Pin tool versions and rule packs. Store the configuration with the repository or in an explicitly reviewed policy repository so a change to the rules is itself visible in a PR.
- Run fast checks on every PR and reserve expensive whole-repository analysis for scheduled or affected-project jobs.
- Cache results by commit SHA, tool version, rule-set hash, and relevant configuration hash. Invalidate the cache when any of those inputs changes.
- Emit stable detector IDs. A renamed rule should retain an alias or migration mapping so historical acceptance data remains meaningful.
- Include the code excerpt or a cryptographic excerpt hash, line and column range, data-flow trace when available, and a remediation reference.
- Keep raw scanner output immutable. Normalization should create a new record rather than overwrite the original payload.
Use SARIF or a common finding schema
SARIF is the standard format identified by GitHub for CodeQL findings and is useful for transporting results between CI, pull-request annotations, and dashboards. If a tool cannot emit SARIF, map it to a shared schema such as:
{
"finding_id": "stable-detector-and-location-id",
"detector_type": "rule",
"detector": "tool-name",
"detector_version": "pinned-version",
"commit_sha": "exact-commit",
"file": "src/auth/handler.ts",
"start_line": 42,
"end_line": 47,
"excerpt_hash": "hash-of-quoted-code",
"category": "authorization",
"cwe": "CWE-862",
"severity": "high",
"confidence": 0.98,
"evidence": "data-flow or rule explanation",
"remediation": "required fix or test",
"corroborated_by": [],
"policy_decision": "block"
}The record should also allow exploitability or reachability notes, references, and a reviewer decision. Keep the policy decision separate from severity: a medium-severity issue in a regulated path may require a block, while a high-severity pattern with an unproven reachability assumption may require human review first.
Constrain the LLM reviewer
Send the smallest context that preserves correctness
Start with the PR diff and then retrieve only what is needed to interpret it: the complete changed functions, definitions they call, route or permission declarations, relevant schemas, tests, configuration, and deterministic findings. Include the merge-base and commit SHA so the model cannot confuse old and new code. Redact credentials, customer data, and unrelated proprietary files before transmission.
Demand falsifiable findings
Reject any model response that lacks all of the following:
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- One or more quoted snippets from the reviewed commit.
- Exact file paths and line ranges that a reviewer can open.
- A statement of the violated invariant, expected attacker capability, or business impact.
- Confidence expressed on a defined scale, plus assumptions such as “this route is externally reachable.”
- A remediation or test whose success would disprove the finding.
- A classification of
block,warn,info, orneeds-human-reviewproposed independently of the final repository policy.
Cap the number of comments per PR and prefer one deduplicated issue with several evidence locations over a stream of near-identical suggestions. Ask for a concise explanation after the structured fields, not instead of them. If the model cannot localize a claim precisely, downgrade it to advisory status.
Use the model for interpretation, not invented facts
Tell the reviewer which files and tool outputs are authoritative. It should not infer a dependency version, deployment permission, or route exposure that is absent from the supplied context. A useful prompt asks the model to distinguish observed facts from assumptions, identify missing evidence, and abstain when the context is insufficient. Never allow it to silently edit the review policy, suppress a rule, or rewrite its own instructions.
Correlate findings before commenting
Correlation is where a hybrid system becomes more useful than two independent bots. Normalize file paths and line ranges, then group observations by a stable fingerprint built from detector category, location, code excerpt hash, and data-flow endpoints.
- Rule confirmed by LLM: keep the deterministic severity and policy; use the model’s explanation to clarify impact.
- LLM proposes an issue with no rule hit: require human review unless the detector has a measured track record and the evidence is complete.
- Rule and LLM disagree: show both records, preserve the raw outputs, and ask a reviewer to adjudicate rather than averaging scores.
- Duplicate findings: publish one comment with all relevant locations and retain the individual detector records for metrics.
Do not merge confidence numbers as though they were calibrated probabilities. A model’s 0.9 and a scanner’s 0.9 may represent different scales. Calibrate each detector against labeled examples, then define explicit policy thresholds.
Design merge gates that earn trust
Start in advisory mode
For the first rollout, post non-blocking comments and collect reviewer acceptance, dismissal reasons, localization accuracy, and remediation success. Measure false positives on the repositories and languages that matter to your team rather than relying on a vendor-wide claim.
Promote only validated checks
A check is ready to block when its false-positive rate, line accuracy, reviewer agreement, and fix success meet a threshold your team has documented. Block immediately for deterministic policies that already have a tested severity definition, such as an exposed secret or a prohibited dependency. Require corroboration or explicit approval for a novel LLM finding.
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Protect sensitive paths
Use CODEOWNERS or an equivalent ownership file for authentication, authorization, payment, cryptography, infrastructure, and data-export paths. Require an explicit approval from the responsible owner, and dismiss stale approvals when new commits change the protected files. A green scanner is not proof that a repository is safe; absence of findings is not evidence of absence of flaws.
Keep the gate explainable
The status check should state the detector ID, commit, policy rule, evidence location, and the exact action needed to clear it. Provide a documented override path that records who approved the exception, why it is acceptable, and when it expires. Do not hide an advisory model disagreement behind a generic “quality failed” status.
Secure the reviewer itself
Prompts, repository instruction files, tool configuration, and retrieved context are part of the attack surface. OWASP specifically identifies persistent steering files such as AGENTS.md and .github/copilot-instructions.md as controls that require explicit approval and CI checks when changed.
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- Restrict network and tool calls to an allowlist; log every invocation and its parameters.
- Treat code and comments in untrusted PRs as hostile input. Separate repository text from system policy and escape it when inserted into prompts.
- Require a trusted maintainer approval for changes to prompts, rules files, model settings, context-retrieval code, and policy thresholds.
- Prevent the model from merging code, changing branch protection, modifying its own instructions, or deleting evidence.
- Set retention and redaction rules for prompts, diffs, and model responses, especially where source code or personal data leaves the build environment.
Run the deterministic checks in a separate trust boundary when possible. A malicious build script should not gain the credentials used to post review comments or access the model gateway.
Implement the workflow in CI
A practical job graph separates fast feedback from expensive reasoning:
- Prepare: check out the exact PR merge base and head, verify repository policy files, and calculate changed components.
- Parallel deterministic jobs: run language checks, SAST, secrets, dependencies, and architecture policies. Upload raw artifacts and normalized SARIF or finding records.
- Context builder: collect only changed code plus bounded declarations, tests, configuration, and relevant findings. Apply redaction and token limits.
- LLM review job: submit a versioned prompt and structured context. Validate the response against a JSON schema and reject malformed or unsupported comments.
- Correlator: fingerprint, deduplicate, attach corroboration, and calculate the repository policy decision.
- Publisher: post annotations and one summary check, preserving links to immutable CI artifacts. Never overwrite the result for a different commit.
- Metrics job: record latency, token use, detector outcomes, reviewer actions, and escaped defects without exposing secrets in telemetry.
When a new commit arrives, mark earlier comments stale and rerun analysis for the new SHA. A passing result for an older commit must not satisfy the current merge gate.
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Costs and operating trade-offs
| Cost area | What drives it | Control |
|---|---|---|
| Scanner compute | Repository size, language count, whole-repository versus incremental analysis | Run affected-project checks on PRs, schedule full scans, and cache by commit and tool version. |
| Model credits | Diff size, retrieved context, reasoning level, and custom instructions | Use bounded context, summarize repeated declarations, and route simple changes to a lighter analysis. |
| Queue latency | Concurrent PRs, model service limits, and serialized security jobs | Parallelize independent scanners and reserve deeper review for changed sensitive paths. |
| Storage | Raw logs, SARIF, prompts, responses, and historical labels | Apply retention, redaction, compression, and access controls. |
| Human triage | False positives, duplicate comments, and uncertain business-logic findings | Cap comments, deduplicate, and measure acceptance before adding blocking policies. |
GitHub’s current planning estimate lists $0.05–$1 in AI credits for a Lite review and $0.25–$5 for a Balanced review, excluding GitHub Actions minutes. The documentation says Balanced routes pull requests to a higher-reasoning model for longer analysis of complex logic, security-sensitive code, and cross-service changes. These are planning estimates, not a universal price: consumption rises with PR size and custom instructions, and a self-built system must budget separately for scanner CPU, model tokens, retrieval, storage, and human review.
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Evaluate whether the system is improving
Build a labeled corpus from historical findings and fresh human adjudication. Rerun it whenever rules, prompts, models, or context retrieval changes. Report metrics by language, repository, detector, severity, and change type so an aggregate score cannot hide a weak path.
- Precision: the share of published findings that reviewers confirm.
- Recall: the share of labeled defects the system finds.
- F1: the balance of precision and recall for a defined corpus.
- False-positive rate: especially important for merge-blocking policies.
- Localization accuracy: whether the cited lines actually demonstrate the problem.
- Latency and cost: time to first review, total queue time, token use, and scanner compute.
- Reviewer acceptance: accepted, dismissed, fixed, deferred, and escalated outcomes.
- Engineering impact: pull-request size, time to remediation, recurring findings converted to rules, and vulnerabilities found after merge.
Track abstentions and missing-context cases as first-class outcomes. A model that declines to guess on an under-specified change can be safer than one that produces a confident but untestable comment.
Choosing an implementation approach
| Approach | Strength | Trade-off to verify |
|---|---|---|
| Managed CodeQL plus Copilot Code Quality | Combines CodeQL quality queries with LLM analysis and Autofix inside a hosted workflow. | Verify repository-data handling, policy controls, supported languages, latency, and current credit use for your plan. |
| Semgrep Code plus Agentic Workflows | Combines deterministic rules, AI-powered detection, and reproducible multi-step workflows. | The workflow documentation currently labels this feature beta; confirm availability and governance before making it a required gate. |
| OWASP AGHAST | Vendor-neutral, open-source framework with CI integration, pluggable analyzers, and JSON or SARIF output. | Plan the model gateway, context retrieval, policy engine, and operational ownership yourself. |
| Self-built orchestrator | Maximum control over data residency, prompts, detector versions, and merge policy. | You must maintain adapters, sandboxing, calibration data, dashboards, and on-call support. |
Compare candidates on deterministic rule coverage, business-logic detection, language and framework support, SARIF quality, self-hosting and data residency, policy controls, secret handling, latency, cost, explainability, false-positive behavior, and integration with CI and CODEOWNERS. Treat advertised language lists, beta features, and prices as time-sensitive and verify them in the provider’s current documentation before adoption.
Common failure modes and fixes
Blocking on every model comment
Symptom: developers bypass or disable the reviewer after several speculative failures. Fix: begin advisory, require evidence and localization, cap comments, and promote only calibrated detectors.
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Symptom: high token cost, slow reviews, and leakage of unrelated source or secrets. Fix: use diff-first retrieval with explicit call-site, configuration, and test expansion.
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Trusting a green scanner
Symptom: teams treat “no findings” as a security certification. Fix: retain human approval for high-impact changes, test business invariants, and monitor escaped defects.
Ignoring changed policy files
Symptom: a PR weakens prompts, rules, ownership, or branch protections while its code appears clean. Fix: protect instruction and policy files with CODEOWNERS, explicit approval, and CI validation.
Publishing stale results
Symptom: a merge gate passes based on a superseded commit. Fix: bind every artifact and status to the exact head SHA and invalidate earlier results when the PR changes.
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Symptom: a good overall score hides poor authorization coverage or excessive noise in one language. Fix: slice precision, recall, F1, localization, cost, and acceptance by repository, language, severity, and change type.
A rollout checklist
- Define which deterministic findings can block immediately and document their severity criteria.
- Pin scanner, rule-pack, prompt, model, and context-retrieval versions.
- Store immutable raw output and normalized records keyed to the commit SHA.
- Validate every model response against a schema requiring evidence, location, confidence, assumptions, and remediation.
- Redact secrets and sandbox tools before enabling untrusted PRs.
- Protect instruction, policy, ownership, and branch-control files with explicit review.
- Launch in non-blocking mode, collect reviewer labels, and establish a baseline corpus.
- Promote only checks that meet documented false-positive, localization, agreement, and remediation thresholds.
- Review costs, latency, token usage, and escaped defects each quarter or after a major model or rule change.
Frequently Asked Questions
Can an LLM replace CodeQL or Semgrep?
No. Deterministic analyzers provide reproducible rules, data-flow evidence, dependency checks, and merge-gate behavior. An LLM adds intent and business-logic reasoning, but its output is probabilistic and can be noisy or poorly localized.
What should block a merge?
Use validated deterministic policies first, such as confirmed secrets, prohibited dependencies, or tested architecture invariants. Add an LLM-based block only when a detector has measured false positives and localization accuracy and the finding is corroborated or explicitly approved by a responsible human.
How do I reduce false positives without losing business-logic coverage?
Keep deterministic checks focused on patterns they can prove, give the model bounded but sufficient context, require quoted evidence and falsifiable remediation, deduplicate comments, cap advisory output, and convert repeatedly confirmed findings into deterministic rules or tests.
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How should untrusted pull requests be handled?
Treat code, comments, instruction files, and configuration in an untrusted PR as hostile input. Use a sandbox, least-privilege credentials, restricted network and tool access, redaction, explicit approval for policy changes, and a separate trust boundary for comment publishing.
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