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StatusAuthorsCoachDRIsOwning StageCreated
ongoing @theoretick @DylanGriffith @connorgilbert @amarpatel devops secure 2022-11-25

Secret Detection as a platform-wide experience


Today’s secret detection feature is built around containerized scans of repositories within a pipeline context. This feature is quite limited compared to where leaks or compromised tokens may appear and should be expanded to include a much wider scope.

Secret detection as a platform-wide experience encompasses detection across platform features with high risk of secret leakage, including repository contents, job logs, and project management features such as issues, epics, and MRs.



  • Support asynchronous secret detection for the following scan targets:
    • push events
    • issuable creation
    • issuable updates
    • issuable comments


The current proposal is limited to asynchronous detection and alerting only.

Blocking secrets on push events is high-risk to a critical path and would require extensive performance profiling before implementing. See a recent example of a customer incident where this was attempted.

Secret revocation and rotation is also beyond the scope of this new capability.

Scanned object types beyond the scope of this MVC include:

  • Media types (JPEGs, PDFs,…)
  • Snippets
  • Wikis

Management UI

Development of an independent interface for managing secrets is out of scope for this blueprint. Any detections will be managed using the existing Vulnerability Management UI.

Management of detected secrets will remain distinct from the Secret Management feature capability as “detected” secrets are categorically distinct from actively “managed” secrets. When a detected secret is identified, it has already been compromised due to their presence in the target object (that is a repository). Alternatively, managed secrets should be stored with stricter standards for secure storage, including encryption and masking when visible (such as job logs or in the UI).

As a long-term priority we should consider unifying the management of the two secret types however that work is out of scope for the current blueprints goals, which remain focused on active detection.


To achieve scalable secret detection for a variety of domain objects a dedicated scanning service must be created and deployed alongside the GitLab distribution. This is referred to as the SecretScanningService.

This service must be:

  • highly performant
  • horizontally scalable
  • generic in domain object scanning capability

Platform-wide secret detection should be enabled by-default on GitLab SaaS as well as self-managed instances.


  • Secure authentication to infrastructure
  • Performance of scanning against large blobs
  • Performance of scanning against volume of domain objects (such as push frequency)
  • Queueing of scan requests

Design and implementation details

The critical paths as outlined under goals above cover two major object types: Git blobs (corresponding to push events) and arbitrary text blobs.

The detection flow for push events relies on subscribing to the PostReceive hook to enqueue Sidekiq requests to the SecretScanningService. The SecretScanningService service fetches enqueued refs, queries Gitaly for the ref blob contents, scans the commit contents, and notifies the Rails application when a secret is detected. See Push event detection flow for sequence.

The detection flow for arbitrary text blobs, such as issue comments, relies on subscribing to Notes::PostProcessService (or equivalent service) to enqueue Sidekiq requests to the SecretScanningService to process the text blob by object type and primary key of domain object. The SecretScanningService service fetches the relevant text blob, scans the contents, and notifies the Rails application when a secret is detected.

The detection flow for job logs requires processing the log during archive to object storage. See discussion in this issue around scanning during streaming and the added complexity in buffering lookbacks for arbitrary trace chunks.

In any case of detection, the Rails application manually creates a vulnerability using the Vulnerabilities::ManuallyCreateService to surface the finding in the existing Vulnerability Management UI.

See technical discovery for further background exploration.

Token types

The existing Secret Detection configuration covers ~100 rules across a variety of platforms. To reduce total cost of execution and likelihood of false positives the dedicated service targets only well-defined tokens. A well-defined token is defined as a token with a precise definition, most often a fixed substring prefix or suffix and fixed length.

Token types to identify in order of importance:

  1. Well-defined GitLab tokens (including Personal Access Tokens and Pipeline Trigger Tokens)
  2. Verified Partner tokens (including AWS)
  3. Remainder tokens currently included in Secret Detection CI configuration

Detection engine

Our current secret detection offering utilizes Gitleaks for all secret scanning in pipeline contexts. By using its --no-git configuration we can scan arbitrary text blobs outside of a repository context and continue to utilize it for non-pipeline scanning.

Given our existing familiarity with the tool and its extensibility, it should remain our engine of choice. Changes to the detection engine are out of scope unless benchmarking unveils performance concerns.

Notable alternatives include high-performance regex engines such as hyperscan or it’s portable fork vectorscan.

High-level architecture

The implementation of the secret scanning service is highly dependent on the outcomes of our benchmarking and capacity planning against both and our Reference Architectures. As the scanning capability must be an on-by-default component of both our SaaS and self-managed instances the PoC, the deployment characteristics must be considered to determine whether this is a standalone component or executed as a subprocess of the existing Sidekiq worker fleet (similar to the implementation of our Elasticsearch indexing service).

Similarly, the scan target volume will require a robust and scalable enqueueing system to limit resource consumption.

See this thread for past discussion around scaling approaches.

Push event detection flow

sequenceDiagram autonumber actor User User->>+Workhorse: git push Workhorse->>+Gitaly: tcp Gitaly->>+Rails: grpc Sidekiq->>+Rails: poll job Rails->>-Sidekiq: PostReceive worker Sidekiq-->>+Sidekiq: enqueue PostReceiveSecretScanWorker Sidekiq->>+Rails: poll job loop PostReceiveSecretScanWorker Rails->>-Sidekiq: PostReceiveSecretScanWorker Sidekiq->>+SecretScanningSvc: ScanBlob(ref) SecretScanningSvc->>+Sidekiq: accepted Note right of SecretScanningSvc: Scanning job enqueued Sidekiq-->>+Rails: done SecretScanningSvc->>+Gitaly: retrieve blob SecretScanningSvc->>+SecretScanningSvc: scan blob SecretScanningSvc->>+Rails: secret found end


  • ✓ Define requirements for detection coverage and actions
  • ✓ Implement Clientside detection of GitLab tokens in comments/issues
  • PoC of secret scanning service
    • Benchmarking of issuables, comments, job logs and blobs to gain confidence that the total costs will be viable
    • Capacity planning for addition of service component to Reference Architectures headroom
    • Service capabilities
      • gRPC commit retrieval from Gitaly
      • blob scanning
  • Implementation of secret scanning service MVC (targeting individual commits)
  • Security and readiness review
  • Deployment and monitoring
  • Implementation of secret scanning service MVC (targeting arbitrary text blobs)
  • Deployment and monitoring
  • High priority domain object rollout (priority TBD)
    • Issuable comments
    • Issuable bodies
    • Job logs