Version	Release Date
2025.6.0	June 20, 2025

System Requirements Update​

Ensure your infrastructure meets the latest requirements for optimal performance and security:

Component	Minimum Version	Recommended Version
KOTS	1.117.3	Latest
Kubernetes	1.28 ⚠️	1.32
PostgreSQL	15	16
Redis	6	7
ggscout	0.16.6	Latest

Helm & Upgrade Considerations​

To ensure compatibility, please review Helm values updates from the previous version. Air gap deployment? Find all the images and tag names in the air gap install page.

Securely Access Secret Values via API with GitGuardian's New “Secrets” Endpoint​

GitGuardian is excited to announce a new API endpoint /v1/secrets/{secret_id}, allowing users to securely access secret values directly through our API.

This feature introduces several key benefits:

1. Enhanced Security Automation - Integrate secret remediation into existing security workflows and tools with secure API access to secret values.
2. Reduced Manual Intervention - Eliminate the need to manually copy secrets from the UI, saving time and reducing human error.
3. Comprehensive Security Controls - Multiple security layers (PAT permissions, workspace settings) ensure secrets are accessed only by authorized users.
4. Complete Secret Context - Receive both the secret value and detector information in a single API call for efficient remediation.

Read more in the documentation

Secrets Detection in Microsoft Teams​

We’re pleased to introduce hardcoded secret detection for Microsoft Teams!

What’s new?

Our platform now scans Microsoft Teams messages for hardcoded secrets—such as API keys, credentials, and tokens—across both new activity and historical content. This means you can instantly identify and remediate exposed secrets, whether they were just shared or left unnoticed in your Teams environment.

Why is this important?

Once a secret is leaked, it remains a security risk until addressed—regardless of when it was exposed. By providing both real-time and historical scanning, we offer:

• Comprehensive coverage: Instantly detect newly introduced secrets and uncover old leaks hiding in past conversations or shared files.
• Proactive risk management: Take swift action to rotate, revoke, or investigate secrets, minimizing the window of exposure.
• Complete peace of mind: Ensure your Teams environment is continuously monitored and secured against secret sprawl.

Secure your collaboration. Protect your business.

Simply connect your Microsoft Teams instance and let our enhanced detection engine do the rest. Our solution will automatically scan both ongoing and historical Teams content, surfacing any hardcoded secrets for prompt remediation.

Check out our documentation to start protecting your MS Teams communications!

Historical Scanning now available for Jira and Confluence Cloud sources.​

We’re excited to announce a significant enhancement to our secret detection capabilities for Jira and Confluence Cloud: historical scanning is now available!

What's new?

Previously, our integration would surface hardcoded secrets in real-time, alerting you to newly introduced risks as soon as they appeared. With this update, we’re extending our detection to include secrets that were leaked in the past—not just those introduced going forward.

Why does this matter?

Once a secret is leaked, it should always be considered compromised, regardless of when the leak occurred. By surfacing historical secrets, you can now:

• Identify and remediate old, forgotten leaks that may still pose a security risk.
• Reach a comprehensive security posture by ensuring that no secrets—past or present—slip through the cracks.
• Take proactive action to rotate or revoke secrets that may have been exposed long ago.

Check out our documentation to enable the feature now:

• Jira Cloud
• Confluence Cloud
• Scaling

Detect hardcoded secrets in your Container Registries​

We are excited to introduce Secret detection for Container Registries, including:

• Azure Container Registry
• Google Artifact Registry
• JFrog Artifactory
• DockerHub

Secrets often end up in container images due to common mistakes during development and image creation, mainly:

• Hardcoding Secrets in Code: Developers may directly embed sensitive credentials, such as API keys or passwords, into application code, which gets packaged into container images.
• Misconfigured Dockerfiles: Commands like ENV or RUN in Dockerfiles can inadvertently expose sensitive data during the build process.

By identifying and addressing hardcoded credentials early in the development pipeline, this feature significantly minimizes the risk of security breaches, helping you prevent the unintended exposure of sensitive information before it even reaches production.

Check out our Blog Post to learn more and our documentation to enable the feature now:

• Azure Container Registry
• Docker Hub
• Google Artifact Registry
• JFrog Artifactory
• Scaling

Export self-hosted GitGuardian logs to Splunk and more​

You can now forward GitGuardian application logs to external log aggregation systems including Splunk, Loki, Elasticsearch, Kafka, and Datadog.

This allows you to integrate GitGuardian's logs with your existing observability infrastructure for centralized monitoring and analysis.

Check out our documentation to configure custom pipelines and start exporting your logs!

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Secrets Detection Engine (v2.140)​

New Detectors

• GitLab Feature Flags Client Token – Detects tokens used for managing feature flags in GitLab projects, crucial for controlling feature rollouts and ensuring smooth deployment processes.
• Laravel Encryption Key with Host – Detects Laravel encryption keys associated with a host.
• GitLab Feature Flags Client Token with Project ID – Identifies tokens and project IDs for GitLab feature management.
• Kubernetes JWT with Host – Detects JWTs used in Kubernetes environments.
• GitLab Trigger Token – Recognizes tokens for triggering GitLab pipelines.
• Brave Search API Key – Detects API keys for accessing Brave Search.
• GitLab Deploy Token – Identifies deploy tokens for managing GitLab project deployments.
• Firecrawl API Key – Detects API keys for Firecrawl services.
• Dify API Key – Detects API keys for Dify services.
• GitLab Runner Authentication Token – Captures authentication tokens for GitLab runners.
• Ubidots API Key – Detects API keys for Ubidots.
• Vapi API Key – Detects API keys for Vapi services.
• Llama Cloud API Key – Ensures detection of API keys for Llama Cloud.

New Checkers These checkers are implemented to verify the detected secrets, adding another layer of security and ensuring their validity and correct application:

• Laravel Encryption Key with Host
• GitLab Feature Flags Client Token with Project ID
• Kubernetes JWT with Host
• Brave Search API Key
• Firecrawl API Key
• Dify API Key
• GitLab Runner Authentication Token

Detector Improvements

• Ubidots Token – Now includes new secret prefixes and improved checker responses for tokens from disabled accounts.
• Azure Cosmos DB Credentials – Enhanced host pattern to improve recall and detection accuracy.
• GitLab Token – Refined pattern to minimize false positives.
• ODBC Connection String – Advanced detection precision for ODBC strings.
• AMQP Credentials – Detector Upgrade: Enhanced multimatch selection to reduce false positive combinations, vital for secure message queuing in distributed systems.
• Confluent Keys – Detector Upgrade: Improved multimatch selection for better accuracy and fewer false positives, essential for managing access to Kafka clusters.
• Generic High Entropy Secret – Detector Upgrade: Excludes secrets ending with '.certificate' from being reported, reducing noise by ignoring non-sensitive certificates.
• Artifactory Token – Analyzer Upgrade: Improved stability by preventing crashes when analyzing secrets with multiple scopes, key for managing and securing software artifacts.
• Microsoft Azure Storage Connection String – Checker Upgrade: Enhanced to accept additional fields, crucial for accessing and managing Azure storage resources securely.
• Microsoft Azure Storage Account Key – Detector Upgrade: Increased precision, reducing false positives, critical for safeguarding data in cloud storage.

Engine Enhancements

• Established a priority rule favoring the confluent_api_keys detector over amqp_assignment and amqp_assignment_attached_port detectors.
• Expanded detection pattern list for encrypted strings to increase precision.
• Enhanced AssignmentRegexMatcher for N prefixed strings in SQL, supporting Microsoft SQL Server.

Enhancements​

• Teams: Optimized the /teams API endpoint to reduce loading times for workspaces with large team structures.
• Self-Hosted:
  • Improved ML Secret Engine Docker image permissions to support running with custom user and group IDs for better Kubernetes security contexts.
  • Improved Docker image permissions to support running with custom user and group IDs for better Kubernetes security contexts.
  • Improved handling of failed index creation migrations to allow safe re-execution of database updates.
  • Added capability to specify constraint of only one worker per node in Kubernetes deployments to optimize resource allocation. Learn more about scaling.

Fixes​

• Emails: Resolved an issue where email alerts were being sent to inactive workspace members.
• Custom Tags: Resolved pagination issues in the custom_tags endpoint that were causing incorrect next page URLs.
• GitLab: Improve permission checking for GitLab group integrations to properly handle inherited permissions from parent groups.
• Severity rules: Corrected an issue preventing Self-Hosted customers from adding or editing custom severity rule sets.
• Secret analyzer: Improved behavior to ensure secret analyzer is properly disabled when validity checking is turned off.
• Self-Hosted Deployment on GCP and Azure: Fixed an issue with ACL limitations on GCP and Azure cloud platforms where Redis deployments disable the ACL command, causing pre-deployment checks for the FLUSHDB command to fail. The system now gracefully handles scenarios where ACL commands are unavailable.