cartography-cncf/cartography
Mar 2026 – Jun 2026
2 Months active
Languages Used
Python
Technical Skills
API DevelopmentError HandlingUnit TestingAWSCloudFormationData Modeling
Yashasvi Yadav
PROFILE
Yashasvi Yadav
Worked on the cartography-cncf/cartography repository to enhance reliability and security visibility in cloud infrastructure data pipelines. Developed HTTP retry logic with exponential backoff for Tailscale API sessions, using Python and urllib3 to address transient errors and reduce manual intervention during data sync. Later, delivered CloudFormation Stack ingestion to detect privilege escalation paths in infrastructure-as-code deployments, modeling new nodes and relationships in Neo4j and validating ingestion with real AWS syncs. Emphasized robust error handling, data modeling, and comprehensive unit and integration testing throughout, ensuring stable workflows and improved detection of privilege escalation vectors without introducing breaking changes to existing pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
467
Activity Months2
Your Network
26 peopleShared Repositories
26
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for cartography: Delivered CloudFormation Stack ingestion to detect privilege escalation paths tied to infrastructure-as-code, expanding coverage beyond standard IAM resources. Implemented a new CloudFormationStack node with essential attributes (id, arn, stack_name, stack_status, role_arn, region) and relationships, including ACCOUNT-RESOURCE and HAS_EXECUTION_ROLE to AWSRole. Added CAN_EXEC mapping for cloudformation:UpdateStack to surface relevant privilege-escalation vectors. Created and updated integration tests (including edge-case where RoleARN is absent) and updated schema docs. Validated ingestion via real AWS sync in us-east-1 and confirmed results with Neo4j queries. The changes elevate security visibility and enable proactive detection of IaC-driven privilege escalation, while maintaining data quality and test coverage.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for cartography: Delivered CloudFormation Stack ingestion to detect privilege escalation paths tied to infrastructure-as-code, expanding coverage beyond standard IAM resources. Implemented a new CloudFormationStack node with essential attributes (id, arn, stack_name, stack_status, role_arn, region) and relationships, including ACCOUNT-RESOURCE and HAS_EXECUTION_ROLE to AWSRole. Added CAN_EXEC mapping for cloudformation:UpdateStack to surface relevant privilege-escalation vectors. Created and updated integration tests (including edge-case where RoleARN is absent) and updated schema docs. Validated ingestion via real AWS sync in us-east-1 and confirmed results with Neo4j queries. The changes elevate security visibility and enable proactive detection of IaC-driven privilege escalation, while maintaining data quality and test coverage.
June 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for cartography repository: Implemented HTTP retry logic with exponential backoff for Tailscale API sessions to handle transient errors, improving reliability of API session creation. Fixed transient error handling that previously caused sync failures on 429/5xx responses. Strengthened the tailscale data sync pipeline with resilient retry patterns, consistent with other modules. Added tests and lint checks to validate behavior; no breaking changes to existing workflows. Result: more stable graph data, reduced manual intervention, and improved operational reliability.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for cartography repository: Implemented HTTP retry logic with exponential backoff for Tailscale API sessions to handle transient errors, improving reliability of API session creation. Fixed transient error handling that previously caused sync failures on 429/5xx responses. Strengthened the tailscale data sync pipeline with resilient retry patterns, consistent with other modules. Added tests and lint checks to validate behavior; no breaking changes to existing workflows. Result: more stable graph data, reduced manual intervention, and improved operational reliability.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentAWSCloudFormationData ModelingError HandlingIntegration TestingNeo4jUnit Testing
Repositories Contributed To
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