Yeti bridges the gap between CTI and DFIR practitioners by providing a Forensics Intelligence…

Why it is Yellow

• CVSS 7.6 high

References

• https://github.com/yeti-platform/yeti/commit/89d8b61cf97b0fb2d46c2f9ee8b7411444345fc6
• https://github.com/yeti-platform/yeti/pull/1277
• https://github.com/yeti-platform/yeti/releases/tag/2.5.1
• https://github.com/yeti-platform/yeti/security/advisories/GHSA-vw33-fwfx-6m9p

Original description

Yeti bridges the gap between CTI and DFIR practitioners by providing a Forensics Intelligence platform and pipeline. In version 2.5.0 and earlier, YETI directly interpolates user-controlled sorting fields and filter_aliases attribute paths into ArangoDB AQL identifier and expression positions in core/database_arango.py. A low-privileged authenticated user can send crafted values to POST /api/v2/observables/search, POST /api/v2/entities/search, POST /api/v2/indicators/search, POST /api/v2/dfiq/search, POST /api/v2/search/, or POST /api/v2/graph/search, causing attacker-controlled AQL subexpressions to be parsed and evaluated. This can provide an error oracle, evaluate expressions against internal collections such as users, alter intended search logic, enumerate schema information, and consume excessive database resources. The verified injection points are read and search contexts, so arbitrary database writes are not established. No fixed version is available as of this review.